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add lesson6 activity 3
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‎lesson06/Activity03/activity03.ipynb‎

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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Working with custom layers"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"In this activity, we will take a look at how to create custom layers that allow you to not only display geo-spatial data but also animate your datapoints over time. \n",
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"We'll get a deeper understanding of how geoplotlib works and how layers are created and drawn.\n",
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"\n",
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"Our dataset does not only contain spatial but also temporal information which enables us to plot flights over time on our map. \n",
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"There is an example on how to do this with taxis in the examples folder of geoplotlib. \n",
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"https://github.com/andrea-cuttone/geoplotlib/blob/master/examples/taxi.py\n",
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"\n",
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"**Note:** \n",
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"The dataset can be found here: \n",
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"https://datamillnorth.org/dataset/flight-tracking"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### Loading the dataset"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"This time our dataset contains flight data recorded from different machines. \n",
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"Each entry is assigned to a unique plane through a `hex_ident`. \n",
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"Each location is related to a specific timestamp that consists of a `date` and a `time`."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"# importing the necessary dependencies\n",
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"import pandas as pd"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"# loading the dataset from the csv file\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"# displaying the first 5 rows of the dataset\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"# renaming columns latitude to lat and longitude to lon\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"**Note:** \n",
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"Remember that geoplotlib needs columns that are named `lat` and `lon`. You will encounter an error if that is not the case."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [],
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"source": [
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"# displaying the first 5 rows of the dataset\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"---"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### Adding an unix timestamp"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"The easiest way to work with and handle time is to use a unix timestamp. \n",
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"In previous activities, we've already seen how to create a new column in our dataset by applying a function to it. \n",
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"We are using the datatime library to parse the date and time columns of our dataset and use it to create a unix timestamp."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [],
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"source": [
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"# method to convert date and time to an unix timestamp\n",
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"from datetime import datetime\n",
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"\n",
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"def to_epoch(date, time):\n",
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" try:\n",
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" timestamp = round(datetime.strptime('{} {}'.format(date, time), '%Y/%m/%d %H:%M:%S.%f').timestamp())\n",
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" return timestamp\n",
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" except ValueError:\n",
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" return round(datetime.strptime('2017/09/11 17:02:06.418', '%Y/%m/%d %H:%M:%S.%f').timestamp())"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [],
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"source": [
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"# creating a new column called timestamp with the to_epoch method applied\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [],
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"source": [
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"# displaying the first 5 rows of the dataset\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"**Note:** \n",
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"We round up the miliseconds in our `to_epoch` method since epoch is the number of seconds (not miliseconds) that have passes since January 1st 1970. \n",
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"Of course we loose some precision here, but we want to focus on creating our own custom layer instead of wasting a lot of time with our dataset."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"---"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### Writing our custom layer"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"After preparing our dataset, we can now start writing our custom layer. \n",
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"As mentioned at the beginning of this activity, it will be based on the taxi example of geoplotlib. \n",
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"\n",
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"We want to have a layer `TrackLayer` that takes an argument dataset which contains `lat` and `lon` data in combination with a `timestamp`. \n",
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"Given this data, we want to plot each point for each timestamp on the map, creating a tail behind the newest position of the plane.\n",
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"The geoplotlib colorbrewer is used to give each plane a color based on their unique `hex_ident`. \n",
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"The view (bounding box) of our visualization will be set to the city Leeds and a text information with the current timestamp is displayed in the upper right corner."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"metadata": {},
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"outputs": [],
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"source": [
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"# custom layer creation\n",
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"import geoplotlib\n",
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"from geoplotlib.layers import BaseLayer\n",
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"from geoplotlib.core import BatchPainter\n",
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"from geoplotlib.colors import colorbrewer\n",
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"from geoplotlib.utils import epoch_to_str, BoundingBox\n",
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"\n",
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"class TrackLayer(BaseLayer):\n",
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"\n",
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" # initialize class variables\n",
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" def __init__(self, dataset, bbox=BoundingBox.WORLD):\n",
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" self.view = bbox\n",
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" pass\n",
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"\n",
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" # implement draw routine\n",
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" def draw(self, proj, mouse_x, mouse_y, ui_manager):\n",
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" pass\n",
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" \n",
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" # bounding box that gets used when layer is created\n",
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" def bbox(self):\n",
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" return self.view"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"---"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### Visualization with of the custom layer"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"After creating the custom layer, using it is as simple as using any other layer in geoplotlib. \n",
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"We can use the `add_layer` method and pass in our custom layer class with the parameters needed."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Our data is focused on the UK and specifically Leeds. \n",
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"So we want to adjust our bounding box to exactly this area."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"metadata": {},
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"outputs": [],
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"source": [
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"# bounding box for our view on leeds\n",
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"from geoplotlib.utils import BoundingBox\n",
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"\n",
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"leeds_bbox = BoundingBox(north=53.8074, west=-3, south=53.7074 , east=0)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"metadata": {},
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"outputs": [],
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"source": [
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"# displaying our custom layer using add_layer\n",
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"from geoplotlib.utils import DataAccessObject\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"**Note:** \n",
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"In order to avoid any errors associated with the library, we have to convert our pandas dataframe to a geoplotlib DataAccessObject. \n",
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"The creator of geoplotlib provides a handy interface for this conversion."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"When looking at the upper right hand corner, we can clearly see the temporal aspect of this visualization. \n",
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"The first observation we make is that our data is really sparse, we sometimes only have a single data point for a plane, seldomly a whole path is drawn. \n",
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"\n",
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"Even though it is so sparse, we can already get a feeling about where the planes are flying most.\n",
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"\n",
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"**Note:** \n",
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"If you're interested in what else can be achieved with this custom layer approach, there are more examples in the geoplotlib repository. \n",
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"- https://github.com/andrea-cuttone/geoplotlib/blob/master/examples/follow_camera.py\n",
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"- https://github.com/andrea-cuttone/geoplotlib/blob/master/examples/quadtree.py\n",
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"- https://github.com/andrea-cuttone/geoplotlib/blob/master/examples/kmeans.py"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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"codemirror_mode": {
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.0"
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