<jupyter_start><jupyter_text>MADATORY PYTHON LIBRARIES<jupyter_code>%matplotlib  inline
import xarray
import os
import pandas as pd
import matplotlib.pyplot as plt
import datetime
import plotly.plotly as py
import plotly.graph_objs as go
plt.rcParams.update({'font.size': 15})<jupyter_output><empty_output><jupyter_text>IN SITU MOORINGS In Situ 'moorings' comprehends a wide range of fixed-stations such as oceanographic buoys, tide gauges, river flows etc. These platforms provides data time series of certain parameters at a specific location over time. It's platform data sources are: MO & RF PLOTTING ON-SITE TIME SERIE Imagine you have downloaded some _TS_ (time serie) dataset from In Situ moorings (see how to download files froma  certain [platform data source](https://github.com/CopernicusMarineInsitu/INSTACTraining-Phase2UPDATE/blob/master/PythonNotebooks/In_Situ_data_download_by_platform_data_source.ipynb) or [platform category](https://github.com/CopernicusMarineInsitu/INSTACTraining-Phase2UPDATE/blob/master/PythonNotebooks/In_Situ_data_download_by_platform_category.ipynb)) like: [MO_TS_MO_ANDRATX.nc](ftp://nrt.cmems-du.eu/Core/INSITU_MED_NRT_OBSERVATIONS_013_035/history/mooring/MO_TS_MO_ANDRATX.nc)<jupyter_code>dataset = 'MO_TS_MO_ANDRATX.nc'<jupyter_output><empty_output><jupyter_text>Let's have a look to its content:<jupyter_code>full_path2file = os.getcwd()+'/'+dataset #default to current directory
print('path2file: %s'%(full_path2file))
ds = xarray.open_dataset(dataset)<jupyter_output><empty_output><jupyter_text>ds contains all the information about the dataset (relevant metadata, variables, dimensions etc):<jupyter_code>ds<jupyter_output><empty_output><jupyter_text>these attributes can be accesed individually; i.e:<jupyter_code>ds.variables.keys()<jupyter_output><empty_output><jupyter_text>Every of the above parameters varies along certain dimensions (within parenthesis when checking the parameter metadata):<jupyter_code>ds['TEMP']
ds['TEMP'].plot(aspect=2, size=5, color='purple')<jupyter_output><empty_output><jupyter_text>Every of the above variables have a corresponding '_QC' variable, which is the variable that contains the data quality flags:<jupyter_code>ds['TEMP_QC']
ds['TEMP_QC'].plot(aspect=2, size=5)<jupyter_output><empty_output><jupyter_text>This '_QC' variable will therefore guide us when when working with the parameter data to distinguish good from bad data:<jupyter_code>pd.DataFrame(data=ds['TEMP_QC'].attrs['flag_values'], index = ds['TEMP_QC'].attrs['flag_meanings'].split(' '), columns = ['quality flag'])<jupyter_output><empty_output><jupyter_text>This way, we will be able to work with good data by selecting only those values with QC flag 1:<jupyter_code>(ds['TEMP'].where(ds['TEMP_QC'] == 1)).plot(aspect=2, size=5, color='purple')<jupyter_output><empty_output><jupyter_text>Setting grid and labels...<jupyter_code>(ds['TEMP'].where(ds['TEMP_QC'] == 1)).plot(aspect=2, size=5, color='purple')
plt.ylabel(ds['TEMP'].units)
plt.xlabel('year')
plt.title('Sea level at station '+ ds.platform_code)
plt.grid()<jupyter_output><empty_output><jupyter_text>PLOTTING A CERTAIN TIME SERIE RANGE<jupyter_code>subset = ds.sel(TIME=slice('2013-01-01', '2013-12-31'))
(subset['TEMP'].where(subset['TEMP_QC'] == 1)).plot(aspect=2, size=5, color='purple')
plt.ylabel(subset['TEMP'].units)
plt.xlabel('year')
plt.title('Sea level at station '+ ds.platform_code)
plt.grid()<jupyter_output><empty_output><jupyter_text>PLOTTING A CERTAIN TIME SERIE RANGE MAX-MEAN-MIN<jupyter_code>#(subset['TEMP'].where(subset['TEMP_QC'] == 1)).plot(aspect=2, size=5, color='grey')
(subset['TEMP'].where(subset['TEMP_QC'] == 1)).resample(freq='W', how='mean', dim='TIME').plot(color='purple', aspect=2, size=5,)
(subset['TEMP'].where(subset['TEMP_QC'] == 1)).resample(freq='W', how='max', dim='TIME').plot(color='black')
(subset['TEMP'].where(subset['TEMP_QC'] == 1)).resample(freq='W', how='min', dim='TIME').plot(color='black')
plt.ylabel(subset2['TEMP'].units)
plt.xlabel('year')
plt.title('Sea level at station '+ ds2.platform_code)
plt.grid()<jupyter_output><empty_output><jupyter_text>INTERACTIVE PLOTS<jupyter_code>unix = [int(str(element)[:10]) for element in subset['TIME'].values.tolist()]
date_times = [datetime.datetime.fromtimestamp(element) for element in unix]
data = [go.Scatter(x=date_times, y=subset['TEMP'].values.tolist())]
layout = go.Layout(xaxis = dict(range = [unix]))
py.iplot(data, layout=layout)<jupyter_output><empty_output>
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