def inv_eval(vtrace):
constraints = []
# frozen data used for removing duplicate points
frozen_data = []
for vt in vtrace:
# add fault tolerency: if the field is null, ignore it
if vt.x1 != None and vt.y1 != None:
# each end of an point will only be added once
p1 = json.dumps(
{
"c_x": vt.x1,
"c_y": vt.y1,
"c_size": vt.size,
"c_color": vt.color,
"c_column": vt.column
},
sort_keys=True)
if p1 not in frozen_data: frozen_data.append(p1)
if vt.x2 != None and vt.y2 != None:
p2 = json.dumps(
{
"c_x": vt.x2,
"c_y": vt.y2,
"c_size": vt.size,
"c_color": vt.color,
"c_column": vt.column
},
sort_keys=True)
if p2 not in frozen_data: frozen_data.append(p2)
# there should not be any points between these two
constraints.append("""
(not (exists (r Tuple) (and (> r.c_x vt.x1)
(< r.c_x vt.x2)
(= r.c_color vt.color)
(= r.c_column vt.column))))""")
data_values = [json.loads(r) for r in frozen_data]
unused_fields = remove_unused_fields(data_values)
encodings = []
for channel, enc_ty in [("x", "_"), ("y", "_"), ("size", "nominal"),
("color", "nominal"), ("column", "nominal")]:
field_name = "c_{}".format(channel)
if field_name in unused_fields:
continue
if channel in ["x", "y"]:
dtype = table_utils.infer_dtype(
[r[field_name] for r in data_values])
enc_ty = "nominal" if dtype == "string" else "quantitative"
encodings.append(Encoding(channel, field_name, enc_ty))
bar_chart = LineChart(encodings=encodings)
return [(SymTable(values=data_values,
constraints=constraints), bar_chart)]
def inv_eval(vtrace):
data_values = []
constraints = []
for vt in vtrace:
# min max will appear in the table
data_values.append({
"c_x": vt.x,
"c_y": vt.min,
"c_color": vt.color,
"c_column": vt.column
})
data_values.append({
"c_x": vt.x,
"c_y": vt.max,
"c_color": vt.color,
"c_column": vt.column
})
# the output table should satisfy these constraints
constraints.append(
"min([r.c_y for r in T if r.c_color == {} and r.c_column == {}]) = {}"
.format(vt.color, vt.column, vt.min))
constraints.append(
"max([r.c_y for r in T if r.c_color == {} and r.c_column == {}]) = {}"
.format(vt.color, vt.column, vt.max))
constraints.append(
"Q1([r.c_y for r in T if r.c_color == {} and r.c_column == {}]) = {}"
.format(vt.color, vt.column, vt.Q1))
constraints.append(
"Q3([r.c_y for r in T if r.c_color == {} and r.c_column == {}]) = {}"
.format(vt.color, vt.column, vt.Q3))
constraints.append(
"median([r.c_y for r in T if r.c_color == {} and r.c_column == {}]) = {}"
.format(vt.color, vt.column, vt.median))
# remove fields that contain none values
unused_fields = remove_unused_fields(data_values)
encodings = []
for channel, enc_ty in [("x", "_"), ("y", "_"), ("color", "nominal"),
("column", "nominal")]:
field_name = "c_{}".format(channel)
if field_name in unused_fields:
continue
if channel in ["x", "y"]:
# the type needs to be determined by datatype
dtype = table_utils.infer_dtype(
[r[field_name] for r in data_values])
enc_ty = "nominal" if dtype == "string" else "quantitative"
encodings.append(Encoding(channel, field_name, enc_ty))
chart = BoxPlot(encodings=encodings)
return [(SymTable(data_values, constraints), chart)]
def _update_encoding_type(layer_spec, layer_data):
"""use side effect to update encodings"""
mark = layer_spec["mark"]["type"] if isinstance(
layer_spec["mark"], (dict, )) else layer_spec["mark"]
