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stringlengths 1
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stringlengths 16
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stringlengths 298
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stringlengths 336
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seaborn | 0 | seaborn/_core/scales.py | def label(
self,
formatter: Formatter | None = None, *,
like: str | Callable | None = None,
base: int | None | Default = default,
unit: str | None = None,
) -> Continuous:
"""
Configure the appearance of tick labels for the scale's axis or legend.
Parameters
----------
formatter : :class:`matplotlib.ticker.Formatter` subclass
Pre-configured formatter to use; other parameters will be ignored.
like : str or callable
Either a format pattern (e.g., `".2f"`), a format string with fields named
`x` and/or `pos` (e.g., `"${x:.2f}"`), or a callable with a signature like
`f(x: float, pos: int) -> str`. In the latter variants, `x` is passed as the
tick value and `pos` is passed as the tick index.
base : number
Use log formatter (with scientific notation) having this value as the base.
Set to `None` to override the default formatter with a log transform.
unit : str or (str, str) tuple
Use SI prefixes with these units (e.g., with `unit="g"`, a tick value
of 5000 will appear as `5 kg`). When a tuple, the first element gives the
separator between the number and unit.
Returns
-------
scale
Copy of self with new label configuration.
"""
| /usr/src/app/target_test_cases/failed_tests_Continuous.label.txt | def label(
self,
formatter: Formatter | None = None, *,
like: str | Callable | None = None,
base: int | None | Default = default,
unit: str | None = None,
) -> Continuous:
"""
Configure the appearance of tick labels for the scale's axis or legend.
Parameters
----------
formatter : :class:`matplotlib.ticker.Formatter` subclass
Pre-configured formatter to use; other parameters will be ignored.
like : str or callable
Either a format pattern (e.g., `".2f"`), a format string with fields named
`x` and/or `pos` (e.g., `"${x:.2f}"`), or a callable with a signature like
`f(x: float, pos: int) -> str`. In the latter variants, `x` is passed as the
tick value and `pos` is passed as the tick index.
base : number
Use log formatter (with scientific notation) having this value as the base.
Set to `None` to override the default formatter with a log transform.
unit : str or (str, str) tuple
Use SI prefixes with these units (e.g., with `unit="g"`, a tick value
of 5000 will appear as `5 kg`). When a tuple, the first element gives the
separator between the number and unit.
Returns
-------
scale
Copy of self with new label configuration.
"""
# Input checks
if formatter is not None and not isinstance(formatter, Formatter):
raise TypeError(
f"Label formatter must be an instance of {Formatter!r}, "
f"not {type(formatter)!r}"
)
if like is not None and not (isinstance(like, str) or callable(like)):
msg = f"`like` must be a string or callable, not {type(like).__name__}."
raise TypeError(msg)
new = copy(self)
new._label_params = {
"formatter": formatter,
"like": like,
"base": base,
"unit": unit,
}
return new
| Continuous.label |
seaborn | 1 | seaborn/_core/plot.py | def add(
self,
mark: Mark,
*transforms: Stat | Move,
orient: str | None = None,
legend: bool = True,
label: str | None = None,
data: DataSource = None,
**variables: VariableSpec,
) -> Plot:
"""
Specify a layer of the visualization in terms of mark and data transform(s).
This is the main method for specifying how the data should be visualized.
It can be called multiple times with different arguments to define
a plot with multiple layers.
Parameters
----------
mark : :class:`Mark`
The visual representation of the data to use in this layer.
transforms : :class:`Stat` or :class:`Move`
Objects representing transforms to be applied before plotting the data.
Currently, at most one :class:`Stat` can be used, and it
must be passed first. This constraint will be relaxed in the future.
orient : "x", "y", "v", or "h"
The orientation of the mark, which also affects how transforms are computed.
Typically corresponds to the axis that defines groups for aggregation.
The "v" (vertical) and "h" (horizontal) options are synonyms for "x" / "y",
but may be more intuitive with some marks. When not provided, an
orientation will be inferred from characteristics of the data and scales.
legend : bool
Option to suppress the mark/mappings for this layer from the legend.
label : str
A label to use for the layer in the legend, independent of any mappings.
data : DataFrame or dict
Data source to override the global source provided in the constructor.
variables : data vectors or identifiers
Additional layer-specific variables, including variables that will be
passed directly to the transforms without scaling.
