GEOG489_NestLossProject_AllCode_v2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "4896d008-c0c6-4231-a983-869ae2a5ed5d",
   "metadata": {},
   "source": [
    "# Determining Leatherback Sea Turtle (*Dermochelys coriacea*) Nest Loss on Matura Beach (Trinidad, W.I.)  \n",
    "##### *Adam Eckert (Leader, nest loss), David Roegner (Tidelines), Montana Hernandez (Nest loss), Valerie Nelson (Nest loss)*\n",
    "  \n",
    "    \n",
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2a9f52c-7710-4c7c-8c31-193913449688",
   "metadata": {},
   "source": [
    "### Project Objectives\n",
    "##### 1. Determine whether Python could be used to calculate the number of leatherback sea turtle nests lost to tide-caused drowing each year\n",
    "##### 2. Determine whether a trend in the location of the tide lines could be detected, given the data at hand  \n",
    "  \n",
    "  \n",
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41a8875c-1682-41a2-b45e-176bf0b5266f",
   "metadata": {},
   "source": [
    "### Project Necessity\n",
    "* The leatherback is a IUCN Redlisted species, with populations declining around the world. Trinidad is home to one of the largest leatherback nesting colonies in the world.\n",
    "* As they cannot nest on beaches with offshore protection (e.g., coral reefs, rocky bottoms), leatherback beaches tend to move considerably throughout the season.\n",
    "* For this reason, leatherbacks frequently select very different locations to nest each time they come ashore to lay their eggs.\n",
    "* The combination of variable placement and high oceanic activity means that some nesting beaches may experience higher nest loss, due to drowing, than others. \n",
    "* The ability to more accurately calculate the number of nests lost to drowning will allow us to better manage the nesting beaches, possibly allowing us to predict and prevent some nest loss.\n",
    "* This is a project that the Wider Caribbean Sea Turtle Conservation Network (WIDECAST) has been working on for many years. Until now, the only tools tried have been graphical and interactive software based (ArcGIS, QGIS, MapInfo). Python, and DIY code, is the next step in this analysis journey. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6d363a7-1f02-4ac4-985f-1248d9749199",
   "metadata": {},
   "source": [
    "### Data Source\n",
    "* All data came from WIDECAST\n",
    "* The data were collected by Garmin eTrex GPS units, and include nest locations (waypoints) and tide line locations (tracklines)\n",
    "* The data were converted from their original form of Garmin Mapsource (GDB) files to ESRI Shapefiles before analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa61c607-0f28-4ff2-a258-4f5ed1e9fe5f",
   "metadata": {},
   "source": [
    "# Package Import and Definition of functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "0b444fba-dc01-4e5b-839f-127de539caeb",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import geopandas as gpd\n",
    "from scipy import stats\n",
    "from shapely.geometry import Point, LineString\n",
    "import os\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import ipywidgets as widgets\n",
    "import io\n",
    "import datetime as dt\n",
    "import glob\n",
    "from tqdm import tqdm\n",
    "import warnings\n",
    "import fnmatch\n",
    "from IPython.display import Image"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "904244df-e3cb-4bbb-bfd9-d27304cd5779",
   "metadata": {},
   "source": [
    "cleanPoints removes outliers by calculating the point's distance from the mean of the rest of the points in the file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "0aa5019b-e001-4a45-bdb3-5fd4656f8cc7",
   "metadata": {},
   "outputs": [],
   "source": [
    "def cleanPoints(wpt):\n",
    "    wpt['lat'] = wpt['geometry'].x\n",
    "    wpt['lon'] = wpt['geometry'].y\n",
    "    wpt = wpt[(np.abs(stats.zscore(wpt['lon'])) < 3)]\n",
    "    return wpt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1449426b-3244-4e23-adfb-448c75ee2c78",
   "metadata": {},
   "source": [
    "cleanLine receives a trackline (tideline shapefile) and cleans it. It uses the same technique as cleanPoints"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "aab26cfb-5e3e-404c-a792-0a9ecd7a6240",
   "metadata": {},
   "outputs": [],
   "source": [
    "def cleanLine(trk):\n",
    "    trk=trk.reset_index(drop=True)\n",
    "    trk1=trk.geometry[0]\n",
    "    # Turn line into points and add to a list for outlier removal\n",
    "    coords=list(trk1.coords)\n",
    "    lon=[]\n",
    "    lat=[]\n",
    "    i=0\n",
    "    \n",
    "    # Add the lat and long of each coordinate to their respective lists and create a dataframe with those lists\n",
    "    while i<len(coords):\n",
    "        lon.append(coords[i][0])\n",
    "        lat.append(coords[i][1])\n",
    "        i+=1\n",
    "    df=pd.DataFrame({'lat':lat,'lon':lon})       \n",
    "    df['Coordinates']=list(zip(df.lon,df.lat))\n",
    "    df['Coordinates']=df['Coordinates'].apply(Point)\n",
    "    # Create new geodataframe with the points and clean them based on distance from mean\n",
    "    gdf=gpd.GeoDataFrame(df,geometry='Coordinates')\n",
    "    gdf2 = gdf[(np.abs(stats.zscore(gdf['lon'])) < 2)]\n",
    "    gdf2= gdf2[(np.abs(stats.zscore(gdf['lat'])) < 2.5)]\n",
    "    gdf2 = gdf2.reset_index(drop=True)\n",
    "    # Turn points into a new line\n",
    "    line=LineString(gdf2['Coordinates'])\n",
    "    lines=[line]\n",
    "    df2=pd.DataFrame({'Day':trk['name'][0],'geometry':lines})\n",
    "    tideLine=gpd.GeoDataFrame(df2,geometry='geometry',crs=4326)\n",
    "    return tideLine"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be648627-75d7-4187-9fe0-c28fc633496e",
   "metadata": {},
   "source": [
    "cleanLineG is similar to cleanLine, but the output has been tweaked slightly to better use for trendline analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "aa3f9499-c8e7-4bb9-9ea4-4a5304cd48db",
   "metadata": {},
   "outputs": [],
   "source": [
    "def cleanLineG(trk1,name):\n",
    "    coords=list(trk1.coords)\n",
    "    lon=[]\n",
    "    lat=[]\n",
    "    i=0\n",
    "    while i<len(coords):\n",
    "        lon.append(coords[i][0])\n",
    "        lat.append(coords[i][1])\n",
    "        i+=1\n",
    "    df=pd.DataFrame({'lat':lat,'lon':lon})\n",
    "    df['Coordinates']=list(zip(df.lon,df.lat))\n",
    "    df['Coordinates']=df['Coordinates'].apply(Point)\n",
    "    gdf=gpd.GeoDataFrame(df,geometry='Coordinates')\n",
    "    \n",
    "    gdf2 = gdf[(np.abs(stats.zscore(gdf['lon'])) < 2)]\n",
    "    gdf2= gdf2[(np.abs(stats.zscore(gdf['lat'])) < 2.5)]\n",
    "    gdf2 = gdf2.reset_index(drop=True)\n",
    "    line=LineString(gdf2['Coordinates'])\n",
