def _get_graph_content_block(cls, header="", subheader="", col=12):
df = pd.DataFrame({
"value": [1, 2, 3, 4, 5, 6],
"count": [123, 232, 543, 234, 332, 888]
})
bars = (alt.Chart(df).mark_bar(size=20).encode(y="count:Q",
x="value:O").properties(
height=200,
width=200,
autosize="fit"))
chart = bars.to_json()
return RenderedGraphContent(
**{
"content_block_type": "graph",
"header": header,
"subheader": subheader,
"graph": chart,
"styling": {
"classes": [f"col-{col}"],
"styles": {
"margin-top": "20px"
},
},
})
def test_render_graph():
graph_component_content = RenderedGraphContent(
**{
"content_block_type": "graph",
"header": "Histogram",
"graph": '{"$schema": "https://vega.github.io/schema/vega-lite/v2.6.0.json", "autosize": "fit", "config": {"view": {"height": 300, "width": 400}}, "data": {"name": "data-a681d02fb484e64eadd9721b37015d5b"}, "datasets": {"data-a681d02fb484e64eadd9721b37015d5b": [{"bins": 3.7, "weights": 5.555555555555555}, {"bins": 10.8, "weights": 3.439153439153439}, {"bins": 17.9, "weights": 17.857142857142858}, {"bins": 25.0, "weights": 24.206349206349206}, {"bins": 32.0, "weights": 16.137566137566136}, {"bins": 39.1, "weights": 12.3015873015873}, {"bins": 46.2, "weights": 9.788359788359788}, {"bins": 53.3, "weights": 5.423280423280423}, {"bins": 60.4, "weights": 3.439153439153439}, {"bins": 67.5, "weights": 1.8518518518518516}]}, "encoding": {"x": {"field": "bins", "type": "ordinal"}, "y": {"field": "weights", "type": "quantitative"}}, "height": 200, "mark": "bar", "width": 200}',
"styling": {"classes": ["col-4"]},
}
).to_json_dict()
rendered_doc = ge.render.view.view.DefaultJinjaComponentView().render(
{
"content_block": graph_component_content,
"section_loop": {"index": 1},
"content_block_loop": {"index": 2},
}
)
print(rendered_doc)
rendered_doc = rendered_doc.replace(" ", "").replace("\t", "").replace("\n", "")
assert (
rendered_doc
== """
<div id="section-1-content-block-2" class="col-4" >
<div id="section-1-content-block-2-header" >
<h5>
Histogram
</h5>
</div>
<div class="show-scrollbars">
<div id="section-1-content-block-2-graph" ></div>
</div>
<script>
// Assign the specification to a local variable vlSpec.
vlSpec = {"$schema": "https://vega.github.io/schema/vega-lite/v2.6.0.json", "autosize": "fit", "config": {"view": {"height": 300, "width": 400}}, "data": {"name": "data-a681d02fb484e64eadd9721b37015d5b"}, "datasets": {"data-a681d02fb484e64eadd9721b37015d5b": [{"bins": 3.7, "weights": 5.555555555555555}, {"bins": 10.8, "weights": 3.439153439153439}, {"bins": 17.9, "weights": 17.857142857142858}, {"bins": 25.0, "weights": 24.206349206349206}, {"bins": 32.0, "weights": 16.137566137566136}, {"bins": 39.1, "weights": 12.3015873015873}, {"bins": 46.2, "weights": 9.788359788359788}, {"bins": 53.3, "weights": 5.423280423280423}, {"bins": 60.4, "weights": 3.439153439153439}, {"bins": 67.5, "weights": 1.8518518518518516}]}, "encoding": {"x": {"field": "bins", "type": "ordinal"}, "y": {"field": "weights", "type": "quantitative"}}, "height": 200, "mark": "bar", "width": 200};
// Embed the visualization in the container with id `vis`
vegaEmbed('#section-1-content-block-2-graph', vlSpec, {
actions: false
}).then(result=>console.log(result)).catch(console.warn);
</script>
</div>
""".replace(
" ", ""
)
.replace("\t", "")
.replace("\n", "")
)
def _render_bar_chart_table(cls, evrs):
distinct_values_set_evr = cls._find_evr_by_type(
evrs, "expect_column_distinct_values_to_be_in_set")
if not distinct_values_set_evr or distinct_values_set_evr.exception_info[
"raised_exception"]:
return
value_count_dicts = distinct_values_set_evr.result['details'][
'value_counts']
if isinstance(value_count_dicts, pd.Series):
values = value_count_dicts.index.tolist()
counts = value_count_dicts.tolist()
else:
values = [
value_count_dict['value']
for value_count_dict in value_count_dicts
]
counts = [
value_count_dict['count']
for value_count_dict in value_count_dicts
]
df = pd.DataFrame({
"value": values,
"count": counts,
})
if len(values) > 60:
return None
else:
chart_pixel_width = (len(values) / 60.0) * 500
if chart_pixel_width < 250:
chart_pixel_width = 250
chart_container_col_width = round((len(values) / 60.0) * 6)
if chart_container_col_width < 4:
chart_container_col_width = 4
elif chart_container_col_width >= 5:
chart_container_col_width = 6
elif chart_container_col_width >= 4:
chart_container_col_width = 5
