def create_plot(cls, *, data, tiles_params: Dict, points_params: Dict,
labels_params: Dict, reverse_y: bool, show_legend: bool,
labels_map_size: bool, corr_value_format: str,
threshold) -> PlotSpec:
# Adjust options
labels_map_size = _OpUtil.adjust_type_color_size(
tiles_params, points_params, labels_params, labels_map_size)
_OpUtil.adjust_diag(tiles_params, points_params, labels_params,
threshold)
_OpUtil.flip_type(tiles_params, points_params, labels_params,
reverse_y)
tooltips = (layer_tooltips().format(
field='@..corr..', format=corr_value_format).line('@..corr..'))
plot = ggplot(data)
if tiles_params is not None:
plot += geom_tile(stat='corr',
show_legend=show_legend,
size=0.0,
width=1.002,
height=1.002,
tooltips=tooltips,
sampling='none',
**tiles_params)
plot += coord_cartesian()
else:
plot += coord_fixed()
if points_params is not None:
plot += geom_point(stat='corr',
show_legend=show_legend,
size_unit='x',
mapping=aes(size='..corr_abs..'),
tooltips=tooltips,
sampling='none',
**points_params)
if labels_params is not None:
m = None
if labels_map_size:
m = aes(size='..corr_abs..')
else:
labels_params['size'] = 1
plot += geom_text(stat='corr',
show_legend=show_legend,
mapping=m,
na_text='',
label_format=corr_value_format,
tooltips=tooltips,
size_unit='x',
sampling='none',
**labels_params)
return plot
def as_annotated_data(raw_data: Any, raw_mapping: Any) -> Tuple:
data_meta = {}
# data
data = raw_data
if is_data_pub_stream(data):
data = {}
for col_name in raw_data.col_names:
data[col_name] = []
data_meta.update({'pubsub': {'channel_id': raw_data.channel_id, 'col_names': raw_data.col_names}})
# mapping
mapping = {}
mapping_meta = []
if raw_mapping is not None:
for aesthetic, variable in raw_mapping.as_dict().items():
if aesthetic == 'name': # ignore FeatureSpec.name property
continue
if isinstance(variable, MappingMeta):
mapping[aesthetic] = variable.variable
mapping_meta.append({
'aes': aesthetic,
'annotation': variable.annotation,
'parameters': variable.parameters
})
else:
mapping[aesthetic] = variable
if len(mapping_meta) > 0:
data_meta.update({'mapping_annotations': mapping_meta})
# series annotations
series_meta = []
if is_dict_or_dataframe(data):
for column_name, values in data.items():
if isinstance(values, Iterable):
not_empty_series = any(True for _ in values)
if not_empty_series and all(isinstance(val, datetime) for val in values):
series_meta.append({
'column': column_name,
'type': 'datetime'
})
if len(series_meta) > 0:
data_meta.update({'series_annotations': series_meta})
return data, aes(**mapping), {'data_meta': data_meta}
def as_annotated_data(raw_data: Any, raw_mapping: dict) -> Tuple:
data_meta = {}
# data
data = raw_data
if is_data_pub_stream(data):
data = {}
for col_name in raw_data.col_names:
data[col_name] = []
data_meta.update({
'pubsub': {
'channel_id': raw_data.channel_id,
'col_names': raw_data.col_names
}
})
elif is_geo_data_frame(data):
data_meta.update(get_geo_data_frame_meta(data))
# mapping
mapping = {}
mapping_meta = []
if raw_mapping is not None:
for aesthetic, variable in raw_mapping.as_dict().items():
if aesthetic == 'name': # ignore FeatureSpec.name property
continue
if isinstance(variable, MappingMeta):
mapping[aesthetic] = variable.variable
mapping_meta.append({
'aes': aesthetic,
'annotation': variable.annotation,
'parameters': variable.parameters
})
else:
mapping[aesthetic] = variable
if len(mapping_meta) > 0:
data_meta.update({'mapping_annotations': mapping_meta})
return data, aes(**mapping), {'data_meta': data_meta}