    "    lines=[line]\n",
    "    df2=pd.DataFrame({'name':name,'geometry':lines})\n",
    "    tideLine=gpd.GeoDataFrame(df2,geometry='geometry',crs=4326)\n",
    "    return tideLine"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1a0fb87-7f32-48bc-9c9a-84e65c929cbd",
   "metadata": {},
   "source": [
    "distance2 calculates the distance between an initial point and a new point for use in trendline analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "69107ce8-a224-41b0-bdb2-23ae63623761",
   "metadata": {},
   "outputs": [],
   "source": [
    "def distance2(initi,point):\n",
    "    return -1*(point.x-initi.x) # Multiplying by -1 make a loss show as positive (i.e., tideline moving up beach means ocean is rising and beach is shrinking)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "574d122f-7a10-4903-bc17-c624d7a0d7d4",
   "metadata": {},
   "source": [
    "# Determine the trend in high tideline movement, 2011-2019"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a081382-3673-4443-ba1a-8b7670cf5cf4",
   "metadata": {},
   "source": [
    "To calculate the changes in the tidelines, we first must load all the tideline shapefiles and clean them. Once that's done, the date of each tideline is calculated, and the tidelines are grouped by date. The tidelines for each month are then averaged, and those averages are compared to the average of the first month. Finally, all these changes are plotted to see if there is a trend in their movement."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39a92b23-83b1-4c7e-900b-aa9350a83a25",
   "metadata": {
    "tags": []
   },
   "source": [
    "### 1. Import tideline shapfiles and clean them"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c32cf0d0-a9b3-4642-902d-6dbd294c3778",
   "metadata": {},
   "source": [
    "1a. Create a list of all the shapefiles for the tracks after 2010"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "f27231d3-41ee-4885-81f8-ca25e02009c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# List all the directory names (years) in the Tracks directory and create a new array for the files\n",
    "years = os.listdir('./Tracks/')\n",
    "indFiles=[]\n",
    "\n",
    "# Loop through each directory and select only the shapefiles (.shp) to add to the list. Some data cleaning is also done\n",
    "for f in years:\n",
    "    temp=os.listdir('Tracks/'+str(f)+'/')\n",
    "    temp=['./Tracks/'+str(f)+'/'+ls for ls in temp]\n",
    "    temp = [x for x in temp if '.shp' in str(x)]\n",
    "    temp = [y for y in temp if '.shp.' not in str(y)]\n",
    "    temp = [z for z in temp if 'y10' not in str(z)]\n",
    "    temp = [a for a in temp if 'e10' not in str(a)]\n",
    "    temp = [b for b in temp if 't10' not in str(b)]\n",
    "    indFiles=indFiles+temp"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bbf18a2f-5c34-4963-aa0d-f3ed8d039cd2",
   "metadata": {},
   "source": [
    "1b. Create a new GeoDataFrame with all the tracks in the data sorted by date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "f132faf7-e832-4f6c-8ec8-659f7dfb1edf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Suppress warnings about code that will be depreciating\n",
    "warnings.filterwarnings(\"ignore\", category=FutureWarning)\n",
    "\n",
    "# Create new GeoDataFrame\n",
    "allLines= gpd.GeoDataFrame(columns=['name', 'geometry'], geometry='geometry')\n",
    "\n",
    "# Loop through every file, open them, and add their lines to the new GeoDataFrame\n",
    "for i in indFiles:\n",
    "    try:\n",
    "        trks = gpd.read_file(i)\n",
    "        if len(trks)>0:\n",
    "            allLines=allLines.append(trks)\n",
    "    except ValueError:\n",
    "        continue\n",
    "\n",
    "# Select only the tracks that have a date as the name and removes some outlier lines\n",
    "allLines= allLines[allLines[\"name\"].str.contains(\"-\")==True]\n",
    "allLines['name'] = pd.to_datetime(allLines['name'])\n",
    "allLines=allLines.sort_values(by='name')\n",
    "allLines=allLines.drop_duplicates('name')\n",
    "allLines=allLines.iloc[1: , :]\n",
    "allLines=allLines.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21d7d017-7843-44e5-afa5-e671f4ede220",
   "metadata": {},
   "source": [
    "1c. Clean the lines and store them all in a DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "6ff7f20b-1b1b-4c8c-a7e0-ee54666a20e6",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 890/890 [00:51<00:00, 17.18it/s]\n"
     ]
    }
   ],
   "source": [
    "# Suppress warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "final=[]\n",
    "j=0\n",
    "\n",
    "# Loop through each line, clean them, and create a list of the cleaned lines\n",
    "for j in tqdm(range(len(allLines))):\n",
    "    trkg=allLines.loc[j]['geometry']\n",
    "    c=cleanLineG(trkg,allLines.loc[j]['name'])\n",
    "    ls=[c]\n",
    "    final= final+ls"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aeec48f8-af0f-487b-be9d-7fcac419f486",
   "metadata": {},
   "source": [
    "### 2. Place shapefiles into their respective month/year DataFrames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "690425ac-88bb-405d-a144-33d227dc62a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Take the list of cleaned lines and adds them to a new list based on the year and month of the line creation\n",
    "jan11=[x for x in final if pd.to_datetime(x['name'].values[0]).year==2011 and pd.to_datetime(x['name'].values[0]).month==1]\n",
    "feb11=[x for x in final if pd.to_datetime(x['name'].values[0]).year==2011 and pd.to_datetime(x['name'].values[0]).month==2]\n",
    "mar11=[x for x in final if pd.to_datetime(x['name'].values[0]).year==2011 and pd.to_datetime(x['name'].values[0]).month==3]\n",
    "apr11=[x for x in final if pd.to_datetime(x['name'].values[0]).year==2011 and pd.to_datetime(x['name'].values[0]).month==4]\n",
    "may11=[x for x in final if pd.to_datetime(x['name'].values[0]).year==2011 and pd.to_datetime(x['name'].values[0]).month==5]\n",
    "jun11=[x for x in final if pd.to_datetime(x['name'].values[0]).year==2011 and pd.to_datetime(x['name'].values[0]).month==6]\n",
    "jul11=[x for x in final if pd.to_datetime(x['name'].values[0]).year==2011 and pd.to_datetime(x['name'].values[0]).month==7]\n",
    "aug11=[x for x in final if pd.to_datetime(x['name'].values[0]).year==2011 and pd.to_datetime(x['name'].values[0]).month==8]\n",
    "sep11=[x for x in final if pd.to_datetime(x['name'].values[0]).year==2011 and pd.to_datetime(x['name'].values[0]).month==9]\n",
    "oct11=[x for x in final if pd.to_datetime(x['name'].values[0]).year==2011 and pd.to_datetime(x['name'].values[0]).month==10]\n",
    "nov11=[x for x in final if pd.to_datetime(x['name'].values[0]).year==2011 and pd.to_datetime(x['name'].values[0]).month==11]\n",
