# Let do our imports first.
import respire,urllib,re
from IPython.display import HTML,JSON


# We just need to monkey-patch the URL join method in this instance, 
# since it truncates the URL due to the way ADMESARfari is hosted

def urljoin_patched(base,path):
    return base+path

respire.client.urljoin = urljoin_patched
    
# Create our client and associated methods
api_client = respire.client_from_url('http://wwwdev.ebi.ac.uk/chembl/admesarfari/rest/spore')    



# What methods do we have available?
# Iterate over the parsed endpoint, pulling out applicable methods, the paths and the descriptions.
# We'll add some HTML elements to the output.
tc=[]
ts = '<table><tr>'
te = '</tr></table>'

for method in api_client.description.methods:
        methodname = method
        method = api_client.description.methods[methodname]

        if method['method']!='HEAD':
            tc.append("<tr><th>"+methodname+"</th></tr>")
            tc.append('<tr><td>'+method['path']+'</td><td>'+method['description']+'</td></tr>')


h = HTML(ts+"".join(tc)+te)
h


# Let's set a few lookup dictionaries

# Taxonmy ID look up
# Get the taxids

taxids = api_client.get_taxids()['results']
t = {}

# Create taxonomy look-up

for taxid in taxids:
    t[taxid['taxid']]=taxid['name']

taxids = t

# Get tissues
tissues = api_client.get_tissues()['results'][0]
alltissues = str(",".join(tissues.keys()))
cells = api_client.get_celltypes()['results'][0]



# Get Human expression levels (Could take a while!)
expressionlevels = api_client.get_expressionmatrix(TISSUE_IDS=alltissues)['expression_matrix']
print "Levels found:",expressionlevels.__len__()

# Let's use an input compound and predict it's ADME profile
# We'll use Gleevec (CHEMBL941) as our input

gleevec_ctab = """
 SciTegic12111210002D

 37 41  0  0  0  0            999 V2000
    6.9208   -3.0042    0.0000 C   0  0
    7.5250   -2.6417    0.0000 N   0  0
    3.2167   -3.0417    0.0000 C   0  0
    5.6875   -3.0167    0.0000 C   0  0
    6.3000   -2.6542    0.0000 N   0  0
    3.8292   -2.6792    0.0000 N   0  0
    8.1417   -2.9833    0.0000 C   0  0
    0.1292   -2.0000    0.0000 N   0  0
    5.0667   -2.6667    0.0000 C   0  0
   -1.1083   -2.6917    0.0000 N   0  0
    6.9250   -3.7167    0.0000 N   0  0
    2.6000   -2.6917    0.0000 C   0  0
    4.4542   -3.0292    0.0000 C   0  0
    8.7542   -2.6125    0.0000 C   0  0
    5.6917   -3.7292    0.0000 C   0  0
    3.2250   -3.7500    0.0000 O   0  0
    9.3458   -1.5375    0.0000 N   0  0
    0.7417   -1.6417    0.0000 C   0  0
    2.6000   -1.9792    0.0000 C   0  0
    1.9875   -3.0500    0.0000 C   0  0
    5.0667   -4.0917    0.0000 C   0  0
    0.1167   -2.7167    0.0000 C   0  0
   -0.4708   -1.6417    0.0000 C   0  0
   -0.5000   -3.0542    0.0000 C   0  0
   -1.1000   -1.9792    0.0000 C   0  0
    8.1583   -3.6958    0.0000 C   0  0
    7.5458   -4.0625    0.0000 C   0  0
    1.3667   -1.9917    0.0000 C   0  0
    4.4542   -3.7417    0.0000 C   0  0
    1.9667   -1.6292    0.0000 C   0  0
    1.3750   -2.7042    0.0000 C   0  0
    8.7417   -1.9083    0.0000 C   0  0
   -1.7375   -3.0292    0.0000 C   0  0
    9.3750   -2.9625    0.0000 C   0  0
    9.9750   -1.8833    0.0000 C   0  0
    6.3042   -4.0875    0.0000 C   0  0
    9.9917   -2.5958    0.0000 C   0  0
  2  1  1  0
  3  6  1  0
  4  5  1  0
  5  1  1  0
  6 13  1  0
  7  2  2  0
  8 18  1  0
  9  4  1  0
 10 25  1  0
 11  1  2  0
 12  3  1  0
 13  9  2  0
 14  7  1  0
 15  4  2  0
 16  3  2  0
 17 32  2  0
 18 28  1  0
 19 12  2  0
 20 12  1  0
 21 15  1  0
 22  8  1  0
 23  8  1  0
 24 22  1  0
 25 23  1  0
 26  7  1  0
 27 11  1  0
 28 31  1  0
 29 21  2  0
 30 19  1  0
 31 20  2  0
 32 14  1  0
 33 10  1  0
 34 14  2  0
 35 37  2  0
 36 15  1  0
 37 34  1  0
 27 26  2  0
 29 13  1  0
 17 35  1  0
 28 30  2  0
 24 10  1  0
M  END
"""

