#Import the WealthKG object from the python file
from WealthKG import wealthKG

# First intialize the framework with a SPARQL endpoint and any needed prefixes. It will return an object to help with querying
url = 'https://query.wikidata.org/sparql'
prefixes = ["wd: <http://www.wikidata.org/entity/>",
            "wdt: <http://www.wikidata.org/prop/direct/>"]
wealthKG = wealthKG.WealthKG(url, prefixes)

# Define the filters needed. The query uses a "?s <p> <o>" format so the filter used for the class will have
# to be a "<p> <o>" format.
class_filters = ['wdt:P31 wd:Q5', #is instance (P31) of human (Q5)
                 'wdt:P106 wd:Q82594', #has occupation (P106) of computer scientist (Q82594)
                ] 

# Define any aditional filters that may be need for the query. The format is flexible as long as it follows
# the SPARQL format.
add_filters = ['FILTER(CONTAINS(STR(?s),"wikidata.org/entity/Q"))',
               'FILTER(CONTAINS(STR(?p),"prop/direct/")) ',
               'FILTER(CONTAINS(STR(?i),"prop/direct/"))']

# Define if we want to just count distinct properties (distinct = True) or count all properties (distinct = False)
distinct = True

# Define the max amount of entities to query. If the class has less then the specified limit it will query all
# the entities
limit = 20000
cs_wealth = wealthKG.single_class_query(class_filters, add_filters, distinct, limit)

# To access the dataframe, we can call the objects properties
cs_wealth.dataframe

# We can use this function to create a histogram for a certain part or show the whole thing
# x-axis is the number of properties, y-axis is the number of entities with x amount of properties
# this histogram is showing the distribution of entities based on the number of properties the entities posses
cs_wealth.get_histogram(part='all')

# It is possible to to see a histogram of a certain part
cs_wealth.get_histogram(part='pCount')

# This function can be used for geting a pareto chart for the class in a specified column
cs_wealth.get_pareto_chart('pCount')

# This function returns a statistical summary for a particular column
cs_wealth.get_summary(part='pCount')

# As wealthKG was already initialized we don't need to initialize it again.
# Here is the demonstration for the multi class query. Before that here is an overview of the parameters

class_identifier = "" #The class usually used to denote if something is a class
class_property = "wdt:P31" #The property usually used to denote if an entity is an instance of a class

# Filters for the class. This example wants to get the classes that are a subclass(P279) of human(Q5)
class_add_filters = ['?class wdt:P279 wd:Q5 .']

# Filters for the properties to be queried
add_filters = ['FILTER(CONTAINS(STR(?s),"wikidata.org/entity/Q"))',
               'FILTER(CONTAINS(STR(?p),"prop/direct/")) ', 
               'FILTER(CONTAINS(STR(?i),"prop/direct/")) '] 
distinct = False #False if want to count every property statement

# limit for amount of classes to query. 0 if no limit (or query all the classes). Set 10 for demonstration purposes
class_limit = 10

# limit for amount of entities to query
limit = 10000

# This function is what is used for the query. As we are querying multiple classes it might take a while
# It returns an object that stores the dataframe of each class
human_subclasses = wealthKG.multiclass_query(class_property, class_identifier, 
                                             class_add_filters,  add_filters, 
                                             class_limit=class_limit, distinct=distinct, limit=limit)

#If want to access each dataframe we can call class_dict keys and use a key access the desired class
human_subclasses.class_dict.keys()

human_subclasses.class_dict['Q7569']

# If want to see all histograms for the class than use this function
# x-axis is the number of properties, y-axis is the number of entities with x amount of properties
# this histogram is showing the distribution of entities based on the number of properties the entities posses
human_subclasses.get_all_histogram(part='pCount', title_text="10 Subclasses of Human")

# We can also some analysis for the entities
human_subclasses.get_average_entities()

human_subclasses.get_entity_count_histogram()

# It is also possible to save the files into csv-s stored in a folder
human_subclasses.save_to_csv_folder("human_subclasses")

# And with the WealthKG object we can read from directories.
# Just input the folder directory into the function.
#IMPORTANT NOTE: for windows directories, add an r before the string. Example: r"user\sample_folder\"

human_subclasses = wealthKG.read_csv_folder(r"human_subclasses\")
human_subclasses.class_list