    "dec11=[x for x in final if pd.to_datetime(x['name'].values[0]).year==2011 and pd.to_datetime(x['name'].values[0]).month==12]\n",
    "\n",
    "jan12=[x for x in final if pd.to_datetime(x['name'].values[0]).year==2012 and pd.to_datetime(x['name'].values[0]).month==1]\n",
    "feb12=[x for x in final if pd.to_datetime(x['name'].values[0]).year==2012 and pd.to_datetime(x['name'].values[0]).month==2]\n",
    "mar12=[x for x in final if pd.to_datetime(x['name'].values[0]).year==2012 and pd.to_datetime(x['name'].values[0]).month==3]\n",
    "apr12=[x for x in final if pd.to_datetime(x['name'].values[0]).year==2012 and pd.to_datetime(x['name'].values[0]).month==4]\n",
    "may12=[x for x in final if pd.to_datetime(x['name'].values[0]).year==2011 and pd.to_datetime(x['name'].values[0]).month==5]\n",
    "jun12=[x for x in final if pd.to_datetime(x['name'].values[0]).year==2012 and pd.to_datetime(x['name'].values[0]).month==6]\n",
    "jul12=[x for x in final if pd.to_datetime(x['name'].values[0]).year==2012 and pd.to_datetime(x['name'].values[0]).month==7]\n",
    "aug12=[x for x in final if pd.to_datetime(x['name'].values[0]).year==2012 and pd.to_datetime(x['name'].values[0]).month==8]\n",
    "sep12=[x for x in final if pd.to_datetime(x['name'].values[0]).year==2012 and pd.to_datetime(x['name'].values[0]).month==9]\n",
    "oct12=[x for x in final if pd.to_datetime(x['name'].values[0]).year==2012 and pd.to_datetime(x['name'].values[0]).month==10]\n",
    "nov12=[x for x in final if pd.to_datetime(x['name'].values[0]).year==2012 and pd.to_datetime(x['name'].values[0]).month==11]\n",
    "dec12=[x for x in final if pd.to_datetime(x['name'].values[0]).year==2012 and pd.to_datetime(x['name'].values[0]).month==12]\n",
    "\n",
    "jan13=[x for x in final if pd.to_datetime(x['name'].values[0]).year==2013 and pd.to_datetime(x['name'].values[0]).month==1]\n",
    "feb13=[x for x in final if pd.to_datetime(x['name'].values[0]).year==2013 and pd.to_datetime(x['name'].values[0]).month==2]\n",
    "mar13=[x for x in final if pd.to_datetime(x['name'].values[0]).year==2013 and pd.to_datetime(x['name'].values[0]).month==3]\n",
    "apr13=[x for x in final if pd.to_datetime(x['name'].values[0]).year==2013 and pd.to_datetime(x['name'].values[0]).month==4]\n",
    "may13=[x for x in final if pd.to_datetime(x['name'].values[0]).year==2013 and pd.to_datetime(x['name'].values[0]).month==5]\n",
    "jun13=[x for x in final if pd.to_datetime(x['name'].values[0]).year==2013 and pd.to_datetime(x['name'].values[0]).month==6]\n",
    "jul13=[x for x in final if pd.to_datetime(x['name'].values[0]).year==2013 and pd.to_datetime(x['name'].values[0]).month==7]\n",
    "aug13=[x for x in final if pd.to_datetime(x['name'].values[0]).year==2013 and pd.to_datetime(x['name'].values[0]).month==8]\n",
    "sep13=[x for x in final if pd.to_datetime(x['name'].values[0]).year==2013 and pd.to_datetime(x['name'].values[0]).month==9]\n",
    "oct13=[x for x in final if pd.to_datetime(x['name'].values[0]).year==2013 and pd.to_datetime(x['name'].values[0]).month==10]\n",
    "nov13=[x for x in final if pd.to_datetime(x['name'].values[0]).year==2013 and pd.to_datetime(x['name'].values[0]).month==11]\n",
    "dec13=[x for x in final if pd.to_datetime(x['name'].values[0]).year==2013 and pd.to_datetime(x['name'].values[0]).month==12]\n",
    "\n",
    "jan14=[x for x in final if pd.to_datetime(x['name'].values[0]).year==2014 and pd.to_datetime(x['name'].values[0]).month==1]\n",
    "feb14=[x for x in final if pd.to_datetime(x['name'].values[0]).year==2014 and pd.to_datetime(x['name'].values[0]).month==2]\n",
    "mar14=[x for x in final if pd.to_datetime(x['name'].values[0]).year==2014 and pd.to_datetime(x['name'].values[0]).month==3]\n",
    "apr14=[x for x in final if pd.to_datetime(x['name'].values[0]).year==2014 and pd.to_datetime(x['name'].values[0]).month==4]\n",
    "may14=[x for x in final if pd.to_datetime(x['name'].values[0]).year==2014 and pd.to_datetime(x['name'].values[0]).month==5]\n",
    "jun14=[x for x in final if pd.to_datetime(x['name'].values[0]).year==2014 and pd.to_datetime(x['name'].values[0]).month==6]\n",
    "jul14=[x for x in final if pd.to_datetime(x['name'].values[0]).year==2014 and pd.to_datetime(x['name'].values[0]).month==7]\n",
    "aug14=[x for x in final if pd.to_datetime(x['name'].values[0]).year==2014 and pd.to_datetime(x['name'].values[0]).month==8]\n",
    "sep14=[x for x in final if pd.to_datetime(x['name'].values[0]).year==2014 and pd.to_datetime(x['name'].values[0]).month==9]\n",
    "oct14=[x for x in final if pd.to_datetime(x['name'].values[0]).year==2014 and pd.to_datetime(x['name'].values[0]).month==10]\n",
    "nov14=[x for x in final if pd.to_datetime(x['name'].values[0]).year==2014 and pd.to_datetime(x['name'].values[0]).month==11]\n",
    "dec14=[x for x in final if pd.to_datetime(x['name'].values[0]).year==2014 and pd.to_datetime(x['name'].values[0]).month==12]\n",
    "\n",
    "jan15=[x for x in final if pd.to_datetime(x['name'].values[0]).year==2015 and pd.to_datetime(x['name'].values[0]).month==1]\n",
    "feb15=[x for x in final if pd.to_datetime(x['name'].values[0]).year==2015 and pd.to_datetime(x['name'].values[0]).month==2]\n",
    "mar15=[x for x in final if pd.to_datetime(x['name'].values[0]).year==2015 and pd.to_datetime(x['name'].values[0]).month==3]\n",
    "apr15=[x for x in final if pd.to_datetime(x['name'].values[0]).year==2015 and pd.to_datetime(x['name'].values[0]).month==4]\n",
    "may15=[x for x in final if pd.to_datetime(x['name'].values[0]).year==2015 and pd.to_datetime(x['name'].values[0]).month==5]\n",
    "jun15=[x for x in final if pd.to_datetime(x['name'].values[0]).year==2015 and pd.to_datetime(x['name'].values[0]).month==6]\n",
    "jul15=[x for x in final if pd.to_datetime(x['name'].values[0]).year==2015 and pd.to_datetime(x['name'].values[0]).month==7]\n",
    "aug15=[x for x in final if pd.to_datetime(x['name'].values[0]).year==2015 and pd.to_datetime(x['name'].values[0]).month==8]\n",
    "sep15=[x for x in final if pd.to_datetime(x['name'].values[0]).year==2015 and pd.to_datetime(x['name'].values[0]).month==9]\n",
    "oct15=[x for x in final if pd.to_datetime(x['name'].values[0]).year==2015 and pd.to_datetime(x['name'].values[0]).month==10]\n",
    "nov15=[x for x in final if pd.to_datetime(x['name'].values[0]).year==2015 and pd.to_datetime(x['name'].values[0]).month==11]\n",
    "dec15=[x for x in final if pd.to_datetime(x['name'].values[0]).year==2015 and pd.to_datetime(x['name'].values[0]).month==12]\n",
    "\n",
    "jan16=[x for x in final if pd.to_datetime(x['name'].values[0]).year==2016 and pd.to_datetime(x['name'].values[0]).month==1]\n",
    "feb16=[x for x in final if pd.to_datetime(x['name'].values[0]).year==2016 and pd.to_datetime(x['name'].values[0]).month==2]\n",