Examples
--------
.. include:: ../docstrings/objects.Plot.add.rst
"""
| /usr/src/app/target_test_cases/failed_tests_Plot.add.txt | def add(
self,
mark: Mark,
*transforms: Stat | Move,
orient: str | None = None,
legend: bool = True,
label: str | None = None,
data: DataSource = None,
**variables: VariableSpec,
) -> Plot:
"""
Specify a layer of the visualization in terms of mark and data transform(s).
This is the main method for specifying how the data should be visualized.
It can be called multiple times with different arguments to define
a plot with multiple layers.
Parameters
----------
mark : :class:`Mark`
The visual representation of the data to use in this layer.
transforms : :class:`Stat` or :class:`Move`
Objects representing transforms to be applied before plotting the data.
Currently, at most one :class:`Stat` can be used, and it
must be passed first. This constraint will be relaxed in the future.
orient : "x", "y", "v", or "h"
The orientation of the mark, which also affects how transforms are computed.
Typically corresponds to the axis that defines groups for aggregation.
The "v" (vertical) and "h" (horizontal) options are synonyms for "x" / "y",
but may be more intuitive with some marks. When not provided, an
orientation will be inferred from characteristics of the data and scales.
legend : bool
Option to suppress the mark/mappings for this layer from the legend.
label : str
A label to use for the layer in the legend, independent of any mappings.
data : DataFrame or dict
Data source to override the global source provided in the constructor.
variables : data vectors or identifiers
Additional layer-specific variables, including variables that will be
passed directly to the transforms without scaling.
Examples
--------
.. include:: ../docstrings/objects.Plot.add.rst
"""
if not isinstance(mark, Mark):
msg = f"mark must be a Mark instance, not {type(mark)!r}."
raise TypeError(msg)
# TODO This API for transforms was a late decision, and previously Plot.add
# accepted 0 or 1 Stat instances and 0, 1, or a list of Move instances.
# It will take some work to refactor the internals so that Stat and Move are
# treated identically, and until then well need to "unpack" the transforms
# here and enforce limitations on the order / types.
stat: Optional[Stat]
move: Optional[List[Move]]
error = False
if not transforms:
stat, move = None, None
elif isinstance(transforms[0], Stat):
stat = transforms[0]
move = [m for m in transforms[1:] if isinstance(m, Move)]
error = len(move) != len(transforms) - 1
else:
stat = None
move = [m for m in transforms if isinstance(m, Move)]
error = len(move) != len(transforms)
if error:
msg = " ".join([
"Transforms must have at most one Stat type (in the first position),",
"and all others must be a Move type. Given transform type(s):",
", ".join(str(type(t).__name__) for t in transforms) + "."
])
raise TypeError(msg)
new = self._clone()
new._layers.append({
"mark": mark,
"stat": stat,
"move": move,
# TODO it doesn't work to supply scalars to variables, but it should
"vars": variables,
"source": data,
"legend": legend,
"label": label,
"orient": {"v": "x", "h": "y"}.get(orient, orient), # type: ignore
})
return new
| Plot.add |
seaborn | 2 | seaborn/_core/plot.py | def facet(
self,
col: VariableSpec = None,
row: VariableSpec = None,
order: OrderSpec | dict[str, OrderSpec] = None,
wrap: int | None = None,
) -> Plot:
"""
Produce subplots with conditional subsets of the data.
Parameters
----------
col, row : data vectors or identifiers
Variables used to define subsets along the columns and/or rows of the grid.
Can be references to the global data source passed in the constructor.
order : list of strings, or dict with dimensional keys
Define the order of the faceting variables.
wrap : int
When using only `col` or `row`, wrap subplots across a two-dimensional
grid with this many subplots on the faceting dimension.
Examples
--------
.. include:: ../docstrings/objects.Plot.facet.rst
"""
| /usr/src/app/target_test_cases/failed_tests_Plot.facet.txt | def facet(
self,
col: VariableSpec = None,
row: VariableSpec = None,
order: OrderSpec | dict[str, OrderSpec] = None,
wrap: int | None = None,
) -> Plot:
"""
Produce subplots with conditional subsets of the data.
Parameters
----------
col, row : data vectors or identifiers
Variables used to define subsets along the columns and/or rows of the grid.
Can be references to the global data source passed in the constructor.
order : list of strings, or dict with dimensional keys
Define the order of the faceting variables.
wrap : int
When using only `col` or `row`, wrap subplots across a two-dimensional
grid with this many subplots on the faceting dimension.
