#!/usr/bin/env python
# coding: utf-8

# In[1]:


try:
    import IPython
except:
    get_ipython().run_line_magic('pip', 'install IPython')
    import IPython 
from IPython.display import display, IFrame, HTML, Javascript
HTML("""<link rel="stylesheet" type="text/css" href="src/css/notebook.css"/>""")


# #  Transforming Collections Data to Linked Art 
# # National Gallery of Art

# ## Input Data
# 
# The collection data exists into two files:
# - CSV data file containing artwork description  [data file](./data/nga/input/nga_ruskin.csv)
# - CSV data file containing detailed digital image information for artworks https://raw.githubusercontent.com/NationalGalleryOfArt/opendata/main/data/published_images.csv
# 
# 
#  #### Further Reading 
#  
# - National Gallery of Art https://www.nga.gov/
# - NGA GitHub https://github.com/NationalGalleryOfArt
# - The input data file is from https://github.com/NationalGalleryOfArt/opendata/tree/main/data

# In[2]:


### Load NGA Collection Data into DataFrame


# In[3]:


file = './data/nga/input/objects.csv'

try:
    import pandas as pd
except:
    get_ipython().system('pip install pandas')
    import pandas as pd
    
mpg = pd.read_csv(file,low_memory=False)
mpg.head()


# ### Load NGA Digital Image File into DataFrame
# 
# The data file containing detailed digital image data is loaded into a pandas dataframe `dataFrameNGAImages`

# In[4]:


file_images = "https://raw.githubusercontent.com/NationalGalleryOfArt/opendata/main/data/published_images.csv"
df_images = pd.read_csv(file_images)
df_images.head()


# ### Remove Byte Order Marks and Define Data Mapping
# 
# Remove Byte Order Marks and create Python dictionary containing data mapping for each input file.

# In[5]:


import csv
try:
    import json
except:
    get_ipython().system('pip install json')
    import json 
    
    
#remove BOM
s = open(file, mode='r', encoding='utf-8-sig').read()
open(file, mode='w', encoding='utf-8').write(s)

allObjects = csv.DictReader(open(file, mode='r',encoding='utf-8'))

for obj in allObjects:
    print(json.dumps(obj,indent=2))
    break 


# ### Transform to JSON-LD 
# 
# This next step uses the following to transform the collections data to Linked Art JSON-LD
# - the data mapping
# - custom coding in createObjProp()
# - cromulant Python library
# - custom coding in la including createObjDescription()
# 
# The URLs for the artwork digital images are in a separate file. With custom coding in `createObjProp()` the rows in the two collection data files are mapped to extract the digital image url.
# 
# <pre>
#     matchImages = dataFrameNGAImages.query('depictstmsobjectid == ' + objProp["id"] )
#     objProp["image_url"] = matchImages["iiifurl"].iloc[0]  + "/full/!500,500/0/default.jpg"
# </pre>
# 
# 
# Additional custom code creates a web page URL for the artwork:
# 
# <pre>
# objProp["homepage"] = "https://www.nga.gov/collection/art-object-page." + id + ".html"   
# </pre>

# In[6]:


mapp =  {
   "id":"objectid",
   "accession_number":"accessionnum",
   "accession_date": "",
   "classification" : "classification",
   "title": "title",
   "alt_title": "",
   "notes": "",
   "date_created":"displaydate",
   "date_created_earliest": "beginyear",
   "date_created_latest": "endyear",
   "created_period":"",
   "created_dynasty":"",
   "created_inscriptions":"",
   "created_notes": "",
   "creator":"attribution",
   "physical_medium": "medium",
   "physical_style": "",
   "physical_technique": "",
   "physical_description": "",
   "physical_dimensions": "dimensions",
   "created_provenance": "" ,
   "credit_line": "creditline",
   "collection" : "departmentabbr",
   "current_status" : "",
   "current_owner" : "",
    "image_url": "",
    "homepage": ""
}

# display transposed dataframe of data mapping
display(pd.DataFrame(mapp, index=[0]).T)


# In[7]:


#  baseURI for JSON-LD document
baseURI = "https://www.nga.gov/collection/"


def createObjProp(obj,mapp,baseURI):
    objProp = {}
    csv_keys = list(obj.keys())
    for key in csv_keys:
        for prop in mapp:
            if key == mapp[prop]:
                if prop == "creator":
                    objProp[prop] = [{"id": baseURI +"creatorid/" + obj[mapp["id"]] ,"name": obj[key],"role":"Artist"}]
                else:
                    objProp[prop] = obj[key]
    objProp["homepage"] = ""
    objProp["current_owner"] = {"name":"National Gallery of Art",
                                "location":"Washington, D.C., United States",
                                "type": "http://vocab.getty.edu/aat/300312281" ,
                                "type_label": ""}
    return objProp   


# In[8]:


from lib import linkedart as la


try:
    import cromulent
except:
    get_ipython().system('pip install cromulent')
    import cromulent
from cromulent.model import factory


outputdir = "./data/nga/output/json/all/"

# list to hold file names for use with jsonld visualisation dropdown
selectOptions = []
selectOptions = [('Please select an artwork', '')]



dfimg_list = df_images['depictstmsobjectid'].tolist()
dfimgurl_list = df_images['iiifurl'].tolist()

counter = 1

for obj in allObjects:
    if counter > 100:
        break
    # create object property dictionary
    objProp = createObjProp(obj,mapp,baseURI)
    
    id = objProp["id"]
    object_uri = baseURI + id
    
    if int(id) in dfimg_list:
        df_images_match = df_images.loc[df_images['depictstmsobjectid'] == int(id)]
        objProp["image_url"] = df_images_match.iloc[0]["iiifurl"] + "/full/!500,500/0/default.jpg"
    
        filename = objProp["id"] + ".json"
        selectOptions.append( ( objProp["title"] + " (" + filename + ")" , filename))
        # create obj description
        objLA = la.createObjDesc(objProp,la.objTypes,object_uri)
    
    
        # write to file 
        text_file = open(outputdir + filename, "wt")
        n = text_file.write(factory.toString(objLA, compact=False))
   
        text_file.close()
        counter = counter + 1
    


# ### Explore the Linked Art JSON-LD files
# 
# Select an artwork from the dropdown to view 
# - the artwork image
# - a visualisation of the Linked Art JSON-LD representation created above

# In[9]:


try:
    import ipywidgets
except:
    get_ipython().run_line_magic('pip', 'install ipywidgets')
    import ipywidgets

from ipywidgets import Layout, FileUpload 
from IPython.display import display, IFrame, HTML, Image
from IPython.core.display import Javascript        
 
import os

try:
    import json
except:
    get_ipython().run_line_magic('pip', 'install json')
    import json 
    
   
def dropdown_eventhandler(change):
    with open('./src/js/visld.js', 'r') as _jscript:
        code = _jscript.read() + "var file = '" + outputdir + change.new + "';var selector = '#visnga';visjsonld(file, selector); "
        display(Javascript(code))
    
        with open( outputdir + "/" + change.new) as json_file:
    
                artwork = json.load(json_file)
                if ("representation" in artwork):
                    image = artwork["representation"][0]["id"]
                    display(Javascript("document.getElementById('artworknga').src = '" + image   +   "';"))
                else:
                    display(Javascript("document.getElementById('artworknga').src = '';"))
                    

selectObject = ipywidgets.Dropdown(options=selectOptions)
selectObject.observe(dropdown_eventhandler, names='value')

display(selectObject)


# <div><img style="height:500px" id="artworknga" src=""/></div>
# 
# <div id='visnga' style='height:100%;width:6000px'></div>

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