    "mar16=[x for x in final if pd.to_datetime(x['name'].values[0]).year==2016 and pd.to_datetime(x['name'].values[0]).month==3]\n",
    "apr16=[x for x in final if pd.to_datetime(x['name'].values[0]).year==2016 and pd.to_datetime(x['name'].values[0]).month==4]\n",
    "may16=[x for x in final if pd.to_datetime(x['name'].values[0]).year==2016 and pd.to_datetime(x['name'].values[0]).month==5]\n",
    "jun16=[x for x in final if pd.to_datetime(x['name'].values[0]).year==2016 and pd.to_datetime(x['name'].values[0]).month==6]\n",
    "jul16=[x for x in final if pd.to_datetime(x['name'].values[0]).year==2016 and pd.to_datetime(x['name'].values[0]).month==7]\n",
    "aug16=[x for x in final if pd.to_datetime(x['name'].values[0]).year==2016 and pd.to_datetime(x['name'].values[0]).month==8]\n",
    "sep16=[x for x in final if pd.to_datetime(x['name'].values[0]).year==2016 and pd.to_datetime(x['name'].values[0]).month==9]\n",
    "oct16=[x for x in final if pd.to_datetime(x['name'].values[0]).year==2016 and pd.to_datetime(x['name'].values[0]).month==10]\n",
    "nov16=[x for x in final if pd.to_datetime(x['name'].values[0]).year==2016 and pd.to_datetime(x['name'].values[0]).month==11]\n",
    "dec16=[x for x in final if pd.to_datetime(x['name'].values[0]).year==2016 and pd.to_datetime(x['name'].values[0]).month==12]\n",
    "\n",
    "jan17=[x for x in final if pd.to_datetime(x['name'].values[0]).year==2017 and pd.to_datetime(x['name'].values[0]).month==1]\n",
    "feb17=[x for x in final if pd.to_datetime(x['name'].values[0]).year==2017 and pd.to_datetime(x['name'].values[0]).month==2]\n",
    "mar17=[x for x in final if pd.to_datetime(x['name'].values[0]).year==2017 and pd.to_datetime(x['name'].values[0]).month==3]\n",
    "apr17=[x for x in final if pd.to_datetime(x['name'].values[0]).year==2017 and pd.to_datetime(x['name'].values[0]).month==4]\n",
    "may17=[x for x in final if pd.to_datetime(x['name'].values[0]).year==2017 and pd.to_datetime(x['name'].values[0]).month==5]\n",
    "jun17=[x for x in final if pd.to_datetime(x['name'].values[0]).year==2017 and pd.to_datetime(x['name'].values[0]).month==6]\n",
    "jul17=[x for x in final if pd.to_datetime(x['name'].values[0]).year==2017 and pd.to_datetime(x['name'].values[0]).month==7]\n",
    "aug17=[x for x in final if pd.to_datetime(x['name'].values[0]).year==2017 and pd.to_datetime(x['name'].values[0]).month==8]\n",
    "sep17=[x for x in final if pd.to_datetime(x['name'].values[0]).year==2017 and pd.to_datetime(x['name'].values[0]).month==9]\n",
    "oct17=[x for x in final if pd.to_datetime(x['name'].values[0]).year==2017 and pd.to_datetime(x['name'].values[0]).month==10]\n",
    "nov17=[x for x in final if pd.to_datetime(x['name'].values[0]).year==2017 and pd.to_datetime(x['name'].values[0]).month==11]\n",
    "dec17=[x for x in final if pd.to_datetime(x['name'].values[0]).year==2017 and pd.to_datetime(x['name'].values[0]).month==12]\n",
    "\n",
    "jan18=[x for x in final if pd.to_datetime(x['name'].values[0]).year==2018 and pd.to_datetime(x['name'].values[0]).month==1]\n",
    "feb18=[x for x in final if pd.to_datetime(x['name'].values[0]).year==2018 and pd.to_datetime(x['name'].values[0]).month==2]\n",
    "mar18=[x for x in final if pd.to_datetime(x['name'].values[0]).year==2018 and pd.to_datetime(x['name'].values[0]).month==3]\n",
    "apr18=[x for x in final if pd.to_datetime(x['name'].values[0]).year==2018 and pd.to_datetime(x['name'].values[0]).month==4]\n",
    "may18=[x for x in final if pd.to_datetime(x['name'].values[0]).year==2018 and pd.to_datetime(x['name'].values[0]).month==5]\n",
    "jun18=[x for x in final if pd.to_datetime(x['name'].values[0]).year==2018 and pd.to_datetime(x['name'].values[0]).month==6]\n",
    "jul18=[x for x in final if pd.to_datetime(x['name'].values[0]).year==2018 and pd.to_datetime(x['name'].values[0]).month==7]\n",
    "aug18=[x for x in final if pd.to_datetime(x['name'].values[0]).year==2018 and pd.to_datetime(x['name'].values[0]).month==8]\n",
    "sep18=[x for x in final if pd.to_datetime(x['name'].values[0]).year==2018 and pd.to_datetime(x['name'].values[0]).month==9]\n",
    "oct18=[x for x in final if pd.to_datetime(x['name'].values[0]).year==2018 and pd.to_datetime(x['name'].values[0]).month==10]\n",
    "nov18=[x for x in final if pd.to_datetime(x['name'].values[0]).year==2018 and pd.to_datetime(x['name'].values[0]).month==11]\n",
    "dec18=[x for x in final if pd.to_datetime(x['name'].values[0]).year==2018 and pd.to_datetime(x['name'].values[0]).month==12]\n",
    "\n",
    "jan19=[x for x in final if pd.to_datetime(x['name'].values[0]).year==2019 and pd.to_datetime(x['name'].values[0]).month==1]\n",
    "feb19=[x for x in final if pd.to_datetime(x['name'].values[0]).year==2019 and pd.to_datetime(x['name'].values[0]).month==2]\n",
    "mar19=[x for x in final if pd.to_datetime(x['name'].values[0]).year==2019 and pd.to_datetime(x['name'].values[0]).month==3]\n",
    "apr19=[x for x in final if pd.to_datetime(x['name'].values[0]).year==2019 and pd.to_datetime(x['name'].values[0]).month==4]\n",
    "may19=[x for x in final if pd.to_datetime(x['name'].values[0]).year==2019 and pd.to_datetime(x['name'].values[0]).month==5]\n",
    "jun19=[x for x in final if pd.to_datetime(x['name'].values[0]).year==2019 and pd.to_datetime(x['name'].values[0]).month==6]\n",
    "jul19=[x for x in final if pd.to_datetime(x['name'].values[0]).year==2019 and pd.to_datetime(x['name'].values[0]).month==7]\n",
    "aug19=[x for x in final if pd.to_datetime(x['name'].values[0]).year==2019 and pd.to_datetime(x['name'].values[0]).month==8]\n",
    "sep19=[x for x in final if pd.to_datetime(x['name'].values[0]).year==2019 and pd.to_datetime(x['name'].values[0]).month==9]\n",
    "oct19=[x for x in final if pd.to_datetime(x['name'].values[0]).year==2019 and pd.to_datetime(x['name'].values[0]).month==10]\n",
    "nov19=[x for x in final if pd.to_datetime(x['name'].values[0]).year==2019 and pd.to_datetime(x['name'].values[0]).month==11]\n",
    "dec19=[x for x in final if pd.to_datetime(x['name'].values[0]).year==2019 and pd.to_datetime(x['name'].values[0]).month==12]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73e14f06-4cff-43e2-aa2e-89391d538630",
   "metadata": {},
   "source": [
    "### 3. Calculate the changes in tideline locations"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35d19e4f-477f-409e-9eb1-6d7d801a03fb",
   "metadata": {},
   "source": [
    "3a. Create a list of months for which to do trendline analysis. Only months with 5 or more lines are used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "1f7d8276-684c-4d31-a00b-31fbc7c3e4ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Put each list of lines into an array together along with the names of the array\n",