Examples
--------
.. include:: ../docstrings/objects.Plot.facet.rst
"""
variables: dict[str, VariableSpec] = {}
if col is not None:
variables["col"] = col
if row is not None:
variables["row"] = row
structure = {}
if isinstance(order, dict):
for dim in ["col", "row"]:
dim_order = order.get(dim)
if dim_order is not None:
structure[dim] = list(dim_order)
elif order is not None:
if col is not None and row is not None:
err = " ".join([
"When faceting on both col= and row=, passing `order` as a list"
"is ambiguous. Use a dict with 'col' and/or 'row' keys instead."
])
raise RuntimeError(err)
elif col is not None:
structure["col"] = list(order)
elif row is not None:
structure["row"] = list(order)
spec: FacetSpec = {
"variables": variables,
"structure": structure,
"wrap": wrap,
}
new = self._clone()
new._facet_spec.update(spec)
return new
| Plot.facet |
seaborn | 3 | seaborn/_core/plot.py | def on(self, target: Axes | SubFigure | Figure) -> Plot:
"""
Provide existing Matplotlib figure or axes for drawing the plot.
When using this method, you will also need to explicitly call a method that
triggers compilation, such as :meth:`Plot.show` or :meth:`Plot.save`. If you
want to postprocess using matplotlib, you'd need to call :meth:`Plot.plot`
first to compile the plot without rendering it.
Parameters
----------
target : Axes, SubFigure, or Figure
Matplotlib object to use. Passing :class:`matplotlib.axes.Axes` will add
artists without otherwise modifying the figure. Otherwise, subplots will be
created within the space of the given :class:`matplotlib.figure.Figure` or
:class:`matplotlib.figure.SubFigure`.
Examples
--------
.. include:: ../docstrings/objects.Plot.on.rst
"""
| /usr/src/app/target_test_cases/failed_tests_Plot.on.txt | def on(self, target: Axes | SubFigure | Figure) -> Plot:
"""
Provide existing Matplotlib figure or axes for drawing the plot.
When using this method, you will also need to explicitly call a method that
triggers compilation, such as :meth:`Plot.show` or :meth:`Plot.save`. If you
want to postprocess using matplotlib, you'd need to call :meth:`Plot.plot`
first to compile the plot without rendering it.
Parameters
----------
target : Axes, SubFigure, or Figure
Matplotlib object to use. Passing :class:`matplotlib.axes.Axes` will add
artists without otherwise modifying the figure. Otherwise, subplots will be
created within the space of the given :class:`matplotlib.figure.Figure` or
:class:`matplotlib.figure.SubFigure`.
Examples
--------
.. include:: ../docstrings/objects.Plot.on.rst
"""
accepted_types: tuple # Allow tuple of various length
accepted_types = (
mpl.axes.Axes, mpl.figure.SubFigure, mpl.figure.Figure
)
accepted_types_str = (
f"{mpl.axes.Axes}, {mpl.figure.SubFigure}, or {mpl.figure.Figure}"
)
if not isinstance(target, accepted_types):
err = (
f"The `Plot.on` target must be an instance of {accepted_types_str}. "
f"You passed an instance of {target.__class__} instead."
)
raise TypeError(err)
new = self._clone()
new._target = target
return new
| Plot.on |
seaborn | 4 | seaborn/_core/plot.py | def pair(
self,
x: VariableSpecList = None,
y: VariableSpecList = None,
wrap: int | None = None,
cross: bool = True,
) -> Plot:
"""
Produce subplots by pairing multiple `x` and/or `y` variables.
Parameters
----------
x, y : sequence(s) of data vectors or identifiers
Variables that will define the grid of subplots.
wrap : int
When using only `x` or `y`, "wrap" subplots across a two-dimensional grid
with this many columns (when using `x`) or rows (when using `y`).
cross : bool
When False, zip the `x` and `y` lists such that the first subplot gets the
first pair, the second gets the second pair, etc. Otherwise, create a
two-dimensional grid from the cartesian product of the lists.
Examples
--------
.. include:: ../docstrings/objects.Plot.pair.rst
"""
| /usr/src/app/target_test_cases/failed_tests_Plot.pair.txt | def pair(
self,
x: VariableSpecList = None,
y: VariableSpecList = None,
wrap: int | None = None,
cross: bool = True,
) -> Plot:
"""
Produce subplots by pairing multiple `x` and/or `y` variables.
