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_core.py
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import plotly.graph_objs as go
import plotly.io as pio
from collections import namedtuple, OrderedDict
from ._special_inputs import IdentityMap, Constant, Range
from .trendline_functions import ols, lowess, rolling, expanding, ewm
from _plotly_utils.basevalidators import ColorscaleValidator
from plotly.colors import qualitative, sequential
import math
from plotly._subplots import (
make_subplots,
_set_trace_grid_reference,
_subplot_type_for_trace_type,
)
import narwhals.stable.v1 as nw
# The reason to use narwhals.stable.v1 is to have a stable and perfectly
# backwards-compatible API, hence the confidence to not pin the Narwhals version exactly,
# allowing for multiple major libraries to have Narwhals as a dependency without
# forbidding users to install them all together due to dependency conflicts.
NO_COLOR = "px_no_color_constant"
trendline_functions = dict(
lowess=lowess, rolling=rolling, ewm=ewm, expanding=expanding, ols=ols
)
# Declare all supported attributes, across all plot types
direct_attrables = (
["base", "x", "y", "z", "a", "b", "c", "r", "theta", "size", "x_start", "x_end"]
+ ["hover_name", "text", "names", "values", "parents", "wide_cross"]
+ ["ids", "error_x", "error_x_minus", "error_y", "error_y_minus", "error_z"]
+ ["error_z_minus", "lat", "lon", "locations", "animation_group"]
)
array_attrables = ["dimensions", "custom_data", "hover_data", "path", "wide_variable"]
group_attrables = ["animation_frame", "facet_row", "facet_col", "line_group"]
renameable_group_attrables = [
"color", # renamed to marker.color or line.color in infer_config
"symbol", # renamed to marker.symbol in infer_config
"line_dash", # renamed to line.dash in infer_config
"pattern_shape", # renamed to marker.pattern.shape in infer_config
]
all_attrables = (
direct_attrables + array_attrables + group_attrables + renameable_group_attrables
)
cartesians = [go.Scatter, go.Scattergl, go.Bar, go.Funnel, go.Box, go.Violin]
cartesians += [go.Histogram, go.Histogram2d, go.Histogram2dContour]
class PxDefaults(object):
__slots__ = [
"template",
"width",
"height",
"color_discrete_sequence",
"color_discrete_map",
"color_continuous_scale",
"symbol_sequence",
"symbol_map",
"line_dash_sequence",
"line_dash_map",
"pattern_shape_sequence",
"pattern_shape_map",
"size_max",
"category_orders",
"labels",
]
def __init__(self):
self.reset()
def reset(self):
self.template = None
self.width = None
self.height = None
self.color_discrete_sequence = None
self.color_discrete_map = {}
self.color_continuous_scale = None
self.symbol_sequence = None
self.symbol_map = {}
self.line_dash_sequence = None
self.line_dash_map = {}
self.pattern_shape_sequence = None
self.pattern_shape_map = {}
self.size_max = 20
self.category_orders = {}
self.labels = {}
defaults = PxDefaults()
del PxDefaults
MAPBOX_TOKEN = None
def set_mapbox_access_token(token):
"""
Arguments:
token (Mapbox token): A Mapbox token to be used in `plotly.express.scatter_mapbox` \
and `plotly.express.line_mapbox` figures. See \
https://docs.mapbox.com/help/how-mapbox-works/access-tokens/ for more details.
"""
global MAPBOX_TOKEN
MAPBOX_TOKEN = token
def get_trendline_results(fig):
"""
Extracts fit statistics for trendlines (when applied to figures generated with
the `trendline` argument set to `"ols"`).
Arguments:
fig (figure): the output of a `plotly.express` charting call
Returns:
A `pandas.DataFrame` with a column "px_fit_results" containing the `statsmodels`
results objects, along with columns identifying the subset of the data the
trendline was fit on.
"""
return fig._px_trendlines
Mapping = namedtuple(
"Mapping",
[
"show_in_trace_name",
"grouper",
"val_map",
"sequence",
"updater",
"variable",
"facet",
],
)
TraceSpec = namedtuple("TraceSpec", ["constructor", "attrs", "trace_patch", "marginal"])
def get_label(args, column):
try:
return args["labels"][column]
except Exception:
return column
def invert_label(args, column):
"""Invert mapping.