predictions = api_client.post_modelpredictor2(data=urllib.quote(gleevec_ctab))['results']

# How many ADME targets were predicted?
print predictions.__len__()

# Let's view the predictions
tc=[]
ts = '<table><tr>'
te = '</tr></table>'

for prediction in predictions:    
        tc.append("<tr><th>"+prediction['PROTEIN_ACCESSION']+"</th><th>"+prediction['full_name']+"</th></tr>")
        if prediction['function'] != None:
            pfunc = prediction['function']
        else:
            pfunc = 'Unknown'
            
        tc.append('<tr><td>'+taxids[prediction['taxid']]+'</td><td>'+pfunc+'</td></tr>')
        
h = HTML(ts+"".join(tc)+te)
h

# Now lets look at expression levels of these targets
# Select only HIGH expression levels
tc=[]
ts = '<table><tr>'
te = '</tr></table>'

for prediction in predictions:    
        tc.append("<tr><th>"+prediction['PROTEIN_ACCESSION']+"</th><th>"+prediction['full_name']+"</th></tr>")
        # This dumps out expression levels for all tissues and cell types!
        targetexpression=[]

        for humexp in expressionlevels:

            for tissue in tissues:

                for cell in cells:

                    percell = humexp[str(tissue)]

                    if str(cell) in percell:

                        if percell[str(cell)]['target_id']==prediction['target_id']:

                            expstring = "Tissue =",percell[str(cell)]['tissue'],", Cell =",cells[str(cell)]," Level =",percell[str(cell)]['exp_level']," Type =",percell[str(cell)]['expression_type']," Reliability =",percell[str(cell)]['reliability']

                            level = percell[str(cell)]['exp_level']
                            if re.match('High|Strong',level):
                                 targetexpression.append(expstring)






        for exp in targetexpression:
            tc.append('<tr><td>'+" ".join(exp)+'</td></tr>')

h = HTML(ts+"".join(tc)+te)
h

# Now lets see how many activity points we have per target
# Let's view the predictions
tc=[]
ts = '<table><tr>'
te = '</tr></table>'

for prediction in predictions:
        try:
            activity = api_client.get_targetbioactivity(TARGET_ID=str(prediction['target_id']))['results']
            print activity.__len__()," activity points"
        except:
            print "Error retrieving data points!"
        #tc.append("<tr><th>"+prediction['PROTEIN_ACCESSION']+"</th><th>"+prediction['full_name']+"</th></tr>")
        #tc.append('<tr><td>Activity points</td><td>'+str(activity.__len__())+'</td></tr>')
        
h = HTML(ts+"".join(tc)+te)
h

# How many compounds do we have per target?
tc=[]
ts = '<table><tr>'
te = '</tr></table>'

for prediction in predictions:
        try:
            targetcompounds = api_client.get_targetcompounds(TARGET_ID=str(prediction['target_id']))['results']
            count = targetcompounds.__len__()," compounds"
        except:
            count = 0
            print "Error retrieving data points!"
        tc.append("<tr><th>"+prediction['PROTEIN_ACCESSION']+"</th><th>"+prediction['full_name']+"</th></tr>")
        tc.append('<tr><td>Activity points</td><td>'+str(count)+'</td></tr>')
        
h = HTML(ts+"".join(tc)+te)
h