Parameters
----------
x, y : sequence(s) of data vectors or identifiers
Variables that will define the grid of subplots.
wrap : int
When using only `x` or `y`, "wrap" subplots across a two-dimensional grid
with this many columns (when using `x`) or rows (when using `y`).
cross : bool
When False, zip the `x` and `y` lists such that the first subplot gets the
first pair, the second gets the second pair, etc. Otherwise, create a
two-dimensional grid from the cartesian product of the lists.
Examples
--------
.. include:: ../docstrings/objects.Plot.pair.rst
"""
# TODO Add transpose= arg, which would then draw pair(y=[...]) across rows
# This may also be possible by setting `wrap=1`, but is that too unobvious?
# TODO PairGrid features not currently implemented: diagonals, corner
pair_spec: PairSpec = {}
axes = {"x": [] if x is None else x, "y": [] if y is None else y}
for axis, arg in axes.items():
if isinstance(arg, (str, int)):
err = f"You must pass a sequence of variable keys to `{axis}`"
raise TypeError(err)
pair_spec["variables"] = {}
pair_spec["structure"] = {}
for axis in "xy":
keys = []
for i, col in enumerate(axes[axis]):
key = f"{axis}{i}"
keys.append(key)
pair_spec["variables"][key] = col
if keys:
pair_spec["structure"][axis] = keys
if not cross and len(axes["x"]) != len(axes["y"]):
err = "Lengths of the `x` and `y` lists must match with cross=False"
raise ValueError(err)
pair_spec["cross"] = cross
pair_spec["wrap"] = wrap
new = self._clone()
new._pair_spec.update(pair_spec)
return new
| Plot.pair |
seaborn | 5 | seaborn/_base.py | def _attach(
self,
obj,
allowed_types=None,
log_scale=None,
):
"""Associate the plotter with an Axes manager and initialize its units.
Parameters
----------
obj : :class:`matplotlib.axes.Axes` or :class:'FacetGrid`
Structural object that we will eventually plot onto.
allowed_types : str or list of str
If provided, raise when either the x or y variable does not have
one of the declared seaborn types.
log_scale : bool, number, or pair of bools or numbers
If not False, set the axes to use log scaling, with the given
base or defaulting to 10. If a tuple, interpreted as separate
arguments for the x and y axes.
"""
| /usr/src/app/target_test_cases/failed_tests__base.VectorPlotter._attach.txt | def _attach(
self,
obj,
allowed_types=None,
log_scale=None,
):
"""Associate the plotter with an Axes manager and initialize its units.
Parameters
----------
obj : :class:`matplotlib.axes.Axes` or :class:'FacetGrid`
Structural object that we will eventually plot onto.
allowed_types : str or list of str
If provided, raise when either the x or y variable does not have
one of the declared seaborn types.
log_scale : bool, number, or pair of bools or numbers
If not False, set the axes to use log scaling, with the given
base or defaulting to 10. If a tuple, interpreted as separate
arguments for the x and y axes.
"""
from .axisgrid import FacetGrid
if isinstance(obj, FacetGrid):
self.ax = None
self.facets = obj
ax_list = obj.axes.flatten()
if obj.col_names is not None:
self.var_levels["col"] = obj.col_names
if obj.row_names is not None:
self.var_levels["row"] = obj.row_names
else:
self.ax = obj
self.facets = None
ax_list = [obj]
# Identify which "axis" variables we have defined
axis_variables = set("xy").intersection(self.variables)