Find key corresponding to value column in dict args["labels"].
Returns `column` if the value does not exist.
"""
reversed_labels = {value: key for (key, value) in args["labels"].items()}
try:
return reversed_labels[column]
except Exception:
return column
def _is_continuous(df: nw.DataFrame, col_name: str) -> bool:
if nw.dependencies.is_pandas_like_dataframe(df_native := df.to_native()):
# fastpath for pandas: Narwhals' Series.dtype has a bit of overhead, as it
# tries to distinguish between true "object" columns, and "string" columns
# disguised as "object". But here, we deal with neither.
return df_native[col_name].dtype.kind in "ifc"
return df.get_column(col_name).dtype.is_numeric()
def _to_unix_epoch_seconds(s: nw.Series) -> nw.Series:
dtype = s.dtype
if dtype == nw.Date:
return s.dt.timestamp("ms") / 1_000
if dtype == nw.Datetime:
if dtype.time_unit in ("s", "ms"):
return s.dt.timestamp("ms") / 1_000
elif dtype.time_unit == "us":
return s.dt.timestamp("us") / 1_000_000
elif dtype.time_unit == "ns":
return s.dt.timestamp("ns") / 1_000_000_000
else:
msg = "Unexpected dtype, please report a bug"
raise ValueError(msg)
else:
msg = f"Expected Date or Datetime, got {dtype}"
raise TypeError(msg)
def _generate_temporary_column_name(n_bytes, columns) -> str:
"""Wraps of Narwhals generate_temporary_column_name to generate a token
which is guaranteed to not be in columns, nor in [col + token for col in columns]
"""
counter = 0
while True:
# This is guaranteed to not be in columns by Narwhals
token = nw.generate_temporary_column_name(n_bytes, columns=columns)
# Now check that it is not in the [col + token for col in columns] list
if token not in {f"{c}{token}" for c in columns}:
return token
counter += 1
if counter > 100:
msg = (
"Internal Error: Plotly was not able to generate a column name with "
f"{n_bytes=} and not in {columns}.\n"
"Please report this to "
"https://github.com/plotly/plotly.py/issues/new and we will try to "
"replicate and fix it."
)
raise AssertionError(msg)
def get_decorated_label(args, column, role):
original_label = label = get_label(args, column)
if "histfunc" in args and (
(role == "z")
or (role == "x" and "orientation" in args and args["orientation"] == "h")
or (role == "y" and "orientation" in args and args["orientation"] == "v")
):
histfunc = args["histfunc"] or "count"
if histfunc != "count":
label = "%s of %s" % (histfunc, label)
else:
label = "count"
if "histnorm" in args and args["histnorm"] is not None:
if label == "count":
label = args["histnorm"]
else:
histnorm = args["histnorm"]
if histfunc == "sum":
if histnorm == "probability":
label = "%s of %s" % ("fraction", label)
elif histnorm == "percent":
label = "%s of %s" % (histnorm, label)
else:
label = "%s weighted by %s" % (histnorm, original_label)
elif histnorm == "probability":
label = "%s of sum of %s" % ("fraction", label)
elif histnorm == "percent":
label = "%s of sum of %s" % ("percent", label)
else:
label = "%s of %s" % (histnorm, label)
if "barnorm" in args and args["barnorm"] is not None:
label = "%s (normalized as %s)" % (label, args["barnorm"])
return label
def make_mapping(args, variable):
if variable == "line_group" or variable == "animation_frame":
return Mapping(
show_in_trace_name=False,
grouper=args[variable],
val_map={},
sequence=[""],
variable=variable,
updater=(lambda trace, v: v),
facet=None,
)
if variable == "facet_row" or variable == "facet_col":
letter = "x" if variable == "facet_col" else "y"
return Mapping(
show_in_trace_name=False,
variable=letter,
grouper=args[variable],
val_map={},
sequence=[i for i in range(1, 1000)],
updater=(lambda trace, v: v),
facet="row" if variable == "facet_row" else "col",
)
(parent, variable, *other_variables) = variable.split(".")
