Landcover factor exploration

In [1]:

    

import pandas
from Pymilio import database
import numpy as np

from colour import Color


    

In [2]:

    

import matplotlib.pylab as plt


    

In [3]:

    

%matplotlib inline


    

In [4]:

    

db = database.Pymilio_db_connection(user='pumilio',
                                    database='pumilio',
                                    read_default_file='~/.my.cnf.pumilio')


    

In [5]:

    

Sounds = db.fetch_as_pandas_df(table='Sounds', fields=['SoundID', 'SiteID', 'ColID']).set_index('SoundID')
Sites = db.fetch_as_pandas_df(table='Sites',
                              fields=['SiteID', 'ID', 'SiteName'],
                              where='ID > 0 AND ID <= 30').set_index('ID')


    

In [6]:

    

LandcoverTypes = db.fetch_as_pandas_df(table='LandcoverTypes', 
                                       fields=['*']).set_index('ID')


    

In [7]:

    

LandcoverArea = db.fetch_as_pandas_df(table='LandcoverAreas', 
                                       fields=['*'],
                                       where='IncludedArea = "500m"').set_index('ID')


    

In [8]:

    

landcover_area = LandcoverArea.join(Sites.drop('SiteID', axis=1), on='SiteID', how='right').sort_values(by='SiteID')


    

In [9]:

    

full_area = 775665.717


    

In [10]:

    

landcover_area_nosort = landcover_area


    

In [11]:

    

landcover_area.sort_values(by=['9', '2'],
                           ascending=[False, True],
                           inplace=True)


    

In [12]:

    

landcover_area['SiteID'].as_matrix()


    

    
Out[12]:





array([23, 30, 22, 17, 19,  2,  4,  9, 11, 10, 25, 13, 24, 12, 14, 27,  1,
       28,  6, 18,  8, 15, 20,  7, 26, 16,  5,  3, 21, 29])

In [21]:

    

# all categories


    

In [15]:

    

plt.figure(figsize=(15, 5))

bar_width = 0.9

ID = np.array([ n for n in range(len(landcover_area)) ])
SiteIDs = landcover_area['SiteID'].as_matrix()
left = ID + 0.05
height = np.zeros(len(landcover_area))
bottom = np.zeros(len(landcover_area))
for index, column in landcover_area.ix[:,'1':'15'].iteritems():
    height = column.as_matrix()
    plt.bar(left=left, 
            height=height, 
            bottom=bottom,
            width=bar_width,
            color=LandcoverTypes['Color'][int(index)],
            edgecolor=None,
            linewidth=0)
    bottom = bottom + height

plt.xlim(0, 30)
plt.ylim(0, full_area)
plt.xlabel('Sites')
plt.ylabel('Area (square meters)')
plt.title('Landcover')
xticks = ID + 0.5
xticklabels = landcover_area['SiteName'].as_matrix()
xticklabels = [ "{0} - {1}".format(xticklabels[i], SiteIDs[i]) for i in ID ]
plt.xticks(xticks, xticklabels, rotation='vertical')
plt.show()


    

In [23]:

    

IndexNDSI = db.fetch_as_pandas_df(table='IndexNDSI', fields=['Sound', 'ndsi_left', 'ndsi_right', 'biophony_left', 'biophony_right', 'anthrophony_left', 'anthrophony_right']).set_index('Sound')


    

In [24]:

    

ndsi = IndexNDSI.join(Sounds).join(Sites.drop('SiteID', axis=1), on='SiteID')


    

In [25]:

    

ndsi_collection1 = ndsi.groupby('ColID').get_group(1)


    

In [26]:

    

ndsi_collection1 = ndsi_collection1.rename(columns={"SiteID": "ID"})


    

In [27]:

    

ndsi_collection1_byID = ndsi_collection1.groupby('ID')


    

In [28]:

    

xticklabels = landcover_area['SiteName'].as_matrix()
xticklabels = [ "{0} - {1}".format(xticklabels[i], SiteIDs[i]) for i in ID ]


    

In [29]:

    

plt.figure(figsize=(15,10))
for name, group in ndsi_collection1_byID:
    x = (group['ID'].as_matrix() - 100) - 0.1
    y = group['biophony_left'].as_matrix()
    plt.plot(x, y, 'r-')
    plt.scatter(x, y, color='red', marker='.')
    x = (group['ID'].as_matrix() - 100) + 0.1
    y = group['biophony_right'].as_matrix()
    plt.plot(x, y, 'b-')
    plt.scatter(x, y, color='blue', marker='.')
plt.xlim(0, 31)
plt.ylim(0, 2.5)
plt.xlabel('Sites')
plt.ylabel('biophony')
plt.title('biophony_left and biophony_right')
xticks = [i for i in range(1, len(ndsi_collection1_byID)+1)]
xticklabels = Sites.sort_index()['SiteName'].as_matrix()
xticklabels = ["{0} - {1}".format(xticklabels[i-1], i) for i in xticks]
plt.xticks(xticks, xticklabels, rotation='vertical')
plt.grid()