    "alldata=[jan11,feb11,mar11,apr11,may11,jun11,jul11,aug11,sep11,oct11,nov11,dec11,\n",
    "         jan12,feb12,mar12,apr12,may12,jun12,jul12,aug12,sep12,oct12,nov12,dec12,\n",
    "         jan13,feb13,mar13,apr13,may13,jun13,jul13,aug13,sep13,oct13,nov13,dec13,\n",
    "         jan14,feb14,mar14,apr14,may14,jun14,jul14,aug14,sep14,oct14,nov14,dec14,\n",
    "         jan15,feb15,mar15,apr15,may15,jun15,jul15,aug15,sep15,oct15,nov15,dec15,\n",
    "         jan16,feb16,mar16,apr16,may16,jun16,jul16,aug16,sep16,oct16,nov16,dec16,\n",
    "         jan17,feb17,mar17,apr17,may17,jun17,jul17,aug17,sep17,oct17,nov17,dec17,\n",
    "         jan18,feb18,mar18,apr18,may18,jun18,jul18,aug18,sep18,oct18,nov18,dec18,\n",
    "         jan19,feb19,mar19,apr19,may19,jun19,jul19,aug19,sep19,oct19,nov19,dec19]\n",
    "monthY=['jan11','feb11','mar11','apr11','may11','jun11','jul11','aug11','sep11','oct11','nov11','dec11',\n",
    "         'jan12','feb12','mar12','apr12','may12','jun12','jul12','aug12','sep12','oct12','nov12','dec12',\n",
    "         'jan13','feb13','mar13','apr13','may13','jun13','jul13','aug13','sep13','oct13','nov13','dec13',\n",
    "         'jan14','feb14','mar14','apr14','may14','jun14','jul14','aug14','sep14','oct14','nov14','dec14',\n",
    "         'jan15','feb15','mar15','apr15','may15','jun15','jul15','aug15','sep15','oct15','nov15','dec15',\n",
    "         'jan16','feb16','mar16','apr16','may16','jun16','jul16','aug16','sep16','oct16','nov16','dec16',\n",
    "         'jan17','feb17','mar17','apr17','may17','jun17','jul17','aug17','sep17','oct17','nov17','dec17',\n",
    "         'jan18','feb18','mar18','apr18','may18','jun18','jul18','aug18','sep18','oct18','nov18','dec18',\n",
    "         'jan19','feb19','mar19','apr19','may19','jun19','jul19','aug19','sep19','oct19','nov19','dec19']\n",
    "del alldata[50][1]\n",
    "alldata2=[]\n",
    "monthsGood=[]\n",
    "\n",
    "# Add months to a new list if there are 5 or more lines in that month\n",
    "for i in range(len(alldata)):\n",
    "    if len(alldata[i])>4:\n",
    "        alldata2.append(alldata[i])\n",
    "        monthsGood.append(monthY[i])\n",
    "    else:\n",
    "        alldata2.append(0)\n",
    "        monthsGood.append(monthY[i])\n",
    "        "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e40a351-b709-42b2-8ff2-b086f1493d79",
   "metadata": {},
   "source": [
    "3b. Calculate the average latitude for selected months and assigns the initial latitude for montsh with less than 5 lines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "183cf060-395c-48ee-8f43-568b10fa000e",
   "metadata": {},
   "outputs": [],
   "source": [
    "average=[]\n",
    "for ls in alldata2:\n",
    "    if ls !=0:\n",
    "        tomean=[]\n",
    "        for z in ls:\n",
    "            try:\n",
    "                # Find the center of the line\n",
    "                center=z.centroid.values[0]\n",
    "                # Check to make sure a point was actually created and add it to a list\n",
    "                if center != 'EMPTY':\n",
    "                    tomean.append(center.x)\n",
    "            except IndexError:\n",
    "                continue\n",
    "        # Find the average Latitude of the list for the given month\n",
    "        average.append(np.average(tomean))\n",
    "    # If the month has less than 5 lines, the initial point is assigned so there is no change\n",
    "    else:\n",
    "        average.append(-61.034890858899516)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28e56c10-a3d7-472a-9e7d-80e25949ef6d",
   "metadata": {},
   "source": [
    "3c. Create a GeoDataFrame of the points with a projection that has a unit of meters (UTM Zone 20N)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "5bf4b054-54fe-4432-8c5a-5f02cd3ba3e8",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Create a longitude list that is the same length as the average to base calculations off of\n",
    "y1=[]\n",
    "for y in range(len(average)):\n",
    "    y1.append(10.637)\n",
    "\n",
    "# Create a GeoDataFrame of points and reproject it\n",
    "points=gpd.points_from_xy(average, y1, crs=4326)\n",
    "points=points.to_crs(epsg=32620)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce2df732-873c-4a4b-8bf0-b8663a281993",
   "metadata": {},
   "source": [
    "3d. Calculate the change in latitude from the initial month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "daf936ae-4e71-45f3-97ce-3d7b367963ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set the initial point with the first month in the data. Also create an empty array for the distances\n",
    "initial=points[0]\n",
    "distance=[]\n",
    "\n",
    "# Loop through each month's average and calculate the change from the initial point\n",
    "for point in points:\n",
    "    val=distance2(initial,point)\n",
    "    distance.append(val)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ce4723c-5de8-4d6d-8691-3b7665df5f4a",
   "metadata": {},
   "source": [
    "3e. Plot the data for the change in tide over time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "09235151-1a31-438f-a466-765d3c29c68c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(close=None, block=None)>"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax=plt.subplots(figsize=(20,8))\n",
    "ax.plot(monthsGood,distance)\n",
    "plt.xticks(rotation=90)\n",
    "plt.grid()\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('Change from March of 2011')\n",
    "plt.title('High Tide Change Over Time')\n",
    "plt.savefig('TrendLine.png')\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d946a63c-c4fe-4b78-bf9c-4897b6de53da",
   "metadata": {},
   "source": [
    "# Determining The Number of Lost Nests, Per Year, on Matura Beach"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c199f182-0fa1-4cab-becc-ad88eadcf2ce",
   "metadata": {},
   "source": [
    "To find the nests that have drowned, we need to import all the tidelines for one year, buffer those lines to make polygons, then determine how many nests are located within those buffers. Only one year is analyzed at a time to increase processing speed. For each year, the tidelines are imported, cleaned, and projected. Their dates are also determined. The program then iterates through each of the nest files, cleans them, determines their date, and compares the locations of their points to all of the lines that occur within 90 days of the nest file's date. 90 was chosen because that's the average amount of time a leatherback nest incubates for in Trinidad. Once the loops are completed and we have a count of how many times each nest has been drowned, those counts are summed and added to a database for export."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1a90f84-e9f8-4135-b1d5-14e10395ac67",
   "metadata": {},