vprefix = variable
arg_name = variable
if variable == "color":
vprefix = "color_discrete"
if variable == "dash":
arg_name = "line_dash"
vprefix = "line_dash"
if variable in ["pattern", "shape"]:
arg_name = "pattern_shape"
vprefix = "pattern_shape"
if args[vprefix + "_map"] == "identity":
val_map = IdentityMap()
else:
val_map = args[vprefix + "_map"].copy()
return Mapping(
show_in_trace_name=True,
variable=variable,
grouper=args[arg_name],
val_map=val_map,
sequence=args[vprefix + "_sequence"],
updater=lambda trace, v: trace.update(
{parent: {".".join([variable] + other_variables): v}}
),
facet=None,
)
def make_trace_kwargs(args, trace_spec, trace_data, mapping_labels, sizeref):
"""Populates a dict with arguments to update trace
Parameters
----------
args : dict
args to be used for the trace
trace_spec : NamedTuple
which kind of trace to be used (has constructor, marginal etc.
attributes)
trace_data : pandas DataFrame
data
mapping_labels : dict
to be used for hovertemplate
sizeref : float
marker sizeref
Returns
-------
trace_patch : dict
dict to be used to update trace
fit_results : dict
fit information to be used for trendlines
"""
trace_data: nw.DataFrame
df: nw.DataFrame = args["data_frame"]
if "line_close" in args and args["line_close"]:
trace_data = nw.concat([trace_data, trace_data.head(1)], how="vertical")
trace_patch = trace_spec.trace_patch.copy() or {}
fit_results = None
hover_header = ""
for attr_name in trace_spec.attrs:
attr_value = args[attr_name]
attr_label = get_decorated_label(args, attr_value, attr_name)
if attr_name == "dimensions":
dims = [
(name, trace_data.get_column(name))
for name in trace_data.columns
if ((not attr_value) or (name in attr_value))
and (trace_spec.constructor != go.Parcoords or _is_continuous(df, name))
and (
trace_spec.constructor != go.Parcats
or (attr_value is not None and name in attr_value)
or nw.to_py_scalar(df.get_column(name).n_unique())
<= args["dimensions_max_cardinality"]
)
]
trace_patch["dimensions"] = [
dict(label=get_label(args, name), values=column)
for (name, column) in dims
]
if trace_spec.constructor == go.Splom:
for d in trace_patch["dimensions"]:
d["axis"] = dict(matches=True)
mapping_labels["%{xaxis.title.text}"] = "%{x}"
mapping_labels["%{yaxis.title.text}"] = "%{y}"
elif attr_value is not None:
if attr_name == "size":
if "marker" not in trace_patch:
trace_patch["marker"] = dict()
trace_patch["marker"]["size"] = trace_data.get_column(attr_value)
trace_patch["marker"]["sizemode"] = "area"
trace_patch["marker"]["sizeref"] = sizeref
mapping_labels[attr_label] = "%{marker.size}"
elif attr_name == "marginal_x":
if trace_spec.constructor == go.Histogram:
mapping_labels["count"] = "%{y}"
elif attr_name == "marginal_y":
if trace_spec.constructor == go.Histogram:
mapping_labels["count"] = "%{x}"
elif attr_name == "trendline":
if (
args["x"]
and args["y"]
and len(
trace_data.select(nw.col(args["x"], args["y"])).drop_nulls()
)
> 1
):
# sorting is bad but trace_specs with "trendline" have no other attrs
sorted_trace_data = trace_data.sort(by=args["x"], nulls_last=True)
y = sorted_trace_data.get_column(args["y"])
x = sorted_trace_data.get_column(args["x"])
if x.dtype == nw.Datetime or x.dtype == nw.Date:
# convert to unix epoch seconds
x = _to_unix_epoch_seconds(x)
elif not x.dtype.is_numeric():
try:
x = x.cast(nw.Float64())
except ValueError:
raise ValueError(
"Could not convert value of 'x' ('%s') into a numeric type. "
"If 'x' contains stringified dates, please convert to a datetime column."