mark_bar_args = {}
if len(values) == 1:
mark_bar_args["size"] = 20
bars = alt.Chart(df).mark_bar(**mark_bar_args).encode(
y='count:Q', x="value:O",
tooltip=["value", "count"]).properties(height=400,
width=chart_pixel_width,
autosize="fit")
chart = bars.to_json()
new_block = RenderedGraphContent(
**{
"content_block_type":
"graph",
"header":
RenderedStringTemplateContent(
**{
"content_block_type": "string_template",
"string_template": {
"template": "Value Counts",
"tooltip": {
"content":
"expect_column_distinct_values_to_be_in_set"
},
"tag": "h6"
}
}),
"graph":
chart,
"styling": {
"classes":
["col-" + str(chart_container_col_width), "mt-1"],
}
})
return new_block
def _get_kl_divergence_chart(cls, partition_object, header=None):
weights = partition_object["weights"]
if len(weights) > 60:
expected_distribution = cls._get_kl_divergence_partition_object_table(
partition_object, header=header
)
else:
chart_pixel_width = (len(weights) / 60.0) * 500
if chart_pixel_width < 250:
chart_pixel_width = 250
chart_container_col_width = round((len(weights) / 60.0) * 6)
if chart_container_col_width < 4:
chart_container_col_width = 4
elif chart_container_col_width >= 5:
chart_container_col_width = 6
elif chart_container_col_width >= 4:
chart_container_col_width = 5
mark_bar_args = {}
if len(weights) == 1:
mark_bar_args["size"] = 20
if partition_object.get("bins"):
bins = partition_object["bins"]
bins_x1 = [round(value, 1) for value in bins[:-1]]
bins_x2 = [round(value, 1) for value in bins[1:]]
df = pd.DataFrame(
{"bin_min": bins_x1, "bin_max": bins_x2, "fraction": weights,}
)
bars = (
alt.Chart(df)
.mark_bar()
.encode(
x="bin_min:O",
x2="bin_max:O",
y="fraction:Q",
tooltip=["bin_min", "bin_max", "fraction"],
)
.properties(width=chart_pixel_width, height=400, autosize="fit")
)
chart = bars.to_json()
elif partition_object.get("values"):
values = partition_object["values"]
df = pd.DataFrame({"values": values, "fraction": weights})
bars = (
alt.Chart(df)
.mark_bar()
.encode(
x="values:N", y="fraction:Q", tooltip=["values", "fraction"]
)
.properties(width=chart_pixel_width, height=400, autosize="fit")
)
chart = bars.to_json()
if header:
expected_distribution = RenderedGraphContent(
**{
"content_block_type": "graph",
"graph": chart,
"header": header,
"styling": {
"classes": [
"col-" + str(chart_container_col_width),
"mt-2",
"pl-1",
"pr-1",
],
"parent": {"styles": {"list-style-type": "none"}},
},
}
)
else:
expected_distribution = RenderedGraphContent(
**{
"content_block_type": "graph",
"graph": chart,
"styling": {
"classes": [
"col-" + str(chart_container_col_width),
"mt-2",
"pl-1",
"pr-1",
],
"parent": {"styles": {"list-style-type": "none"}},
},
}
)
return expected_distribution
def _descriptive_value_counts_bar_chart_renderer(
cls,
configuration=None,
result=None,
language=None,
runtime_configuration=None,
**kwargs,
):
assert result, "Must pass in result."
value_count_dicts = result.result["details"]["value_counts"]
if isinstance(value_count_dicts, pd.Series):
values = value_count_dicts.index.tolist()
counts = value_count_dicts.tolist()
else:
values = [
value_count_dict["value"] for value_count_dict in value_count_dicts
]
counts = [
value_count_dict["count"] for value_count_dict in value_count_dicts
]
df = pd.DataFrame(
{
"value": values,
"count": counts,
}
)
if len(values) > 60:
return None
else:
chart_pixel_width = (len(values) / 60.0) * 500
if chart_pixel_width < 250:
chart_pixel_width = 250
chart_container_col_width = round((len(values) / 60.0) * 6)
if chart_container_col_width < 4:
chart_container_col_width = 4
elif chart_container_col_width >= 5:
chart_container_col_width = 6
elif chart_container_col_width >= 4:
chart_container_col_width = 5
mark_bar_args = {}
if len(values) == 1:
mark_bar_args["size"] = 20
bars = (
alt.Chart(df)
.mark_bar(**mark_bar_args)
.encode(y="count:Q", x="value:O", tooltip=["value", "count"])
.properties(height=400, width=chart_pixel_width, autosize="fit")
)
chart = bars.to_json()
new_block = RenderedGraphContent(
**{
"content_block_type": "graph",
"header": RenderedStringTemplateContent(
**{
"content_block_type": "string_template",
"string_template": {
"template": "Value Counts",
"tooltip": {
"content": "expect_column_distinct_values_to_be_in_set"
},
"tag": "h6",
},
}
),
"graph": chart,
"styling": {
"classes": ["col-" + str(chart_container_col_width), "mt-1"],
},
}
)
return new_block