   "source": [
    "### 1. Ask User for location of data (or define your own pathway), and target Year"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f69df3e7-e031-4548-acf3-3f4c8ef3700f",
   "metadata": {},
   "source": [
    "The pathway should contain all information up to the parent folder that holds all year folders, and should end with a /\n",
    "\n",
    "Within the parent folder, there must be two folders: TRK_SHP holds the tide line shapefiles, WPT_SHP holds the nest shapefiles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "121e0a9f-0b44-4a5e-96c1-4d46fb94c71c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "Which year are you analyzing?  2018\n"
     ]
    }
   ],
   "source": [
    "# Define GitHub Directory for Data\n",
    "# gh_path = 'C:/Users/aecke/OneDrive/ACTIVE/UIS/Spring_2022/GEOG489/GitHub/GEOG489_Nest_Loss/'\n",
    "\n",
    "# Ask User for data pathway\n",
    "# gh_path = input(\"Where is your data stored? \")\n",
    "# year_count = input(\"Which year are you analyzing? \")\n",
    "\n",
    "##### Use this for the Demo #####\n",
    "gh_path = './Data_Loss/'\n",
    "year_count = input(\"Which year are you analyzing? \")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2e6c050-eadc-49cb-9f3e-752c8b15bc11",
   "metadata": {},
   "source": [
    "### 2. Load all the tide lines found in the target year's folder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "2161b9b9-adf1-43fd-aafe-e6f4b626a031",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               name                                           geometry    Date\n",
      "0         01-MAR-18  LINESTRING (-61.03672 10.65176, -61.03661 10.6...  010318\n",
      "1         01-MAR-18  LINESTRING (-61.03832 10.67863, -61.03827 10.6...  010318\n",
      "2         01-JUN-18  LINESTRING (-61.03556 10.65182, -61.03542 10.6...  010618\n",
      "3         01-JUN-18  LINESTRING (-61.03556 10.65182, -61.03542 10.6...  010618\n",
      "4         02-APR-18  LINESTRING (-61.03766 10.68660, -61.03758 10.6...  020418\n",
      "..              ...                                                ...     ...\n",
      "212  ACTIVE LOG 001  LINESTRING (-61.03798 10.67018, -61.03797 10.6...  310318\n",
      "213  ACTIVE LOG 002  LINESTRING (-61.03788 10.66925, -61.03786 10.6...  310318\n",
      "214  ACTIVE LOG 003  LINESTRING (-61.03673 10.65919, -61.03673 10.6...  310318\n",
      "215  ACTIVE LOG 004  LINESTRING (-61.03601 10.65540, -61.03602 10.6...  310318\n",
      "216  ACTIVE LOG 005  LINESTRING (-61.03607 10.65533, -61.03607 10.6...  310318\n",
      "\n",
      "[217 rows x 3 columns]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Suppress warnings about code depreciation\n",
    "warnings.filterwarnings(\"ignore\", category=FutureWarning)\n",
    "\n",
    "# Creates the dataframe that will hold all the year's tidelines\n",
    "trks_any = gpd.GeoDataFrame()\n",
    "\n",
    "for filename_path in glob.iglob(f'{gh_path}{year_count}/TRK_SHP/*.shp'):\n",
    "    # print(filename_path)\n",
    "    # There are some files that throw errors for some reason, so this is a dirty way of ignore those files and continuing with the loop\n",
    "    try:\n",
    "        temp_shp = gpd.read_file(filename_path)\n",
    "    except:\n",
    "        pass\n",
    "    \n",
    "    # Check to make sure there are lines in the shapefile before trying to process them. If there are lines, then the code can continue\n",
    "    if len(temp_shp) > 0:\n",
    "        for i in range(len(temp_shp)):\n",
    "            \n",
    "            # print(temp_shp)\n",
    "            # print(temp_shp.iloc[i])\n",
    "            filename = os.path.basename(filename_path)\n",
    "\n",
    "            filedate = filename[ :6]\n",
    "            # print(filedate)\n",
    "            \n",
    "            temp_shp['Date'] = filedate\n",
    "            # print(temp_shp)\n",
    "            \n",
    "            trks_any = trks_any.append(temp_shp)\n",
    "\n",
    "# Reset the index of the new tide line dataframe, then print and plot it for confirmation of sucessful loading\n",
    "trks_any.reset_index(drop=True, inplace=True)\n",
    "print(trks_any)\n",
    "trks_any.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7bb35ce8-5c0d-4b26-8383-da4e88b7e2af",
   "metadata": {},
   "source": [
    "### 3. Clean the tide lines of outliers, project them, and add their dates"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3253cf5e-014e-454c-9623-42b85884a283",
   "metadata": {},
   "source": [
    "3a. Clean and project shapefiles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "e172362c-171d-4ff5-8f7f-61d46582557a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 217/217 [00:18<00:00, 11.74it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                Day                                           geometry\n",
      "0         01-MAR-18  LINESTRING (714763.494 1178155.434, 714775.355...\n",
      "1         01-MAR-18  LINESTRING (714569.184 1181127.082, 714574.838...\n",
      "2         01-JUN-18  LINESTRING (714890.882 1178163.085, 714905.865...\n",
      "3         01-JUN-18  LINESTRING (714890.882 1178163.085, 714905.865...\n",
      "4         02-APR-18  LINESTRING (714636.301 1182008.452, 714645.101...\n",
      "..              ...                                                ...\n",
      "212  ACTIVE LOG 001  LINESTRING (714612.985 1180192.437, 714614.245...\n",
      "213  ACTIVE LOG 002  LINESTRING (714624.701 1180089.310, 714626.188...\n",
      "214  ACTIVE LOG 003  LINESTRING (714757.341 1178977.184, 714757.635...\n",
      "215  ACTIVE LOG 004  LINESTRING (714838.394 1178558.392, 714837.461...\n",
      "216  ACTIVE LOG 005  LINESTRING (714832.261 1178550.703, 714832.127...\n",
      "\n",
      "[217 rows x 2 columns]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Suppress warnings\n",
    "warnings.filterwarnings(\"ignore\", category=UserWarning)\n",
    "\n",
    "# Create DataFrame to hold the cleaned tide lines\n",
    "clean_trks_any = gpd.GeoDataFrame()\n",
    "\n",
    "# Iterate through the tide lines present in the original DataFrame, pass them to the cleanLine function, then append them to the new DataFrame\n",
    "for i in tqdm(range(len(trks_any))):\n",
    "    temp_clean_trk = cleanLine(trks_any.loc[[i]])\n",
    "    # print(temp_clean_trk)\n",
    "    clean_trks_any = clean_trks_any.append(temp_clean_trk)\n",
    "\n",
    "# Reset the new DataFrame's index, project it to the original CRS, then reproject it to Projecte Coordinate System.\n",
    "clean_trks_any.reset_index(drop=True, inplace=True)\n",
    "clean_trks_any = clean_trks_any.set_crs(epsg=4326)\n",
    "clean_trks_any = clean_trks_any.to_crs(epsg=32620)\n",
    "\n",