% args["x"]
)
if not y.dtype.is_numeric():
try:
y = y.cast(nw.Float64())
except ValueError:
raise ValueError(
"Could not convert value of 'y' into a numeric type."
)
# preserve original values of "x" in case they're dates
# otherwise numpy/pandas can mess with the timezones
# NB this means trendline functions must output one-to-one with the input series
# i.e. we can't do resampling, because then the X values might not line up!
non_missing = ~(x.is_null() | y.is_null())
trace_patch["x"] = sorted_trace_data.filter(non_missing).get_column(
args["x"]
)
if (
trace_patch["x"].dtype == nw.Datetime
and trace_patch["x"].dtype.time_zone is not None
):
# Remove time zone so that local time is displayed
trace_patch["x"] = (
trace_patch["x"].dt.replace_time_zone(None).to_numpy()
)
else:
trace_patch["x"] = trace_patch["x"].to_numpy()
trendline_function = trendline_functions[attr_value]
y_out, hover_header, fit_results = trendline_function(
args["trendline_options"],
sorted_trace_data.get_column(args["x"]), # narwhals series
x.to_numpy(), # numpy array
y.to_numpy(), # numpy array
args["x"],
args["y"],
non_missing.to_numpy(), # numpy array
)
assert len(y_out) == len(trace_patch["x"]), (
"missing-data-handling failure in trendline code"
)
trace_patch["y"] = y_out
mapping_labels[get_label(args, args["x"])] = "%{x}"
mapping_labels[get_label(args, args["y"])] = "%{y} <b>(trend)</b>"
elif attr_name.startswith("error"):
error_xy = attr_name[:7]
arr = "arrayminus" if attr_name.endswith("minus") else "array"
if error_xy not in trace_patch:
trace_patch[error_xy] = {}
trace_patch[error_xy][arr] = trace_data.get_column(attr_value)
elif attr_name == "custom_data":
if len(attr_value) > 0:
# here we store a data frame in customdata, and it's serialized
# as a list of row lists, which is what we want
trace_patch["customdata"] = trace_data.select(nw.col(attr_value))
elif attr_name == "hover_name":
if trace_spec.constructor not in [
go.Histogram,
go.Histogram2d,
go.Histogram2dContour,
]:
trace_patch["hovertext"] = trace_data.get_column(attr_value)
if hover_header == "":
hover_header = "<b>%{hovertext}</b><br><br>"
elif attr_name == "hover_data":
if trace_spec.constructor not in [
go.Histogram,
go.Histogram2d,
go.Histogram2dContour,
]:
hover_is_dict = isinstance(attr_value, dict)
customdata_cols = args.get("custom_data") or []
for col in attr_value:
if hover_is_dict and not attr_value[col]:
continue
if col in [
args.get("x"),
args.get("y"),
args.get("z"),
args.get("base"),
]:
continue
try:
position = args["custom_data"].index(col)
except (ValueError, AttributeError, KeyError):
position = len(customdata_cols)
customdata_cols.append(col)
attr_label_col = get_decorated_label(args, col, None)
mapping_labels[attr_label_col] = "%%{customdata[%d]}" % (
position
)
if len(customdata_cols) > 0:
# here we store a data frame in customdata, and it's serialized
# as a list of row lists, which is what we want
# dict.fromkeys(customdata_cols) allows to deduplicate column
# names, yet maintaining the original order.
trace_patch["customdata"] = trace_data.select(
*[nw.col(c) for c in dict.fromkeys(customdata_cols)]
)
elif attr_name == "color":
if trace_spec.constructor in [
go.Choropleth,
go.Choroplethmap,
go.Choroplethmapbox,
]:
trace_patch["z"] = trace_data.get_column(attr_value)
trace_patch["coloraxis"] = "coloraxis1"
mapping_labels[attr_label] = "%{z}"
elif trace_spec.constructor in [
go.Sunburst,
go.Treemap,
go.Icicle,
go.Pie,
go.Funnelarea,
]:
if "marker" not in trace_patch:
trace_patch["marker"] = dict()
if args.get("color_is_continuous"):
trace_patch["marker"]["colors"] = trace_data.get_column(
attr_value
)
trace_patch["marker"]["coloraxis"] = "coloraxis1"
mapping_labels[attr_label] = "%{color}"
else:
trace_patch["marker"]["colors"] = []
if args["color_discrete_map"] is not None:
mapping = args["color_discrete_map"].copy()
else:
mapping = {}
for cat in trace_data.get_column(attr_value).to_list():
# although trace_data.get_column(attr_value) is a Narwhals
# Series, which is an iterable, explicitly calling a to_list()
# makes sure that the elements we loop over are python objects
# in all cases, since depending on the backend this may not be
# the case (e.g. PyArrow)
if mapping.get(cat) is None:
mapping[cat] = args["color_discrete_sequence"][
len(mapping) % len(args["color_discrete_sequence"])
]
trace_patch["marker"]["colors"].append(mapping[cat])
else:
colorable = "marker"
if trace_spec.constructor in [go.Parcats, go.Parcoords]:
colorable = "line"
if colorable not in trace_patch:
trace_patch[colorable] = dict()
trace_patch[colorable]["color"] = trace_data.get_column(attr_value)
trace_patch[colorable]["coloraxis"] = "coloraxis1"
mapping_labels[attr_label] = "%%{%s.color}" % colorable
elif attr_name == "animation_group":
trace_patch["ids"] = trace_data.get_column(attr_value)
elif attr_name == "locations":
trace_patch[attr_name] = trace_data.get_column(attr_value)
mapping_labels[attr_label] = "%{location}"
elif attr_name == "values":
trace_patch[attr_name] = trace_data.get_column(attr_value)
_label = "value" if attr_label == "values" else attr_label
mapping_labels[_label] = "%{value}"
elif attr_name == "parents":
trace_patch[attr_name] = trace_data.get_column(attr_value)
_label = "parent" if attr_label == "parents" else attr_label
mapping_labels[_label] = "%{parent}"
elif attr_name == "ids":
trace_patch[attr_name] = trace_data.get_column(attr_value)
_label = "id" if attr_label == "ids" else attr_label
mapping_labels[_label] = "%{id}"
elif attr_name == "names":
if trace_spec.constructor in [
go.Sunburst,
go.Treemap,
go.Icicle,
go.Pie,
go.Funnelarea,
]:
trace_patch["labels"] = trace_data.get_column(attr_value)
_label = "label" if attr_label == "names" else attr_label
mapping_labels[_label] = "%{label}"
else:
trace_patch[attr_name] = trace_data.get_column(attr_value)
else:
trace_patch[attr_name] = trace_data.get_column(attr_value)
mapping_labels[attr_label] = "%%{%s}" % attr_name
elif (trace_spec.constructor == go.Histogram and attr_name in ["x", "y"]) or (
trace_spec.constructor in [go.Histogram2d, go.Histogram2dContour]
and attr_name == "z"
):
# ensure that stuff like "count" gets into the hoverlabel
mapping_labels[attr_label] = "%%{%s}" % attr_name
if trace_spec.constructor not in [go.Parcoords, go.Parcats]:
# Modify mapping_labels according to hover_data keys
# if hover_data is a dict
mapping_labels_copy = OrderedDict(mapping_labels)
if args["hover_data"] and isinstance(args["hover_data"], dict):
