#GPS Collar

import pandas as pd #for handling csv and csv contents
from rdflib import Graph, Literal, RDF, URIRef, Namespace #basic RDF handling
from rdflib.namespace import FOAF , XSD, SSN, SOSA #most common namespaces
import urllib.parse #for parsing strings to URI's
import matplotlib.pyplot as plt
import plotly.graph_objects as go
import plotly.express as px
import stardog
import json
import io
import os

df = pd.read_csv('Jasmin.csv')

---------------------------------------------------------------------------
FileNotFoundError                         Traceback (most recent call last)
Input In [2], in <cell line: 1>()
----> 1 df = pd.read_csv('Jasmin.csv')

File C:\Python310\lib\site-packages\pandas\util\_decorators.py:311, in deprecate_nonkeyword_arguments.<locals>.decorate.<locals>.wrapper(*args, **kwargs)
    305 if len(args) > num_allow_args:
    306     warnings.warn(
    307         msg.format(arguments=arguments),
    308         FutureWarning,
    309         stacklevel=stacklevel,
    310     )
--> 311 return func(*args, **kwargs)

File C:\Python310\lib\site-packages\pandas\io\parsers\readers.py:680, in read_csv(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, error_bad_lines, warn_bad_lines, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options)
    665 kwds_defaults = _refine_defaults_read(
    666     dialect,
    667     delimiter,
   (...)
    676     defaults={"delimiter": ","},
    677 )
    678 kwds.update(kwds_defaults)
--> 680 return _read(filepath_or_buffer, kwds)

File C:\Python310\lib\site-packages\pandas\io\parsers\readers.py:575, in _read(filepath_or_buffer, kwds)
    572 _validate_names(kwds.get("names", None))
    574 # Create the parser.
--> 575 parser = TextFileReader(filepath_or_buffer, **kwds)
    577 if chunksize or iterator:
    578     return parser

File C:\Python310\lib\site-packages\pandas\io\parsers\readers.py:933, in TextFileReader.__init__(self, f, engine, **kwds)
    930     self.options["has_index_names"] = kwds["has_index_names"]
    932 self.handles: IOHandles | None = None
--> 933 self._engine = self._make_engine(f, self.engine)

File C:\Python310\lib\site-packages\pandas\io\parsers\readers.py:1217, in TextFileReader._make_engine(self, f, engine)
   1213     mode = "rb"
   1214 # error: No overload variant of "get_handle" matches argument types
   1215 # "Union[str, PathLike[str], ReadCsvBuffer[bytes], ReadCsvBuffer[str]]"
   1216 # , "str", "bool", "Any", "Any", "Any", "Any", "Any"
-> 1217 self.handles = get_handle(  # type: ignore[call-overload]
   1218     f,
   1219     mode,
   1220     encoding=self.options.get("encoding", None),
   1221     compression=self.options.get("compression", None),
   1222     memory_map=self.options.get("memory_map", False),
   1223     is_text=is_text,
   1224     errors=self.options.get("encoding_errors", "strict"),
   1225     storage_options=self.options.get("storage_options", None),
   1226 )
   1227 assert self.handles is not None
   1228 f = self.handles.handle

File C:\Python310\lib\site-packages\pandas\io\common.py:789, in get_handle(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)
    784 elif isinstance(handle, str):
    785     # Check whether the filename is to be opened in binary mode.
    786     # Binary mode does not support 'encoding' and 'newline'.
    787     if ioargs.encoding and "b" not in ioargs.mode:
    788         # Encoding
--> 789         handle = open(
    790             handle,
    791             ioargs.mode,
    792             encoding=ioargs.encoding,
    793             errors=errors,
    794             newline="",
    795         )
    796     else:
    797         # Binary mode
    798         handle = open(handle, ioargs.mode)

FileNotFoundError: [Errno 2] No such file or directory: 'Jasmin.csv'

df['Altitude']= pd.to_numeric(df['Altitude'],errors='coerce')
df['Altitude'].mean().round(6)

143.757143

df.head(10)