if "color" in layer_spec["encoding"]:
field_data = set([
r[layer_spec["encoding"]["color"]["field"]] for r in layer_data
])
dtype = table_utils.infer_dtype(field_data)
enc_ty = "nominal" if dtype == "string" else "quantitative"
layer_spec["encoding"]["color"]["type"] = enc_ty
def inv_eval(vtrace):
frozen_data = []
for vt in vtrace:
# each end of an point will only be added once
p1 = json.dumps(
{
"c_x": vt.x1,
"c_y": vt.yt1,
"c_y2": vt.yb1,
"c_color": vt.color,
"c_column": vt.column
},
sort_keys=True)
p2 = json.dumps(
{
"c_x": vt.x2,
"c_y": vt.yt2,
"c_y2": vt.yb2,
"c_color": vt.color,
"c_column": vt.column
},
sort_keys=True)
if p1 not in frozen_data: frozen_data.append(p1)
if p2 not in frozen_data: frozen_data.append(p2)
data_values = [json.loads(r) for r in frozen_data]
channel_types = [("x", "_"), ("y", "quantitative"),
("y2", "quantitative"), ("color", "nominal"),
("column", "nominal")]
# remove fields that contain none values
unused_fields = remove_unused_fields(data_values)
encodings = []
for channel, enc_ty in channel_types:
field_name = "c_{}".format(channel)
if field_name in unused_fields:
continue
if channel == "x":
dtype = table_utils.infer_dtype(
[r[field_name] for r in data_values])
enc_ty = "nominal" if dtype == "string" else "quantitative"
encodings.append(Encoding(channel, field_name, enc_ty))
chart = AreaChart(encodings=encodings)
return [(SymTable(values=data_values), chart)]
def _update_encoding_type(layer_spec, layer_data): """use side effect to update encodings""" mark = layer_spec["mark"]["type"] if isinstance(layer_spec["mark"], (dict,)) else layer_spec["mark"] if "color" in layer_spec["encoding"]: field_data = set([r[layer_spec["encoding"]["color"]["field"]] for r in layer_data]) dtype = table_utils.infer_dtype(field_data) enc_ty = "nominal" if dtype == "string" else "quantitative" layer_spec["encoding"]["color"]["type"] = enc_ty if mark == "rect": for ch in layer_spec["encoding"]: if ch in ["x", "y"]: if layer_spec["encoding"][ch]["type"] != "nominal": layer_spec["encoding"][ch]["type"] = "nominal" for ch in layer_spec["encoding"]: if (ch == "x" or ch == "y") and mark == "rect": # rect won't support temporal continue field_data = set([r[layer_spec["encoding"][ch]["field"]] for r in layer_data]) if all([is_date(d) for d in field_data]): layer_spec["encoding"][ch]["type"] = "temporal"
def inv_eval(vtrace):
mark_ty = "rect" if vtrace[0].point_shape == "rect" else "point"
data_values = []
for vt in vtrace:
data_values.append({
"c_x": vt.x,
"c_y": vt.y,
"c_size": vt.size,
"c_color": vt.color,
"c_shape": vt.shape,
"c_column": vt.column
})
# remove fields that contain none values
unused_fields = remove_unused_fields(data_values)
encodings = []
for channel, enc_ty in [("x", "_"), ("y", "_"), ("size", "_"),
("color", "nominal"), ("shape", "nominal"),
("column", "nominal")]:
field_name = "c_{}".format(channel)
if field_name in unused_fields:
continue
if channel in ["x", "y", "size"] or (channel == "color"
and mark_ty == "rect"):
# the type needs to be determined by datatype
dtype = table_utils.infer_dtype(
[r[field_name] for r in data_values])
enc_ty = "nominal" if dtype == "string" else "quantitative"
encodings.append(Encoding(channel, field_name, enc_ty))
chart = ScatterPlot(mark_ty=mark_ty, encodings=encodings)
return [(SymTable(values=data_values), chart)]
