traits   => [qw/OAuth API::RESTv1_1/],

Darkling Sky / Barefoot
Electric Smile / Dom & Roland
Inspired By Insane / Fade
Cant You See / Optiv & CZA
Right Here / Ulterior Motive
Dead Hollywood / Royalston
Whatever (Mefjus Remix) / Optiv & BTK
Circles / Enei featuring Sam Wills
The Chant / Mikal
Boshoven / Nymfo & June Miller
Marka (feat. Strategy) / Dub Phizix & Skeptical
Run / Spinline
We Are One (NuLogic Remix) / Logistics
Rich Kids / Hamilton
Boiler Room / Ben Fawce & P Fine
We Become One (feat. Foreign Beggars & Craze) / Calyx & TeeBee
Posers / Breakage
To Fly / Eastcolors & Enei
Ice Block / James Marvel & Shimon
This City (12 Mix) / LSB feat Sian Sanderson
Liberation / Enei & Emperor
Midnight Nation / Optiv
Chubrub / Various Artists
Temper / Ed Rush & Optical
Roxy / Nymfo & State Of Mind
Sleepless (B-Complex remix) / New Zealand Shapeshifter
Hide Your Tears Because We Are In Heaven / Unquote

@app.route("/chemblws/compounds/<chembl_id>")
def compound_by_ChEMBLID(chembl_id):
    compound = chembldb.query(Molecule).filter_by(chembl_id=chembl_id).one()
    result = {...}
    return jsonify(result)

{
    "compound": {
        "acdLogd": 7.67, 
        "acdLogp": 7.67, 
        "alogp": 3.63, 
        "chemblId": "CHEMBL1", 
        "knownDrug": "No", 
        "medChemFriendly": "Yes", 
        "molecularFormula": "C32H32O8", 
        "molecularWeight": 544.59, 
        "numRo5Violations": 1, 
        "passesRuleOfThree": "No", 
        "rotatableBonds": 2, 
        "smiles": "COc1ccc2[C@@H]3[C@H](COc2c1)C(C)(C)OC4=C3C(=O)C(=O)C5=C4OC(C)(C)[C@@H]6COc7cc(OC)ccc7[C@H]56", 
        "stdInChiKey": "GHBOEFUAGSHXPO-XZOTUCIWSA-N"
    }
}

{
    "compound": {
        "acdBasicPka": 6.52, 
        "acdLogd": 2.09, 
        "acdLogp": 2.14, 
        "alogp": 2.11, 
        "chemblId": "CHEMBL2", 
        "knownDrug": "Yes", 
        "medChemFriendly": "Yes", 
        "molecularFormula": "C19H21N5O4", 
        "molecularWeight": 383.4, 
        "numRo5Violations": 0, 
        "passesRuleOfThree": "No", 
        "preferredCompoundName": "PRAZOSIN", 
        "rotatableBonds": 4, 
        "smiles": "COc1cc2nc(nc(N)c2cc1OC)N3CCN(CC3)C(=O)c4occc4", 
        "species": "NEUTRAL", 
        "stdInChiKey": "IENZQIKPVFGBNW-UHFFFAOYSA-N", 
        "synonyms": "CP-12299,Minipress,Minizide,PRAZOSIN,Prazosin"
    }
}

from pptx import Presentation
from pptx.util import Inches
from pychembldb import *

prs = Presentation()
title_only_slidelayout = prs.slidelayouts[5]
slide = prs.slides.add_slide(title_only_slidelayout)
shapes = slide.shapes

shapes.title.text = "10.1016/S0960-894X(01)80693-4"

rows = 6
cols = 6
left = Inches(0.5)
top = Inches(1.5)
width = Inches(8.0)
height = Inches(0.8)

tbl = shapes.add_table(rows, cols, left, top, width, height)

tbl.columns[0].width = Inches(2.0)

tbl.cell(0, 0).text = 'inchi_key'
tbl.cell(0, 1).text = 'activity'
tbl.cell(0, 2).text = 'HBD'
tbl.cell(0, 3).text = 'HBA'
tbl.cell(0, 4).text = 'PSA'
tbl.cell(0, 5).text = 'MWT'

for journal in chembldb.query(Doc).filter_by(doi="10.1016/S0960-894X(01)80693-4"):
    for assay in journal.assays[:1]:
        for i, activity in enumerate(assay.activities[:5], 1):
            tbl.cell(i, 0).text = activity.compound.molecule.structure.standard_inchi_key
            tbl.cell(i, 1).text = str(activity.published_value)
            tbl.cell(i, 2).text = str(activity.compound.molecule.property.hbd)
            tbl.cell(i, 3).text = str(activity.compound.molecule.property.hba)
            tbl.cell(i, 4).text = str(activity.compound.molecule.property.psa)
            tbl.cell(i, 5).text = str(activity.compound.molecule.property.mw_freebase)

prs.save('test.pptx')

> lm.fit <- lm(log(PageViews) ~ log(UniqueVisitors), data = top.1000.sites)
> summary(lm.fit)

Call:
lm(formula = log(PageViews) ~ log(UniqueVisitors), data = top.1000.sites)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.1825 -0.7986 -0.0741  0.6467  5.1549

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)    
(Intercept)         -2.83441    0.75201  -3.769 0.000173 ***
log(UniqueVisitors)  1.33628    0.04568  29.251  < 2e-16 ***
---

from sklearn import linear_model
import pandas as pd
import numpy as np
top_1k_sites = pd.read_csv("top_1000_sites.tsv", sep="\t")
scatter(np.log(top_1k_sites["PageViews"]), np.log(top_1k_sites["UniqueVisitors"]))
clf = linear_model.LinearRegression()
clf.fit(np.log(top_1k_sites[["UniqueVisitors"]]), np.log(top_1k_sites["PageViews"]))
clf.coef_        # array([ 1.33627803])
clf.intercept_ # -2.834409473735672

!!! 5
html
  head
    meta(charset='utf-8')
    meta(name='viewport', content='width=device-width')
    title Handlebars demo
    link(rel='stylesheet', href='stylesheets/app.css')
    script(src='javascripts/vendor/custom.modernizr.js')
  body
    .row
      .large-12
        h2 PythonScript with Handlebars
        hr
    script(src='javascripts/jquery.js')
    script(src='javascripts/handlebars.js')
    script(src='javascripts/pythonscript.js')
    script(src='javascripts/jquery.py.js')
    script(src='javascripts/app.py.js')
    script(src='javascripts/foundation/foundation.js')
    script
      $(document).foundation();

source = "<h3>{{title}}</h3><div>{{body}}</div>"

template =  JS("Handlebars.compile(source)")
title = "ひげひげひげ"
body = "セクシーコマンドー外伝"
data = JS("template({title:title,body:body})")
J("body .row ").append(data)