# -- Verify the types of our x and y variables here.
# This doesn't really make complete sense being here here, but it's a fine
# place for it, given the current system.
# (Note that for some plots, there might be more complicated restrictions)
# e.g. the categorical plots have their own check that as specific to the
# non-categorical axis.
if allowed_types is None:
allowed_types = ["numeric", "datetime", "categorical"]
elif isinstance(allowed_types, str):
allowed_types = [allowed_types]
for var in axis_variables:
var_type = self.var_types[var]
if var_type not in allowed_types:
err = (
f"The {var} variable is {var_type}, but one of "
f"{allowed_types} is required"
)
raise TypeError(err)
# -- Get axis objects for each row in plot_data for type conversions and scaling
facet_dim = {"x": "col", "y": "row"}
self.converters = {}
for var in axis_variables:
other_var = {"x": "y", "y": "x"}[var]
converter = pd.Series(index=self.plot_data.index, name=var, dtype=object)
share_state = getattr(self.facets, f"_share{var}", True)
# Simplest cases are that we have a single axes, all axes are shared,
# or sharing is only on the orthogonal facet dimension. In these cases,
# all datapoints get converted the same way, so use the first axis
if share_state is True or share_state == facet_dim[other_var]:
converter.loc[:] = getattr(ax_list[0], f"{var}axis")
else:
# Next simplest case is when no axes are shared, and we can
# use the axis objects within each facet
if share_state is False:
for axes_vars, axes_data in self.iter_data():
ax = self._get_axes(axes_vars)
converter.loc[axes_data.index] = getattr(ax, f"{var}axis")
# In the more complicated case, the axes are shared within each
# "file" of the facetgrid. In that case, we need to subset the data
# for that file and assign it the first axis in the slice of the grid
else:
names = getattr(self.facets, f"{share_state}_names")
for i, level in enumerate(names):
idx = (i, 0) if share_state == "row" else (0, i)
axis = getattr(self.facets.axes[idx], f"{var}axis")
converter.loc[self.plot_data[share_state] == level] = axis
# Store the converter vector, which we use elsewhere (e.g comp_data)
self.converters[var] = converter
# Now actually update the matplotlib objects to do the conversion we want
grouped = self.plot_data[var].groupby(self.converters[var], sort=False)
for converter, seed_data in grouped:
if self.var_types[var] == "categorical":
if self._var_ordered[var]:
order = self.var_levels[var]
else:
order = None
seed_data = categorical_order(seed_data, order)
converter.update_units(seed_data)
# -- Set numerical axis scales
# First unpack the log_scale argument
if log_scale is None:
scalex = scaley = False
else:
# Allow single value or x, y tuple
try:
scalex, scaley = log_scale
except TypeError:
scalex = log_scale if self.var_types.get("x") == "numeric" else False
scaley = log_scale if self.var_types.get("y") == "numeric" else False
# Now use it
for axis, scale in zip("xy", (scalex, scaley)):
if scale:
for ax in ax_list:
set_scale = getattr(ax, f"set_{axis}scale")
if scale is True:
set_scale("log", nonpositive="mask")
else:
set_scale("log", base=scale, nonpositive="mask")
# For categorical y, we want the "first" level to be at the top of the axis
if self.var_types.get("y", None) == "categorical":
for ax in ax_list:
ax.yaxis.set_inverted(True)
# TODO -- Add axes labels
| VectorPlotter._attach |
seaborn | 6 | seaborn/_base.py | def iter_data(
self, grouping_vars=None, *,
reverse=False, from_comp_data=False,
by_facet=True, allow_empty=False, dropna=True,
):
"""Generator for getting subsets of data defined by semantic variables.
Also injects "col" and "row" into grouping semantics.
Parameters
----------
grouping_vars : string or list of strings
Semantic variables that define the subsets of data.
reverse : bool
If True, reverse the order of iteration.
from_comp_data : bool
If True, use self.comp_data rather than self.plot_data
by_facet : bool
If True, add faceting variables to the set of grouping variables.
allow_empty : bool
If True, yield an empty dataframe when no observations exist for
combinations of grouping variables.
dropna : bool
If True, remove rows with missing data.
Yields
------
sub_vars : dict
Keys are semantic names, values are the level of that semantic.
sub_data : :class:`pandas.DataFrame`
Subset of ``plot_data`` for this combination of semantic values.
"""
| /usr/src/app/target_test_cases/failed_tests_VectorPlotter.iter_data.txt | def iter_data(
self, grouping_vars=None, *,
reverse=False, from_comp_data=False,
by_facet=True, allow_empty=False, dropna=True,
):
"""Generator for getting subsets of data defined by semantic variables.
Also injects "col" and "row" into grouping semantics.
Parameters
----------
grouping_vars : string or list of strings
Semantic variables that define the subsets of data.
reverse : bool
If True, reverse the order of iteration.
from_comp_data : bool
If True, use self.comp_data rather than self.plot_data
by_facet : bool
If True, add faceting variables to the set of grouping variables.
allow_empty : bool
If True, yield an empty dataframe when no observations exist for
combinations of grouping variables.
dropna : bool
If True, remove rows with missing data.
Yields
------
sub_vars : dict
Keys are semantic names, values are the level of that semantic.
sub_data : :class:`pandas.DataFrame`
Subset of ``plot_data`` for this combination of semantic values.