for k, v in mapping_labels.items():
# We need to invert the mapping here
k_args = invert_label(args, k)
if k_args in args["hover_data"]:
formatter = args["hover_data"][k_args][0]
if formatter:
if isinstance(formatter, str):
mapping_labels_copy[k] = v.replace("}", "%s}" % formatter)
else:
_ = mapping_labels_copy.pop(k)
hover_lines = [k + "=" + v for k, v in mapping_labels_copy.items()]
trace_patch["hovertemplate"] = hover_header + "<br>".join(hover_lines)
trace_patch["hovertemplate"] += "<extra></extra>"
return trace_patch, fit_results
def configure_axes(args, constructor, fig, orders):
configurators = {
go.Scatter3d: configure_3d_axes,
go.Scatterternary: configure_ternary_axes,
go.Scatterpolar: configure_polar_axes,
go.Scatterpolargl: configure_polar_axes,
go.Barpolar: configure_polar_axes,
go.Scattermap: configure_map,
go.Choroplethmap: configure_map,
go.Densitymap: configure_map,
go.Scattermapbox: configure_mapbox,
go.Choroplethmapbox: configure_mapbox,
go.Densitymapbox: configure_mapbox,
go.Scattergeo: configure_geo,
go.Choropleth: configure_geo,
}
for c in cartesians:
configurators[c] = configure_cartesian_axes
if constructor in configurators:
configurators[constructor](args, fig, orders)
def set_cartesian_axis_opts(args, axis, letter, orders):
log_key = "log_" + letter
range_key = "range_" + letter
if log_key in args and args[log_key]:
axis["type"] = "log"
if range_key in args and args[range_key]:
axis["range"] = [math.log(r, 10) for r in args[range_key]]
elif range_key in args and args[range_key]:
axis["range"] = args[range_key]
if args[letter] in orders:
axis["categoryorder"] = "array"
axis["categoryarray"] = (
orders[args[letter]]
if isinstance(axis, go.layout.XAxis)
else list(reversed(orders[args[letter]])) # top down for Y axis
)
def configure_cartesian_marginal_axes(args, fig, orders):
nrows = len(fig._grid_ref)
ncols = len(fig._grid_ref[0])
# Set y-axis titles and axis options in the left-most column
for yaxis in fig.select_yaxes(col=1):
set_cartesian_axis_opts(args, yaxis, "y", orders)
# Set x-axis titles and axis options in the bottom-most row
for xaxis in fig.select_xaxes(row=1):
set_cartesian_axis_opts(args, xaxis, "x", orders)
# Configure axis ticks on marginal subplots
if args["marginal_x"]:
fig.update_yaxes(
showticklabels=False, showline=False, ticks="", range=None, row=nrows
)
if args["template"].layout.yaxis.showgrid is None:
fig.update_yaxes(showgrid=args["marginal_x"] == "histogram", row=nrows)
if args["template"].layout.xaxis.showgrid is None:
fig.update_xaxes(showgrid=True, row=nrows)
if args["marginal_y"]:
fig.update_xaxes(
showticklabels=False, showline=False, ticks="", range=None, col=ncols
)
if args["template"].layout.xaxis.showgrid is None:
fig.update_xaxes(showgrid=args["marginal_y"] == "histogram", col=ncols)
if args["template"].layout.yaxis.showgrid is None:
fig.update_yaxes(showgrid=True, col=ncols)
# Add axis titles to non-marginal subplots
y_title = get_decorated_label(args, args["y"], "y")
if args["marginal_x"]:
fig.update_yaxes(title_text=y_title, row=1, col=1)
else:
for row in range(1, nrows + 1):
fig.update_yaxes(title_text=y_title, row=row, col=1)
x_title = get_decorated_label(args, args["x"], "x")
if args["marginal_y"]:
fig.update_xaxes(title_text=x_title, row=1, col=1)
else:
for col in range(1, ncols + 1):
fig.update_xaxes(title_text=x_title, row=1, col=col)
# Configure axis type across all x-axes
if "log_x" in args and args["log_x"]:
fig.update_xaxes(type="log")
# Configure axis type across all y-axes
if "log_y" in args and args["log_y"]:
fig.update_yaxes(type="log")
# Configure matching and axis type for marginal y-axes
matches_y = "y" + str(ncols + 1)