	ID	LocalDate	LocalTime	GMTDate	GMTTime	lat	long	Temperature	Speed	Direction	Altitude	Cov	HDOP	Distance	Count
0	1SAT32	2011-10-26	07:40:35 AM	2011-10-25	11:40:35 PM	5.437206	118.111301	24.5	0.24	0	NaN	0	1.64	0	1
1	2SAT32	2011-10-26	03:41:35 PM	2011-10-26	07:41:35 AM	5.433402	118.108162	29.0	0.76	0	NaN	0	1.74	548	2
2	3SAT32	2011-10-26	11:40:35 PM	2011-10-26	03:40:35 PM	5.433734	118.108599	27.0	0.86	0	NaN	0	2.18	61	3
3	4SAT32	2011-10-27	11:40:34 PM	2011-10-27	03:40:34 PM	5.455738	118.183640	28.0	0.16	0	NaN	0	1.89	8669	4
4	5SAT32	2011-10-28	07:42:53 AM	2011-10-27	11:42:53 PM	5.456503	118.187684	27.5	1.46	0	NaN	1	2.50	456	5
5	6SAT32	2011-10-28	03:41:05 PM	2011-10-28	07:41:05 AM	5.483344	118.232541	33.5	0.06	0	NaN	5	1.45	5800	6
6	7SAT32	2011-10-28	03:41:05 PM	2011-10-28	07:41:05 AM	5.483344	118.232541	33.5	0.06	0	NaN	5	1.45	0	7
7	8SAT32	2011-10-28	03:41:05 PM	2011-10-28	07:41:05 AM	5.483344	118.232541	33.5	0.06	0	NaN	5	1.45	0	8
8	9SAT32	2011-10-28	07:40:34 PM	2011-10-28	11:40:34 AM	5.479701	118.243606	29.0	0.14	0	NaN	4	0.95	1291	9
9	10SAT32	2011-10-28	07:40:34 PM	2011-10-28	11:40:34 AM	5.479701	118.243606	29.0	0.14	0	NaN	5	0.95	0	10

df.describe()

	lat	long	Temperature	Speed	Direction	Altitude	Cov	HDOP	Distance	Count
count	899.000000	899.000000	899.000000	899.000000	899.000000	7.000000	899.000000	899.000000	899.000000	899.000000
mean	5.509015	118.237317	28.888765	0.587164	1.329255	143.757143	2.497219	2.550044	640.407119	450.000000
std	0.073845	0.105518	4.016236	0.673300	17.429289	88.443143	2.170552	3.812480	917.534269	259.663243
min	5.392483	117.981100	-35.500000	0.000000	0.000000	101.500000	0.000000	0.710000	0.000000	1.000000
25%	5.465016	118.199810	28.000000	0.180000	0.000000	103.050000	0.000000	1.295000	145.500000	225.500000
50%	5.490733	118.248029	28.500000	0.370000	0.000000	114.900000	2.000000	1.830000	341.000000	450.000000
75%	5.546202	118.311595	30.000000	0.735000	0.000000	120.200000	5.000000	2.715000	761.000000	674.500000
max	5.706847	118.403456	37.500000	6.610000	343.000000	343.400000	5.000000	99.900000	9603.000000	899.000000

# 'Visualising Elephant Jasmin Movements'
# Creating and visualizing a scatter plot on Mapbox
fig = px.scatter_mapbox(df, lat='lat', lon='long',
                  animation_frame = 'GMTDate', animation_group = 'GMTTime',
                  color='Speed', size='Speed',
                  color_continuous_scale=px.colors.cyclical.IceFire,
                  size_max=50, zoom=1.75, hover_name='ID', 
                  hover_data = ['GMTDate', 'GMTTime', 'lat','long'], 
                  title = 'Visualising Elephant Jasmin Movements')

fig.update_layout(
        height=800,
        margin={'l': 0, 't': 0, 'b': 0, 'r': 0},
        mapbox={
            'center': {'lon': 118, 'lat': 5},
            'accesstoken':"pk.eyJ1IjoibmFlaW1hIiwiYSI6ImNsNDRoa295ZDAzMmkza21tdnJrNWRqNmwifQ.-cUTmhr1Q03qUXJfQoIKGQ",
            'style': 'satellite-streets',
            #'style': "open-street-map",
             #'style': 'outdoors',
            'center': {'lon': 118, 'lat': 5},
            'zoom': 8})
fig.show()

g = Graph()
ID = Namespace('foo_')
owl = Namespace('http://www.w3.org/2002/07/owl#')
sosa = Namespace('http://www.w3.org/ns/sosa/')
foo = Namespace('http://www.ontology/ns/foo/1.1#')
lat = Namespace('http://www.w3.org/2003/01/geo/wgs84_pos#')
long =Namespace('http://www.w3.org/2003/01/geo/wgs84_pos#')
alt = Namespace('http://www.w3.org/2003/01/geo/wgs84_pos#')
time = Namespace('http://www.w3.org/2006/time#')
schema = Namespace('http://schema.org/')
XSD=Namespace('http://www.w3.org/2001/XMLSchema#')
Class = Namespace('http://purl.org/ontology/wo/Class/')
Family = Namespace('http://purl.org/ontology/wo/Family/')
Kingdom = Namespace('http://purl.org/ontology/wo/kingdom/')
Order = Namespace('http://purl.org/ontology/wo/Proboscidea/')
Phylum = Namespace('http://purl.org/ontology/wo/Phylum/')

for index, row in df.iterrows():

    g.add((URIRef(foo+row['ID']), RDF.type, sosa.Observation))
    g.add((URIRef(foo+row['ID']), sosa.madeBySensor, URIRef(foo+'Jasmin')))
    g.add((URIRef(foo+row['ID']), sosa.hasResult, Literal(row['ID'], datatype=XSD.string)))
    
