<jupyter_start><jupyter_text>Quick Plot of Waterbody Timeseries* **Compatability:** Notebook currently compatible with the `NCI` environment only. You can make this notebook `Sandbox` compatible by pointing it to the DEA Waterbodies timeseries located in AWS.* **Products used:** None.* **Prerequisites:** This notebook explores the individual waterbody timeseries csvs contained within the DEA Waterbodies dataset. It has been designed with that very specific purpose in mind, and is not intended as a general analysis notebook.   DescriptionThis notebook plots up an individual waterbody timeseries from DEA Waterbodies1. Load the required python modules2. Set up the directory where the timeseries data are all located3. Choose the waterbody to be plotted4. Draw a plot***  Getting startedTo run this analysis, run all the cells in the notebook, starting with the "Load packages" cell.   Load packagesImport Python packages that are used for the analysis.<jupyter_code>%matplotlib inline
import matplotlib.pyplot as plt

import pandas as pd
from pandas.plotting import register_matplotlib_converters
register_matplotlib_converters()<jupyter_output><empty_output><jupyter_text>Analysis parameters* `TimeseriesDir`: Folder where the DEA Waterbodies timeseries are saved* `WaterID`: A string containing the ID of the waterbody to plot<jupyter_code>TimeseriesDir = '/g/data/r78/cek156/dea-notebooks/Scientific_workflows/DEAWaterbodies/timeseries_aus_uid/'
WaterID = 'r4f2epyvw'<jupyter_output><empty_output><jupyter_text>Plot it!<jupyter_code>TimeHistory = pd.read_csv(f'{TimeseriesDir}{WaterID[:4]}/{WaterID}.csv')

TimeHistory['Observation Date'] = pd.to_datetime(TimeHistory['Observation Date'])
RemoveMissing = TimeHistory.dropna()

plt.figure(figsize = [10,8])
plt.plot(RemoveMissing['Observation Date'], RemoveMissing['Wet pixel percentage'], '-o')
plt.xlabel('Year')
plt.ylabel('Percentage full')
plt.title(f'Percentage of total surface area observed as wet for ID {WaterID}');
#plt.xlim(['01-01-2018', '01-01-2020'])<jupyter_output><empty_output><jupyter_text>*** Additional information**License:** The code in this notebook is licensed under the [Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0). Digital Earth Australia data is licensed under the [Creative Commons by Attribution 4.0](https://creativecommons.org/licenses/by/4.0/) license.**Contact:** If you need assistance, please post a question on the [Open Data Cube Slack channel](http://slack.opendatacube.org/) or on the [GIS Stack Exchange](https://gis.stackexchange.com/questions/ask?tags=open-data-cube) using the `open-data-cube` tag (you can view previously asked questions [here](https://gis.stackexchange.com/questions/tagged/open-data-cube)).If you would like to report an issue with this notebook, you can file one on [Github](https://github.com/GeoscienceAustralia/dea-notebooks).**Last modified:** January 2020**Compatible datacube version:** N/A  TagsBrowse all available tags on the DEA User Guide's [Tags Index](https://docs.dea.ga.gov.au/genindex.html)<jupyter_code>**Tags**: :index:`DEA Waterbodies`<jupyter_output><empty_output>
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