if args["marginal_x"]:
for row in range(2, nrows + 1, 2):
fig.update_yaxes(matches=matches_y, type=None, row=row)
if args["marginal_y"]:
for col in range(2, ncols + 1, 2):
fig.update_xaxes(matches="x2", type=None, col=col)
def configure_cartesian_axes(args, fig, orders):
if ("marginal_x" in args and args["marginal_x"]) or (
"marginal_y" in args and args["marginal_y"]
):
configure_cartesian_marginal_axes(args, fig, orders)
return
# Set y-axis titles and axis options in the left-most column
y_title = get_decorated_label(args, args["y"], "y")
for yaxis in fig.select_yaxes(col=1):
yaxis.update(title_text=y_title)
set_cartesian_axis_opts(args, yaxis, "y", orders)
# Set x-axis titles and axis options in the bottom-most row
x_title = get_decorated_label(args, args["x"], "x")
for xaxis in fig.select_xaxes(row=1):
if "is_timeline" not in args:
xaxis.update(title_text=x_title)
set_cartesian_axis_opts(args, xaxis, "x", orders)
# Configure axis type across all x-axes
if "log_x" in args and args["log_x"]:
fig.update_xaxes(type="log")
# Configure axis type across all y-axes
if "log_y" in args and args["log_y"]:
fig.update_yaxes(type="log")
if "is_timeline" in args:
fig.update_xaxes(type="date")
if "ecdfmode" in args:
if args["orientation"] == "v":
fig.update_yaxes(rangemode="tozero")
else:
fig.update_xaxes(rangemode="tozero")
def configure_ternary_axes(args, fig, orders):
fig.update_ternaries(
aaxis=dict(title_text=get_label(args, args["a"])),
baxis=dict(title_text=get_label(args, args["b"])),
caxis=dict(title_text=get_label(args, args["c"])),
)
def configure_polar_axes(args, fig, orders):
patch = dict(
angularaxis=dict(direction=args["direction"], rotation=args["start_angle"]),
radialaxis=dict(),
)
for var, axis in [("r", "radialaxis"), ("theta", "angularaxis")]:
if args[var] in orders:
patch[axis]["categoryorder"] = "array"
patch[axis]["categoryarray"] = orders[args[var]]
radialaxis = patch["radialaxis"]
if args["log_r"]:
radialaxis["type"] = "log"
if args["range_r"]:
radialaxis["range"] = [math.log(x, 10) for x in args["range_r"]]
else:
if args["range_r"]:
radialaxis["range"] = args["range_r"]
if args["range_theta"]:
patch["sector"] = args["range_theta"]
fig.update_polars(patch)
def configure_3d_axes(args, fig, orders):
patch = dict(
xaxis=dict(title_text=get_label(args, args["x"])),
yaxis=dict(title_text=get_label(args, args["y"])),
zaxis=dict(title_text=get_label(args, args["z"])),
)
for letter in ["x", "y", "z"]:
axis = patch[letter + "axis"]
if args["log_" + letter]:
axis["type"] = "log"
if args["range_" + letter]:
axis["range"] = [math.log(x, 10) for x in args["range_" + letter]]
else:
if args["range_" + letter]:
axis["range"] = args["range_" + letter]
if args[letter] in orders:
axis["categoryorder"] = "array"
axis["categoryarray"] = orders[args[letter]]
fig.update_scenes(patch)
def configure_mapbox(args, fig, orders):
center = args["center"]
if not center and "lat" in args and "lon" in args:
center = dict(
lat=args["data_frame"][args["lat"]].mean(),
lon=args["data_frame"][args["lon"]].mean(),
)
fig.update_mapboxes(
accesstoken=MAPBOX_TOKEN,
center=center,
zoom=args["zoom"],
style=args["mapbox_style"],
)
def configure_map(args, fig, orders):
center = args["center"]
if not center and "lat" in args and "lon" in args:
center = dict(
lat=args["data_frame"][args["lat"]].mean(),
lon=args["data_frame"][args["lon"]].mean(),
)
fig.update_maps(
center=center,
zoom=args["zoom"],
style=args["map_style"],
)
def configure_geo(args, fig, orders):
fig.update_geos(
center=args["center"],
scope=args["scope"],
fitbounds=args["fitbounds"],
visible=args["basemap_visible"],