    "# Once again, print and plot to confirm success\n",
    "print(clean_trks_any)\n",
    "clean_trks_any.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9bbf29e-dd46-4bfb-809a-102c36ce1aed",
   "metadata": {},
   "source": [
    "3b. Join the date from the uncleaned lines to the cleaned lines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "fa35eef1-5426-4633-ba07-7d03b936577b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                Day                                           geometry    Date\n",
      "0         01-MAR-18  LINESTRING (714763.494 1178155.434, 714775.355...  010318\n",
      "1         01-MAR-18  LINESTRING (714569.184 1181127.082, 714574.838...  010318\n",
      "2         01-JUN-18  LINESTRING (714890.882 1178163.085, 714905.865...  010618\n",
      "3         01-JUN-18  LINESTRING (714890.882 1178163.085, 714905.865...  010618\n",
      "4         02-APR-18  LINESTRING (714636.301 1182008.452, 714645.101...  020418\n",
      "..              ...                                                ...     ...\n",
      "212  ACTIVE LOG 001  LINESTRING (714612.985 1180192.437, 714614.245...  310318\n",
      "213  ACTIVE LOG 002  LINESTRING (714624.701 1180089.310, 714626.188...  310318\n",
      "214  ACTIVE LOG 003  LINESTRING (714757.341 1178977.184, 714757.635...  310318\n",
      "215  ACTIVE LOG 004  LINESTRING (714838.394 1178558.392, 714837.461...  310318\n",
      "216  ACTIVE LOG 005  LINESTRING (714832.261 1178550.703, 714832.127...  310318\n",
      "\n",
      "[217 rows x 3 columns]\n"
     ]
    }
   ],
   "source": [
    "clean_trks_any = clean_trks_any.merge(trks_any[['Date']], left_index=True, right_index=True)\n",
    "print(clean_trks_any)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3bccdb00-8dc5-4611-b9c9-1b1f4353acbb",
   "metadata": {},
   "source": [
    "### 4. Find all nests that have been submerged by any tide line during their incubation period (90 days)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be0410a5-5bac-4ea5-b115-fed0dca219ec",
   "metadata": {},
   "source": [
    "4a. Initialize dataframe to hold the years, their nests lost, and their total nests. Will later hold the percent lost.\n",
    "\n",
    "User input works like this:\n",
    "1. If the user is only running one year, an empty DataFrame is created\n",
    "2. If the user is running multiple years, and this is the first run/year, an empty DataFrame is created\n",
    "3. If the user is running multiple years, and this is not the first run/year, the DataFrame is not created so that the new year's data can be appended to the existing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "587f6a62-eea7-42a1-9761-1c7d19b0ce8d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "Will you be running one year, or multiple years? (One, Multi)  nes\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter only one of the choices in ().\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "Will you be running one year, or multiple years? (One, Multi)  multi\n",
      "Is this your first run? (Yes/No)  no\n"
     ]
    }
   ],
   "source": [
    "while 1:\n",
    "    multiRun = input('Will you be running one year, or multiple years? (One, Multi) ')\n",
    "    if multiRun.lower() == 'one':\n",
    "        full_total_Date_Losses_df = pd.DataFrame(columns = ['Year', 'Nests_Lost', 'Total_Nests'])\n",
    "        break\n",
    "    elif multiRun.lower() == 'multi':\n",
    "        firstRun = input('Is this your first run? (Yes/No) ')\n",
    "        if firstRun.lower() == 'yes':\n",
    "            full_total_Date_Losses_df = pd.DataFrame(columns = ['Year', 'Nests_Lost', 'Total_Nests'])\n",
    "            break\n",
    "        elif firstRun.lower() == 'no':\n",
    "            break\n",
    "    else:\n",
    "        print('Enter only one of the choices in ().')\n",
    "        continue"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "032a6013-a2f7-4345-acd4-7b554c5db56e",
   "metadata": {},
   "source": [
    "4b. Load nests file, clean it, add its date, find all lines that are within 90 days of its date of laying, determine number of nests lost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "93f96d06-ca2a-4876-919f-8597f0e22625",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 107/107 [01:18<00:00,  1.36it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Lay_Date  Nests_Lost\n",
      "0    010618           8\n",
      "0    010618           0\n",
      "0    020418           6\n",
      "0    020418          10\n",
      "0    020518          19\n",
      "..      ...         ...\n",
      "0    290318           6\n",
      "0    300518           6\n",
      "0    300518           2\n",
      "0    310318           4\n",
      "0    310318           0\n",
      "\n",
      "[93 rows x 2 columns]\n",
      "   Year Nests_Lost Total_Nests  Percent_Loss\n",
      "0  2010       1618        9607     16.841886\n",
      "1  2018        305        3192           NaN\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# Load nests file\n",
    "total_Date_Losses_df = pd.DataFrame()\n",
    "total_Nest_Count = 0\n",
    "\n",
    "# This is used as a counter to show progress, because tqdm doesn't work with glob\n",
    "# The total number of nest files is around 100\n",
    "ncount = 1\n",
    "\n",
    "# Find the total number of nest files to be iterated through\n",
    "nest_file_count = len(fnmatch.filter(os.listdir(f'{gh_path}{year_count}/WPT_SHP/'), '*.shp'))\n",
    "\n",
    "# Iterate through every shapefile in the folder that contains the nest files\n",
    "for filename_path in tqdm(glob.iglob(f'{gh_path}{year_count}/WPT_SHP/*.shp'), total=nest_file_count):\n",
    "    # print(ncount) # Poor-man's progress bar\n",
    "    \n",
    "    # Load nests file, project it to UTM Zone 20N to match tide lines\n",
    "    temp_shp_wpt = gpd.read_file(filename_path)\n",
    "    if temp_shp_wpt.crs == None:\n",
    "        temp_shp_wpt = temp_shp_wpt.set_crs(epsg=4326)\n",
    "    temp_shp_wpt = temp_shp_wpt.to_crs(epsg=32620)\n",
    "\n",
    "    # Pull the nest lay date from the name of the file and assign it to a date column in the nests geodataframe\n",
    "    filename_wpt = os.path.basename(filename_path)\n",
    "    filedate_wpt = filename_wpt[ :6]\n",
    "    temp_shp_wpt['Date'] = filedate_wpt\n",
    "    # print(f'Current date = {filedate_wpt}')\n",
    "    \n",
    "    # Clean the nests GDF of any outliers\n",
    "    clean_wpt = cleanPoints(temp_shp_wpt)\n",
    "\n",
    "    # Check the clean was successful\n",
    "    # print(temp_shp_wpt)\n",
    "    # print(clean_wpt)\n",
    "    \n",
    "    # Check to see if there are points in the clean nests geodataframe\n",
    "    if len(clean_wpt) > 0:\n",
    "        # Count the total number of nests in the file\n",
