In [1]:

    

import hydroengine as he

import json
from shapely.geometry import mapping, shape

import pandas as pd

import matplotlib.pyplot as plt
%matplotlib inline  
import seaborn as sns

sns.set(color_codes=True)


    

In [2]:

    

# Limpopo downstream
geometry = { "type": "Point", "coordinates": [33.56529235839844, -24.895779429137615] }
lakes = he.get_lakes(geometry)


    

In [ ]:

    

len(lakes['features'])


    

In [4]:

    

lakes['features'][0]


    

    
Out[4]:





{'geometry': {'coordinates': [[[29.234220174793556, -19.86715432823487],
    [29.234107821684788, -19.86640424660197],
    [29.234983677103635, -19.86572599034646],
    [29.234354874265467, -19.864068609827527],
    [29.234467138511878, -19.86333197407517],
    [29.234220174793556, -19.861665586210588],
    [29.234485139801027, -19.85987796948916],
    [29.234085386523493, -19.858907766769335],
    [29.233241006182023, -19.86027765281979],
    [29.233101783171552, -19.862397707545544],
    [29.232553757575314, -19.863107349197808],
    [29.231129945745817, -19.8642078128075],
    [29.23174980570441, -19.86625597246903],
    [29.231610492624696, -19.86718577863214],
    [29.234027037353027, -19.867639422578307],
    [29.234220174793556, -19.86715432823487]]],
  'geodesic': True,
  'type': 'Polygon'},
 'id': '0000f105acb5489e4fa6',
 'properties': {'Continent': 'Africa',
  'Country': 'Zimbabwe',
  'Depth_avg': 3.2,
  'Dis_avg': 0.114,
  'Elevation': 1337,
  'Grand_id': 0,
  'Hylak_id': 1416075,
  'Lake_area': 0.2,
  'Lake_name': '',
  'Lake_type': 1,
  'Poly_src': 'SWBD',
  'Pour_lat': -19.867048,
  'Pour_long': 29.232903,
  'Res_time': 63.3,
  'Shore_dev': 1.49,
  'Shore_len': 2.34,
  'Slope_100': 2.06,
  'Vol_res': 0.0,
  'Vol_src': 3,
  'Vol_total': 0.62,
  'Wshd_area': 73.3},
 'type': 'Feature'}

In [5]:

    

lake_id = 183160


    

In [6]:

    

ts = he.get_lake_time_series(lake_id, 'water_area')


    

In [7]:

    

d = pd.DataFrame(ts)


    

In [9]:

    

d['time'] = pd.to_datetime(d['time'], unit='ms')


    

In [10]:

    

plt.plot(d.time, d.water_area, '.')


    

    
Out[10]:





[<matplotlib.lines.Line2D at 0x168947f42b0>]






    

In [11]:

    

# remove empty values, the algorithm should be actually smarter, distinguishing between NODATA and NOT WATER
d = d[d.water_area > 0]


    

In [12]:

    

plt.plot(d.time, d.water_area, '-')


    

    
Out[12]:





[<matplotlib.lines.Line2D at 0x168948a62e8>]






    

In [13]:

    

lake = he.get_lake_by_id(lake_id)


    

In [14]:

    

s = shape(lake['geometry'])
s


    

    
Out[14]:

In [15]:

    

lake['properties']


    

    
Out[15]:





{'Continent': 'Africa',
 'Country': 'South Africa',
 'Depth_avg': 6.0,
 'Dis_avg': 2.023,
 'Elevation': 985,
 'Grand_id': 4145,
 'Hylak_id': 183160,
 'Lake_area': 9.66,
 'Lake_name': '',
 'Lake_type': 2,
 'Poly_src': 'SWBD',
 'Pour_lat': -25.310428,
 'Pour_long': 27.472905,
 'Res_time': 333.5,
 'Shore_dev': 3.56,
 'Shore_len': 39.24,
 'Slope_100': 1.64,
 'Vol_res': 58.3,
 'Vol_src': 2,
 'Vol_total': 58.3,
 'Wshd_area': 4548.0}