def test_build_agg_data(unittest, rolling_data):
import dtale.views as views
df, _ = views.format_data(rolling_data)
extended_aggregation = [
dict(col="0", agg="mean"),
dict(col="0", agg="sum"),
dict(col="1", agg="mean"),
]
output, code, cols = chart_utils.build_agg_data(
df,
"date", ["0", "1"], {},
None,
extended_aggregation=extended_aggregation)
unittest.assertEqual(sorted(output.columns),
["0|mean", "0|sum", "1|mean", "date"])
unittest.assertEqual(sorted(cols), ["0|mean", "0|sum", "1|mean"])
output, code, cols = chart_utils.build_agg_data(df, "date", ["0", "1"], {},
"mean")
unittest.assertEqual(list(output.columns), ["date", "0|mean", "1|mean"])
unittest.assertEqual(cols, ["0|mean", "1|mean"])
output, code, cols = chart_utils.build_agg_data(df, "date", ["0", "1"], {},
"drop_duplicates")
unittest.assertEqual(
list(output.columns),
["index", "date", "0|drop_duplicates", "1|drop_duplicates"],
)
unittest.assertEqual(cols, ["0|drop_duplicates", "1|drop_duplicates"])
extended_aggregation = [
dict(col="0", agg="mean"),
dict(col="0", agg="pctsum"),
dict(col="1", agg="mean"),
dict(col="1", agg="first"),
]
output, code, cols = chart_utils.build_agg_data(
df,
"date", ["0", "1"], {},
None,
extended_aggregation=extended_aggregation)
unittest.assertEqual(
sorted(list(output.columns)),
["0|mean", "0|pctsum", "1|first", "1|mean", "date"],
)
unittest.assertEqual(sorted(cols),
["0|mean", "0|pctsum", "1|first", "1|mean"])
def heatmap_builder(data_id, **inputs):
"""
Builder function for :plotly:`plotly.graph_objects.Heatmap <plotly.graph_objects.Heatmap>`
:param data_id: integer string identifier for a D-Tale process's data
:type data_id: str
:param inputs: Optional keyword arguments containing the following information:
- x: column to be used as x-axis of chart
- y: column to be used as y-axis of chart
- z: column to use for the Z-Axis
- agg: points to a specific function that can be applied to :func: pandas.core.groupby.DataFrameGroupBy
:type inputs: dict
:return: heatmap
:rtype: :plotly:`plotly.graph_objects.Heatmap <plotly.graph_objects.Heatmap>`
"""
try:
if not valid_chart(**inputs):
return None
raw_data = global_state.get_data(data_id)
wrapper = chart_wrapper(data_id, raw_data, inputs)
hm_kwargs = dict(hoverongaps=False,
colorscale='Greens',
showscale=True,
hoverinfo='x+y+z')
x, y, z, agg = (inputs.get(p) for p in ['x', 'y', 'z', 'agg'])
y = y[0]
data = retrieve_chart_data(raw_data, x, y, z)
x_title = update_label_for_freq(x)
y_title = update_label_for_freq(y)
z_title = z
data = data.sort_values([x, y])
check_all_nan(data)
dupe_cols = [x, y]
if agg is not None:
z_title = '{} ({})'.format(z_title, AGGS[agg])
if agg == 'corr':
data = data.dropna()
data = data.set_index([x, y]).unstack().corr()
data = data.stack().reset_index(0, drop=True)
y_title = x_title
dupe_cols = [
'{}{}'.format(col, i)
for i, col in enumerate(data.index.names)
]
[x, y] = dupe_cols
data.index.names = dupe_cols
data = data.reset_index()
data.loc[data[x] == data[y], z] = np.nan
hm_kwargs = dict_merge(
hm_kwargs,
dict(colorscale=[[0, 'red'], [0.5, 'yellow'],
[1.0, 'green']],
zmin=-1,
zmax=1))
else:
data = build_agg_data(data, x, y, inputs, agg, z=z)
if not len(data):
raise Exception('No data returned for this computation!')
check_exceptions(
data[dupe_cols],
agg != 'corr',
data_limit=40000,
limit_msg=
'Heatmap exceeds {} cells, cannot render. Please apply filter...')
dtypes = {
c: classify_type(dtype)
for c, dtype in get_dtypes(data).items()
}
data_f, _ = chart_formatters(data)
data = data_f.format_df(data)
data = data.sort_values([x, y])
data = data.set_index([x, y])
data = data.unstack(0)[z]
x_data = weekday_tick_handler(data.columns, x)
y_data = weekday_tick_handler(data.index.values, y)
heat_data = data.values
x_axis = dict_merge({
'title': x_title,
'tickangle': -20
}, build_spaced_ticks(x_data))
if dtypes.get(x) == 'I':
x_axis['tickformat'] = '.0f'
y_axis = dict_merge({
'title': y_title,
'tickangle': -20
}, build_spaced_ticks(y_data))
if dtypes.get(y) == 'I':
y_axis['tickformat'] = '.0f'
hovertemplate = ''.join([
x_title, ': %{customdata[0]}<br>', y_title,
': %{customdata[1]}<br>', z_title, ': %{z}<extra></extra>'
])
hm_kwargs = dict_merge(
hm_kwargs,
dict(z=heat_data,
colorbar={'title': z_title},
hoverinfo='x+y+z',
hovertemplate=hovertemplate,
customdata=[[[xd, yd] for xd in x_data] for yd in y_data]))
return wrapper(
dcc.Graph(id='heatmap-graph-{}'.format(y),
style={
'margin-right': 'auto',
'margin-left': 'auto',
'height': 600
},
figure=dict(data=[go.Heatmap(**hm_kwargs)],
layout=build_layout(
dict_merge(
dict(xaxis=x_axis,
yaxis=y_axis,
xaxis_zeroline=False,
yaxis_zeroline=False),
build_title(x, y, z=z, agg=agg))))))
except BaseException as e:
return build_error(str(e), str(traceback.format_exc()))
def test_build_agg_data():
with pytest.raises(NotImplementedError):
chart_utils.build_agg_data(None, None, None, None, "rolling", z="z")
with pytest.raises(NotImplementedError):
chart_utils.build_agg_data(None, None, None, None, "corr")
def test_build_agg_data():
with pytest.raises(NotImplementedError):
chart_utils.build_agg_data(None, None, None, None, 'rolling', z='z')
with pytest.raises(NotImplementedError):
chart_utils.build_agg_data(None, None, None, None, 'corr')