    g.add((URIRef(foo+row['ID']), sosa.observedProperty, URIRef(foo+'LocalDate')))
    g.add((URIRef(foo+row['ID']), sosa.resultTime, Literal(row['LocalDate'], datatype=XSD.date)))
          
    g.add((URIRef(foo+row['ID']), sosa.observedProperty, URIRef(foo+'LocalTime')))
    g.add((URIRef(foo+row['ID']), sosa.resultTime, Literal(row['LocalTime'], datatype=XSD.time)))
      
    g.add((URIRef(foo+row['ID']), sosa.observedProperty, URIRef(foo+'GMTDate')))
    g.add((URIRef(foo+row['ID']), sosa.resultTime, Literal(row['GMTDate'], datatype=XSD.date)))
    
    g.add((URIRef(foo+row['ID']), sosa.observedProperty, URIRef(foo+'GMTTime')))
    g.add((URIRef(foo+row['ID']), sosa.resultTime, Literal(row['GMTTime'], datatype=XSD.time)))
    
    g.add((URIRef(foo+row['ID']), sosa.observedProperty, URIRef(foo+'lat')))
    g.add((URIRef(foo+row['ID']), wgs_84.lat, Literal(row['lat'], datatype=XSD.float)))
     
    g.add((URIRef(foo+row['ID']), sosa.observedProperty, URIRef(foo+'long')))
    g.add((URIRef(foo+row['ID']), wgs_84.long, Literal(row['long'], datatype=XSD.float)))
    
    g.add((URIRef(foo+row['ID']), sosa.observedProperty, URIRef(foo+'Temperature')))
    g.add((URIRef(foo+row['ID']), foo.hasTemperature, Literal(row['Temperature'], datatype=XSD.double)))
    
    g.add((URIRef(foo+row['ID']), sosa.observedProperty, URIRef(foo+'Speed')))
    g.add((URIRef(foo+row['ID']), foo.hasSpeed, Literal(row['Speed'], datatype=XSD.float)))
    
    g.add((URIRef(foo+row['ID']), sosa.observedProperty, URIRef(foo+'Altitude')))
    g.add((URIRef(foo+row['ID']),  wgs_84.alt, Literal(row['Altitude'], datatype=XSD.float)))
   
    g.add((URIRef(foo+row['ID']), sosa.observedProperty, URIRef(foo+'Direction')))
    g.add(( URIRef(foo+row['ID']), sosa.hasDirection, Literal(row['Direction'], datatype=XSD.float)))
    
    g.add((URIRef(foo+row['ID']), sosa.observedProperty, URIRef(foo+'Distance')))
    g.add((URIRef(foo+row['ID']), foo.hasDistance, Literal(row['Distance'], datatype=XSD.float)))
    
    g.add((URIRef(foo+row['ID']), sosa.observedProperty, URIRef(foo+'HDOP')))
    g.add((URIRef(foo+row['ID']), foo.hasHDOP, Literal(row['HDOP'], datatype=XSD.integer)))
 
    g.add((URIRef(foo+row['ID']), sosa.observedProperty, URIRef(foo+'Cov')))
    g.add((URIRef(foo+row['ID']), foo.hasCov, Literal(row['Cov'], datatype=XSD.integer)))
    
    g.add((URIRef(foo+row['ID']), sosa.observedProperty, URIRef(foo+'Count')))
    g.add((URIRef(foo+row['ID']), foo.hasCount, Literal(row['Count'], datatype=XSD.integer)))
    
    g.add((URIRef(foo+row['ID']), sosa.hasFeatureOfInterest, URIRef(foo+'ElephasMaximus')))
    
    g.add((URIRef(foo+'ElephasMaximus'), RDF.type, Class.Mammalia))
    
    g.add((URIRef(foo+'ElephasMaximus'), RDF.type, Family.Elephantidae))
  
    g.add((URIRef(foo+'ElephasMaximus'), RDF.type, Kingdom.Animalia))
          
    g.add((URIRef(foo+'ElephasMaximus'), RDF.type, Phylum.Chordata))

# print(g.serialize(format='turtle')).head(10)

# saving ontology to disk
g.serialize("Jasmin.rdf", format="ttl")

# adding serialized data to Stardog (knowledge graph platform)

conn_details = {
  'endpoint': 'http://localhost:5820',
  'username': 'admin',
  'password': 'admin'
}
with stardog.Admin(**conn_details) as admin:
    Jasmin = admin.new_database('Jasmin')

conn = stardog.Connection('Jasmin', **conn_details)

conn.begin()

conn.add(
    stardog.content.File('Jasmin.rdf', stardog.content_types.TURTLE),
)

conn.commit()

# Fetch data back from Stardog 
conn_details = {
  'endpoint': 'http://localhost:5820',
  'username': 'admin',
  'password': 'admin'}

conn = stardog.Connection('FOO', **conn_details)