"""
# TODO should this default to using all (non x/y?) semantics?
# or define grouping vars somewhere?
if grouping_vars is None:
grouping_vars = []
elif isinstance(grouping_vars, str):
grouping_vars = [grouping_vars]
elif isinstance(grouping_vars, tuple):
grouping_vars = list(grouping_vars)
# Always insert faceting variables
if by_facet:
facet_vars = {"col", "row"}
grouping_vars.extend(
facet_vars & set(self.variables) - set(grouping_vars)
)
# Reduce to the semantics used in this plot
grouping_vars = [var for var in grouping_vars if var in self.variables]
if from_comp_data:
data = self.comp_data
else:
data = self.plot_data
if dropna:
data = data.dropna()
levels = self.var_levels.copy()
if from_comp_data:
for axis in {"x", "y"} & set(grouping_vars):
converter = self.converters[axis].iloc[0]
if self.var_types[axis] == "categorical":
if self._var_ordered[axis]:
# If the axis is ordered, then the axes in a possible
# facet grid are by definition "shared", or there is a
# single axis with a unique cat -> idx mapping.
# So we can just take the first converter object.
levels[axis] = converter.convert_units(levels[axis])
else:
# Otherwise, the mappings may not be unique, but we can
# use the unique set of index values in comp_data.
levels[axis] = np.sort(data[axis].unique())
else:
transform = converter.get_transform().transform
levels[axis] = transform(converter.convert_units(levels[axis]))
if grouping_vars:
grouped_data = data.groupby(
grouping_vars, sort=False, as_index=False, observed=False,
)
grouping_keys = []
for var in grouping_vars:
key = levels.get(var)
grouping_keys.append([] if key is None else key)
iter_keys = itertools.product(*grouping_keys)
if reverse:
iter_keys = reversed(list(iter_keys))
for key in iter_keys:
pd_key = (
key[0] if len(key) == 1 and _version_predates(pd, "2.2.0") else key
)
try:
data_subset = grouped_data.get_group(pd_key)
except KeyError:
# XXX we are adding this to allow backwards compatibility
# with the empty artists that old categorical plots would
# add (before 0.12), which we may decide to break, in which
# case this option could be removed
data_subset = data.loc[[]]
if data_subset.empty and not allow_empty:
continue
sub_vars = dict(zip(grouping_vars, key))
yield sub_vars, data_subset.copy()
else:
yield {}, data.copy()
| VectorPlotter.iter_data |
seaborn | 7 | seaborn/_base.py | def scale_categorical(self, axis, order=None, formatter=None):
"""
Enforce categorical (fixed-scale) rules for the data on given axis.
Parameters
----------
axis : "x" or "y"
Axis of the plot to operate on.
order : list
Order that unique values should appear in.
formatter : callable
Function mapping values to a string representation.
Returns
-------
self
"""
| /usr/src/app/target_test_cases/failed_tests__base.VectorPlotter.scale_categorical.txt | def scale_categorical(self, axis, order=None, formatter=None):
"""
Enforce categorical (fixed-scale) rules for the data on given axis.
Parameters
----------
axis : "x" or "y"
Axis of the plot to operate on.
order : list
Order that unique values should appear in.
formatter : callable
Function mapping values to a string representation.
Returns
-------
self
"""