    "        nest_count = len(clean_wpt)\n",
    "        \n",
    "        # Create a variable that holds the date for this nest file, then create an actual Python Date variable\n",
    "        wpt_date = filedate_wpt\n",
    "        temp_wpt_date = dt.date(int(f'20{wpt_date[4:6]}'), int(wpt_date[2:4]), int(wpt_date[ :2]))\n",
    "\n",
    "        # Initialize two variables, one to hold the total number of nests lost over the incubation period, the other to hold the incubation period\n",
    "        totalCount = 0\n",
    "        incubation_time = 90\n",
    "\n",
    "        # Begin loop to determine nest loss\n",
    "        for i in range(incubation_time):\n",
    "\n",
    "            # Determine which date the nest line should come from, then convert it to a string for comparison later\n",
    "            temp_wpt_date90 = temp_wpt_date + dt.timedelta(days=i)\n",
    "            temp_wpt_date90_str = temp_wpt_date90.strftime('%d%m%y')\n",
    "\n",
    "            # Iterate through the tracks within each date\n",
    "            for x in range(len(clean_trks_any)):\n",
    "                \n",
    "                # Check if there is a track for the target day\n",
    "                if clean_trks_any.at[x, 'Date'] == temp_wpt_date90_str: \n",
    "                    \n",
    "                    # Pull the track and assign it to a variable\n",
    "                    tideline = clean_trks_any.iloc[[x]]\n",
    "\n",
    "                    # Create a flat cap, one-sided buffer of 50 meters to the east of the line, and a flat cap, one-sided buffer of 2 meters to the west of the line\n",
    "                    lrgBuff = tideline.buffer(-50, cap_style=2, single_sided=True)\n",
    "                    smlBuff = tideline.buffer(2, cap_style=2, single_sided=True)\n",
    "\n",
    "                    # Determine how many nests are loacted within each buffer\n",
    "                    lrgContains = ((lrgBuff.contains(clean_wpt))).tolist()\n",
    "                    smlContains = ((smlBuff.contains(clean_wpt))).tolist()\n",
    "\n",
    "                    # Sum the counts to find the total number of nests within buffers\n",
    "                    count = lrgContains.count(True) + smlContains.count(True)\n",
    "                    totalCount = totalCount + count\n",
    "\n",
    "                    # Create a tuple, then a DataFrame, that contains the Lay date and the number of nests lost from that day\n",
    "                    Date_Losses = [(wpt_date, totalCount)]\n",
    "                    Date_Losses_df = pd.DataFrame.from_records(Date_Losses, columns=['Lay_Date', 'Nests_Lost'])\n",
    "\n",
    "        # print(ncount)\n",
    "        # print(Date_Losses_df)\n",
    "        total_Date_Losses_df = total_Date_Losses_df.append(Date_Losses_df)\n",
    "        ncount += 1\n",
    "        total_Nest_Count += nest_count\n",
    "print(total_Date_Losses_df)\n",
    "\n",
    "# Count the total number of nests lost that year and append it to another dataframe, which will hold all the lossess from all the years\n",
    "year_loss_total = total_Date_Losses_df['Nests_Lost'].sum()\n",
    "full_total_Date_Losses = [{'Year': year_count, 'Nests_Lost': year_loss_total, 'Total_Nests': total_Nest_Count}]\n",
    "full_total_Date_Losses_df = full_total_Date_Losses_df.append(full_total_Date_Losses, ignore_index = True)\n",
    "print(full_total_Date_Losses_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9f4ef81-db13-4ce9-be5b-0dfceae8b545",
   "metadata": {},
   "source": [
    "4c. If there is only one entry in the DataFrame, calculate percent loss for the first (only) entry. Otherwise, iterate through entries and calculate percent loss for each entry."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "f0183a1c-7acb-48e1-88fc-c09bf04bcd2d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 2/2 [00:00<00:00, 1999.67it/s]\n"
     ]
    },
    {
     "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>Year</th>\n",
       "      <th>Nests_Lost</th>\n",
       "      <th>Total_Nests</th>\n",
       "      <th>Percent_Loss</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2010</td>\n",
       "      <td>1618</td>\n",
       "      <td>9607</td>\n",
       "      <td>16.841886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018</td>\n",
       "      <td>305</td>\n",
       "      <td>3192</td>\n",
       "      <td>9.555138</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year Nests_Lost Total_Nests  Percent_Loss\n",
       "0  2010       1618        9607     16.841886\n",
       "1  2018        305        3192      9.555138"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "if len(full_total_Date_Losses_df) > 1:\n",
    "    for x in tqdm(range(len(full_total_Date_Losses_df))):\n",
    "        full_total_Date_Losses_df.at[x, 'Percent_Loss'] = (int(full_total_Date_Losses_df.at[x, 'Nests_Lost']) / int(full_total_Date_Losses_df.at[x, 'Total_Nests']) * 100)\n",
    "else:\n",
    "    full_total_Date_Losses_df['Percent_Loss'] = (int(full_total_Date_Losses_df.at[0, 'Nests_Lost']) / int(full_total_Date_Losses_df.at[0, 'Total_Nests']) * 100)\n",
    "\n",
    "full_total_Date_Losses_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5ce9bb7-0e64-4dd8-993f-79af7005ec86",
   "metadata": {},
   "source": [
    "### 5. Save the DataFrame of nest loss statistics as a csv"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "800f6047-2bcb-443e-b0b4-592097ddcd5c",
   "metadata": {},
   "source": [
    "Name is set by whether there are multiple years analyzed or not. One year: named by year. Two or more years: names pulled from dataframe and used to name, seperated by '_'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "ce6c5c57-d14a-4d07-b4d1-96966117c8c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "if len(full_total_Date_Losses_df) > 1:\n",
    "    years_analyzed = []\n",
    "    for idx, rows in full_total_Date_Losses_df.iterrows():\n",
    "        years_temp_list = rows.Year\n",
    "        years_analyzed.append(years_temp_list)\n",
    "    years_for_filename = ('_'.join(years_analyzed))\n",
    "    full_total_Date_Losses_df.to_csv(f'./Lost_Nests_{years_for_filename}.csv')\n",
    "else:\n",
    "    full_total_Date_Losses_df.to_csv(f'./Lost_Nests_{year_count}.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06ba0686-ecd3-46fd-9aca-5e6ed880c32d",
   "metadata": {},
   "source": [
    "### 6. FOR PRESENTATION DEMO ONLY - Visualize the results off running all years"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "ce3f3c0f-7d4e-4d1c-99d3-fa1f05646837",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(filename='Loss_Graph.jpg')"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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}