projection=dict(type=args["projection"]),
)
def configure_animation_controls(args, constructor, fig):
def frame_args(duration):
return {
"frame": {"duration": duration, "redraw": constructor != go.Scatter},
"mode": "immediate",
"fromcurrent": True,
"transition": {"duration": duration, "easing": "linear"},
}
if "animation_frame" in args and args["animation_frame"] and len(fig.frames) > 1:
fig.layout.updatemenus = [
{
"buttons": [
{
"args": [None, frame_args(500)],
"label": "▶",
"method": "animate",
},
{
"args": [[None], frame_args(0)],
"label": "◼",
"method": "animate",
},
],
"direction": "left",
"pad": {"r": 10, "t": 70},
"showactive": False,
"type": "buttons",
"x": 0.1,
"xanchor": "right",
"y": 0,
"yanchor": "top",
}
]
fig.layout.sliders = [
{
"active": 0,
"yanchor": "top",
"xanchor": "left",
"currentvalue": {
"prefix": get_label(args, args["animation_frame"]) + "="
},
"pad": {"b": 10, "t": 60},
"len": 0.9,
"x": 0.1,
"y": 0,
"steps": [
{
"args": [[f.name], frame_args(0)],
"label": f.name,
"method": "animate",
}
for f in fig.frames
],
}
]
def make_trace_spec(args, constructor, attrs, trace_patch):
if constructor in [go.Scatter, go.Scatterpolar]:
if "render_mode" in args and (
args["render_mode"] == "webgl"
or (
args["render_mode"] == "auto"
and len(args["data_frame"]) > 1000
and args.get("line_shape") != "spline"
and args["animation_frame"] is None
)
):
if constructor == go.Scatter:
constructor = go.Scattergl
if "orientation" in trace_patch:
del trace_patch["orientation"]
else:
constructor = go.Scatterpolargl
# Create base trace specification
result = [TraceSpec(constructor, attrs, trace_patch, None)]
# Add marginal trace specifications
for letter in ["x", "y"]:
if "marginal_" + letter in args and args["marginal_" + letter]:
trace_spec = None
axis_map = dict(
xaxis="x1" if letter == "x" else "x2",
yaxis="y1" if letter == "y" else "y2",
)
if args["marginal_" + letter] == "histogram":
trace_spec = TraceSpec(
constructor=go.Histogram,
attrs=[letter, "marginal_" + letter],
trace_patch=dict(opacity=0.5, bingroup=letter, **axis_map),
marginal=letter,
)
elif args["marginal_" + letter] == "violin":
trace_spec = TraceSpec(
constructor=go.Violin,
attrs=[letter, "hover_name", "hover_data"],
trace_patch=dict(scalegroup=letter),
marginal=letter,
)
elif args["marginal_" + letter] == "box":
trace_spec = TraceSpec(
constructor=go.Box,
attrs=[letter, "hover_name", "hover_data"],
trace_patch=dict(notched=True),
marginal=letter,
)
elif args["marginal_" + letter] == "rug":
symbols = {"x": "line-ns-open", "y": "line-ew-open"}
trace_spec = TraceSpec(
constructor=go.Box,
attrs=[letter, "hover_name", "hover_data"],
trace_patch=dict(
fillcolor="rgba(255,255,255,0)",
line={"color": "rgba(255,255,255,0)"},
boxpoints="all",
jitter=0,
hoveron="points",
marker={"symbol": symbols[letter]},
),
marginal=letter,
)
if "color" in attrs or "color" not in args:
if "marker" not in trace_spec.trace_patch:
trace_spec.trace_patch["marker"] = dict()
first_default_color = args["color_continuous_scale"][0]
trace_spec.trace_patch["marker"]["color"] = first_default_color
result.append(trace_spec)
# Add trendline trace specifications
if args.get("trendline") and args.get("trendline_scope", "trace") == "trace":
result.append(make_trendline_spec(args, constructor))
return result
def make_trendline_spec(args, constructor):
trace_spec = TraceSpec(
constructor=(
go.Scattergl
if constructor == go.Scattergl # could be contour
else go.Scatter
),
attrs=["trendline"],
trace_patch=dict(mode="lines"),
marginal=None,
)
if args["trendline_color_override"]:
trace_spec.trace_patch["line"] = dict(color=args["trendline_color_override"])
return trace_spec