Published
Edited
Jan 3, 2022
1 star
g = py`
g = ${rdflib}.Graph()
g.parse(data=${ttl_text}, format="ttl") ;
g`
sparqlquery = py`
g = ${g}
out = []
for rows in g.query(${sparql}):
out.append(rows.asdict())
out
`
foaf_demo = py`
out = []
g = ${g}
for row in g.query(${sparql}):
person = row["person"].n3(g.namespace_manager)
surname = eval(row["surname"].n3())
email = row["email"].n3()
out.append(dict({"person": person, "surname": surname,"email": email}))
out
`
kg = py`
namespaces = {
"nom": "http://example.org/#",
"wtm": "http://purl.org/heals/food/",
"ind": "http://purl.org/heals/ingredient/",
"skos": "http://www.w3.org/2004/02/skos/core#",
}
kg = ${kglab}.KnowledgeGraph(
name = "A recipe KG example based on Food.com",
base_uri = "https://www.food.com/recipe/",
namespaces = namespaces,
)
import io
with io.open('ex2_0_tmp.ttl','w',encoding='utf8') as f:
f.write(${await FileAttachment("ex2_0_tmp.ttl").text()})
kg.load_rdf("ex2_0_tmp.ttl")
kg`
pyVisFrame(await py`
sparql = ${sparql2}
pyvis_graph = ${kg}.visualize_query(sparql, notebook=True)
pyvis_graph.force_atlas_2based()
pyvis_graph.show("tmp.fig06.html")`)
html`${await py`
${pd}.set_option("max_rows", None)
df = ${kg}.query_as_df(${sparql2})
df.head(20).to_html()
`}`
kg_nom = py`
namespaces = {
"nom": "http://example.org/#",
"wtm": "http://purl.org/heals/food/",
"ind": "http://purl.org/heals/ingredient/",
"skos": "http://www.w3.org/2004/02/skos/core#",
}
kg_nom = ${kglab}.KnowledgeGraph(
name = "A recipe KG example based on Food.com",
base_uri = "https://www.food.com/recipe/",
namespaces = namespaces,
)
import io
with io.open('tmp.ttl','w',encoding='utf8') as f:
f.write(${await FileAttachment("ex2_0_tmp.ttl").text()})
kg_nom.load_rdf("tmp.ttl")
with io.open('nom.ttl','w',encoding='utf8') as f:
f.write(${await kgzip.file("kglab-main/dat/nom.ttl").text()})
kg_nom.load_rdf("nom.ttl")
kg_nom`
sparql3 = `SELECT ?recipe ?definition
WHERE {
?recipe rdf:type wtm:Recipe .
?recipe skos:definition ?definition .
?recipe wtm:hasIngredient ind:ChickenEgg .
?recipe wtm:hasIngredient ind:AllPurposeFlour .
?recipe wtm:hasIngredient ind:Salt
}
`
tally_print = py`
tally = []
kg = ${kg_nom}
prints = []
for row in kg.query(${sparql3}):
url = row.recipe
recipe_name = row.definition
# collect two flags to evaluate our noodle vs. pancake rules
has_butter = (url, kg.get_ns("wtm").hasIngredient, kg.get_ns("ind").Butter) in kg.rdf_graph()
sez_noodle = any([x in recipe_name for x in ["noodle", "spaetzle", "dumpling", "pasta"]])
tally.append(( has_butter, sez_noodle ))
if not has_butter and not sez_noodle:
prints.append(dict({"url": url, "recipe_name": recipe_name, "has_butter": has_butter, "sez_noodle": sez_noodle }))
{"tally": tally, "prints": prints}
`
contingency_table = py`
# ${packagesPy}
from collections import Counter
plt = ${plt}
#convert the interior 2-element lists coming from javascript back into tuples:
tally_tuples = [ tuple(x) for x in ${tally_print.get("tally")}]
# warning: \`mosaic\` expects data in (x_axis, y_axis), so revert
# the axis so that this contingency table has the same order
contingency_table = dict(Counter(tally_tuples))
# pyodide failed to convert the contingency_table toJs() at this step so could not split out textual and plot into 2 ObservableHQ cells
from statsmodels.graphics.mosaicplot import mosaic
fig, ax = plt.subplots(1, 1)
fig, _ = mosaic(
contingency_table,
index=[1, 0],
gap=0.05,
title="recipes",
# improvde default cmap
properties=lambda key: {"color": "yellow" if key[1] == "True" else "gray"},
ax=ax,
)
ax.set_xlabel("sez_noddle")
ax.set_ylabel("has_butter")
ax.set_box_aspect(1)
(str(contingency_table), plt.show())
`
contingency_table[1]
kg_annotated_nom = py`
kg = ${kg_nom}
rdflib = ${rdflib}
for id in ${noodle_ids}:
url = f"https://www.food.com/recipe/{id}"
node = rdflib.URIRef(url)
kg.add(node, kg.get_ns("rdf").type, kg.get_ns("nom").Noodle)
for id in ${pancake_ids}:
url = f"https://www.food.com/recipe/{id}"
node = rdflib.URIRef(url)
kg.add(node, kg.get_ns("rdf").type, kg.get_ns("nom").Pancake)
kg.save_rdf("tmp.ttl")
kg`
{
// be sure to run the dependency first
kg_annotated_nom;
return downloadPyoTTLFile("tmp.ttl");
}
//convert from the UTF-8 arrayBuffer that was saved into something we can see in the browser using the one line function above ab2str
{
// be sure to run the dependency first
kg_annotated_nom;
const ttlArrayBuffer = await py`
read = ""
with open("tmp.ttl", "rb") as f:
read = f.read()
read
`;
return ab2str(ttlArrayBuffer);
}
kglab = py` # ${packagesPy}
#make completely sure pyvis is loaded first
import micropip
micropip.install("https://thadk.net/pyodide_packages/pyodide_18_1/pyvis-0.1.9-py2.py3-none-any.whl")
import kglab
# ${otherKglabDependencies} ${micropipDependenciesPy} ${kglabDependency}
kglab
`
rdflib = py` # ${otherKglabDependencies}${micropipDependenciesPy}
import rdflib
rdflib
`
pd = py` # ${packagesPy}
import pandas as pd
pd
`
py`dir(${kglab})`
py`repr(${kglab})`
/* Patch plt.show() to work in Observable from https://observablehq.com/@gnestor/pyodide-demo */
plt = py`
from matplotlib import pyplot as plt
import types
import io
import base64
from js import document
def show(self):
buf = io.BytesIO()
self.savefig(buf, format='png')
buf.seek(0)
img_str = 'data:image/png;base64,' + base64.b64encode(buf.read()).decode('UTF-8')
el = document.createElement('img')
el.src = img_str
return el
plt._show = types.MethodType(plt.show, plt)
plt.show = types.MethodType(show, plt)
plt`
Yasgui = require("@triply/yasgui@4.2.20/build/yasgui.min.js")
yasgui_css = html`<link href="https://unpkg.com/@triply/yasgui/build/yasgui.min.css" rel="stylesheet" type="text/css" />`
Purpose-built for displays of data
Observable is your go-to platform for exploring data and creating expressive data visualizations. Use reactive JavaScript notebooks for prototyping and a collaborative canvas for visual data exploration and dashboard creation.
