Index file visualization

This notebook shows an easy way to represent the In Situ data positions using the index files.
For this visualization of a sample index_latest.txt dataset of the Copernicus Marine Environment Monitoring Service.



In [11]:

    
indexfile = "datafiles/index_latest.txt"

Import packages

We use the two packages:

folium for the visualization and
numpy for the data reading / processing.



In [12]:

    
import numpy as np
import folium

Selection criteria

Here we show how the data positions can be selected prior to the plot.
The example explains the selection by data providers, but that can be generalised to other properties.

Provider

The list dataprovider contains the name of the providers we want to keep for the plot.



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dataproviderlist = ['IEO', 'INSTITUTO ESPANOL DE OCEANOGRAFIA', 'SOCIB']

Here we could also add something for the time or space domain.

Load and prepare data

Since the index_latest.txt is a formatted file, we use the numpy function genfromtxt to extract the data from the document.



In [14]:

    
dataindex = np.genfromtxt(indexfile, skip_header=6, unpack=True, delimiter=',', dtype=None, \
              names=['catalog_id', 'file_name', 'geospatial_lat_min', 'geospatial_lat_max',
                     'geospatial_lon_min', 'geospatial_lon_max',
                     'time_coverage_start', 'time_coverage_end', 
                     'provider', 'date_update', 'data_mode', 'parameters'])

For each data file, the positions are defined as a bounding box.
To define the position shown on the map, we use the mean of the stored geospatial_lat/lon_min/max for each dataset.



In [15]:

    
lon_min = dataindex['geospatial_lon_min']
lon_max = dataindex['geospatial_lon_max']
lat_min = dataindex['geospatial_lat_min']
lat_max = dataindex['geospatial_lat_max']
lonmean, latmean = 0.5*(lon_min + lon_max), 0.5*(lat_min + lat_max)

Select by data provider

We create a list of indices corresponding to the entries with a provider belonging to the list specified at the beginning.



In [16]:

    
indexlist = []
for np, provider in enumerate(dataindex['provider']):
    matching = [s for s in dataproviderlist if s in provider]
    if matching:
        indexlist.append(np)

Could do intersection of the list, but for that we need to specify the provider name as specified in the index file.

Visualization

Finally, we create the map object.
The map is centered on the Mediterranean Sea, this has to be changed according to the region of interest and the index file we consider.



In [17]:

    
map = folium.Map(location=[39.5, 2], zoom_start=8)
cntr = 0
for i in indexlist:
    curr_data = dataindex[i]
    link = curr_data[1]
    
    last_idx_slash = link.rfind('/')
    
    ncdf_file_name = link[last_idx_slash+1::]
    folium.Marker( location = [latmean[i], lonmean[i]], popup=ncdf_file_name).add_to(map)

Finally we can create the map where we will see the locations of the platforms as well as the corresponding file names.



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map









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