def marshall(proto, spec=None, **kwargs):
"""Marshall a proto with DeckGL chart info.
See DeltaGenerator.deck_gl_chart for docs.
"""
if "data" in kwargs:
# TODO: Remove this check after 2019-11-28.
raise Exception(
"The `data` parameter is deprecated. Use `st.map` or"
"specify a spec dict in `st.deck_gl_chart`."
)
data = []
if spec is None:
spec = dict()
# Merge spec with unflattened kwargs, where kwargs take precedence.
# This only works for string keys, but kwarg keys are strings anyways.
spec = dict(spec, **dicttools.unflatten(kwargs))
if "layers" not in spec:
spec["layers"] = []
# Syntax sugar: if no layers defined and data is passed at the top
# level, create a scatterplot layer with the top-level data by default.
if data is not None:
spec["layers"].append({"data": data, "type": "ScatterplotLayer"})
for layer in spec["layers"]:
# Don't add layers that have no data.
if "data" not in layer:
continue
# Remove DataFrame because it's not JSON-serializable
data = layer.pop("data")
layer_proto = proto.layers.add()
fixed_layer = case_converters.convert_dict_keys(
case_converters.to_lower_camel_case, layer
)
layer_proto.spec = json.dumps(fixed_layer)
# TODO: If several layers use the same data frame, the data gets resent
# for each layer. Need to improve this.
data_frame_proto.marshall_data_frame(data, layer_proto.data)
del spec["layers"]
# Dump JSON after removing DataFrames (see loop above), because DataFrames
# are not JSON-serializable.
proto.spec = json.dumps(spec)
def marshall(proto, data=None, spec=None, **kwargs):
"""Marshall a proto with DeckGL chart info.
See DeltaGenerator.deck_gl_chart for docs.
"""
if data is None:
data = []
if spec is None:
spec = dict()
# Merge spec with unflattened kwargs, where kwargs take precedence.
# This only works for string keys, but kwarg keys are strings anyways.
spec = dict(spec, **dicttools.unflatten(kwargs))
if 'layers' not in spec:
spec['layers'] = []
# Syntax sugar: if no layers defined and data is passed at the top
# level, create a scatterplot layer with the top-level data by default.
if data is not None:
spec['layers'].append({
'data': data,
'type': 'ScatterplotLayer',
})
for layer in spec['layers']:
# Don't add layers that have no data.
if 'data' not in layer:
continue
# Remove DataFrame because it's not JSON-serializable
data = layer.pop('data')
layer_proto = proto.layers.add()
fixed_layer = case_converters.convert_dict_keys(
case_converters.to_lower_camel_case, layer)
layer_proto.spec = json.dumps(fixed_layer)
# TODO: If several layers use the same data frame, the data gets resent
# for each layer. Need to improve this.
data_frame_proto.marshall_data_frame(data, layer_proto.data)
del spec['layers']
# Dump JSON after removing DataFrames (see loop above), because DataFrames
# are not JSON-serializable.
proto.spec = json.dumps(spec)
def marshall(proto, data=None, spec=None, use_container_width=False, **kwargs):
"""Construct a Vega-Lite chart object.
See DeltaGenerator.vega_lite_chart for docs.
"""
# Support passing data inside spec['datasets'] and spec['data'].
# (The data gets pulled out of the spec dict later on.)
if isinstance(data, dict) and spec is None:
spec = data
data = None
# Support passing no spec arg, but filling it with kwargs.
# Example:
# marshall(proto, baz='boz')
if spec is None:
spec = dict()
else:
# Clone the spec dict, since we may be mutating it.
spec = dict(spec)
# Support passing in kwargs. Example:
# marshall(proto, {foo: 'bar'}, baz='boz')
if len(kwargs):
# Merge spec with unflattened kwargs, where kwargs take precedence.
# This only works for string keys, but kwarg keys are strings anyways.
spec = dict(spec, **dicttools.unflatten(kwargs, _CHANNELS))
if len(spec) == 0:
raise ValueError("Vega-Lite charts require a non-empty spec dict.")
if "autosize" not in spec:
spec["autosize"] = {"type": "fit", "contains": "padding"}
# Pull data out of spec dict when it's in a 'dataset' key:
# marshall(proto, {datasets: {foo: df1, bar: df2}, ...})
if "datasets" in spec:
for k, v in spec["datasets"].items():
dataset = proto.datasets.add()
dataset.name = str(k)
dataset.has_name = True
data_frame_proto.marshall_data_frame(v, dataset.data)
del spec["datasets"]
# Pull data out of spec dict when it's in a top-level 'data' key:
# marshall(proto, {data: df})
# marshall(proto, {data: {values: df, ...}})
# marshall(proto, {data: {url: 'url'}})
# marshall(proto, {data: {name: 'foo'}})
if "data" in spec:
data_spec = spec["data"]
if isinstance(data_spec, dict):
if "values" in data_spec:
data = data_spec["values"]
del data_spec["values"]
else:
data = data_spec
del spec["data"]
proto.spec = json.dumps(spec)
proto.use_container_width = use_container_width
if data is not None:
data_frame_proto.marshall_data_frame(data, proto.data)
def marshall(proto, data=None, spec=None, width=0, **kwargs):
"""Construct a Vega-Lite chart object.
See DeltaGenerator.vega_lite_chart for docs.
"""
# Support passing data inside spec['datasets'] and spec['data'].
# (The data gets pulled out of the spec dict later on.)
if type(data) in dict_types and spec is None:
spec = data
data = None
# Support passing no spec arg, but filling it with kwargs.
# Example:
# marshall(proto, baz='boz')
if spec is None:
spec = dict()
else:
# Clone the spec dict, since we may be mutating it.
spec = dict(spec)
# Support passing in kwargs. Example:
# marshall(proto, {foo: 'bar'}, baz='boz')
if len(kwargs):
# Merge spec with unflattened kwargs, where kwargs take precedence.
# This only works for string keys, but kwarg keys are strings anyways.
spec = dict(spec, **dicttools.unflatten(kwargs, _CHANNELS))
if len(spec) == 0:
raise ValueError('Vega-Lite charts require a non-empty spec dict.')
# TODO: Improve autosizing code. It doesn't work with some kinds of charts,
# like composed charts, for example.
if width >= 0 and 'width' not in spec:
spec['width'] = width
if 'autosize' not in spec:
spec['autosize'] = {'type': 'fit', 'contains': 'padding'}
# Pull data out of spec dict when it's in a 'dataset' key:
# marshall(proto, {datasets: {foo: df1, bar: df2}, ...})
if 'datasets' in spec:
for k, v in spec['datasets'].items():
dataset = proto.datasets.add()
dataset.name = str(k)
dataset.has_name = True
data_frame_proto.marshall_data_frame(v, dataset.data)
del spec['datasets']
# Pull data out of spec dict when it's in a top-level 'data' key:
# marshall(proto, {data: df})
# marshall(proto, {data: {values: df, ...}})
# marshall(proto, {data: {url: 'url'}})
# marshall(proto, {data: {name: 'foo'}})
if 'data' in spec:
data_spec = spec['data']
if type(data_spec) in dict_types:
if 'values' in data_spec:
data = data_spec['values']
del data_spec['values']
else:
data = data_spec
del spec['data']
proto.spec = json.dumps(spec)
if data is not None:
data_frame_proto.marshall_data_frame(data, proto.data)