# This method both modifies the internal representation of the data
# (converting it to string) and sets some attributes on self. It might be
# a good idea to have a separate object attached to self that contains the
# information in those attributes (i.e. whether to enforce variable order
# across facets, the order to use) similar to the SemanticMapping objects
# we have for semantic variables. That object could also hold the converter
# objects that get used, if we can decouple those from an existing axis
# (cf. https://github.com/matplotlib/matplotlib/issues/19229).
# There are some interactions with faceting information that would need
# to be thought through, since the converts to use depend on facets.
# If we go that route, these methods could become "borrowed" methods similar
# to what happens with the alternate semantic mapper constructors, although
# that approach is kind of fussy and confusing.
# TODO this method could also set the grid state? Since we like to have no
# grid on the categorical axis by default. Again, a case where we'll need to
# store information until we use it, so best to have a way to collect the
# attributes that this method sets.
# TODO if we are going to set visual properties of the axes with these methods,
# then we could do the steps currently in CategoricalPlotter._adjust_cat_axis
# TODO another, and distinct idea, is to expose a cut= param here
_check_argument("axis", ["x", "y"], axis)
# Categorical plots can be "univariate" in which case they get an anonymous
# category label on the opposite axis.
if axis not in self.variables:
self.variables[axis] = None
self.var_types[axis] = "categorical"
self.plot_data[axis] = ""
# If the "categorical" variable has a numeric type, sort the rows so that
# the default result from categorical_order has those values sorted after
# they have been coerced to strings. The reason for this is so that later
# we can get facet-wise orders that are correct.
# XXX Should this also sort datetimes?
# It feels more consistent, but technically will be a default change
# If so, should also change categorical_order to behave that way
if self.var_types[axis] == "numeric":
self.plot_data = self.plot_data.sort_values(axis, kind="mergesort")
# Now get a reference to the categorical data vector and remove na values
cat_data = self.plot_data[axis].dropna()
# Get the initial categorical order, which we do before string
# conversion to respect the original types of the order list.
# Track whether the order is given explicitly so that we can know
# whether or not to use the order constructed here downstream
self._var_ordered[axis] = order is not None or cat_data.dtype.name == "category"
order = pd.Index(categorical_order(cat_data, order), name=axis)
# Then convert data to strings. This is because in matplotlib,
# "categorical" data really mean "string" data, so doing this artists
# will be drawn on the categorical axis with a fixed scale.
# TODO implement formatter here; check that it returns strings?
if formatter is not None:
cat_data = cat_data.map(formatter)
order = order.map(formatter)
else:
cat_data = cat_data.astype(str)
order = order.astype(str)
# Update the levels list with the type-converted order variable
self.var_levels[axis] = order
# Now ensure that seaborn will use categorical rules internally
self.var_types[axis] = "categorical"
# Put the string-typed categorical vector back into the plot_data structure
self.plot_data[axis] = cat_data
return self
| VectorPlotter.scale_categorical |
seaborn | 8 | seaborn/_base.py | def categorical_order(vector, order=None):
"""Return a list of unique data values.
Determine an ordered list of levels in ``values``.
Parameters
----------
vector : list, array, Categorical, or Series
Vector of "categorical" values
order : list-like, optional
Desired order of category levels to override the order determined
from the ``values`` object.
Returns
-------
order : list
Ordered list of category levels not including null values.
"""
| /usr/src/app/target_test_cases/failed_tests__base.categorical_order.txt | def categorical_order(vector, order=None):
"""Return a list of unique data values.
Determine an ordered list of levels in ``values``.
Parameters
----------
vector : list, array, Categorical, or Series
Vector of "categorical" values
order : list-like, optional
Desired order of category levels to override the order determined
from the ``values`` object.
Returns
-------
order : list
Ordered list of category levels not including null values.
"""
if order is None:
if hasattr(vector, "categories"):
order = vector.categories
else:
try:
order = vector.cat.categories
except (TypeError, AttributeError):
order = pd.Series(vector).unique()
if variable_type(vector) == "numeric":
order = np.sort(order)
order = filter(pd.notnull, order)
return list(order)
| _base.categorical_order |
seaborn | 9 | seaborn/_base.py | def infer_orient(x=None, y=None, orient=None, require_numeric=True):
"""Determine how the plot should be oriented based on the data.
For historical reasons, the convention is to call a plot "horizontally"
or "vertically" oriented based on the axis representing its dependent
variable. Practically, this is used when determining the axis for
numerical aggregation.
Parameters
----------
x, y : Vector data or None
Positional data vectors for the plot.
orient : string or None
Specified orientation. If not None, can be "x" or "y", or otherwise
must start with "v" or "h".
require_numeric : bool
If set, raise when the implied dependent variable is not numeric.
Returns
-------
orient : "x" or "y"
Raises
------
ValueError: When `orient` is an unknown string.
TypeError: When dependent variable is not numeric, with `require_numeric`
"""
| /usr/src/app/target_test_cases/failed_tests_infer_orient.txt | def infer_orient(x=None, y=None, orient=None, require_numeric=True):
"""Determine how the plot should be oriented based on the data.
For historical reasons, the convention is to call a plot "horizontally"
or "vertically" oriented based on the axis representing its dependent
variable. Practically, this is used when determining the axis for
numerical aggregation.
Parameters
----------
x, y : Vector data or None
Positional data vectors for the plot.
orient : string or None
Specified orientation. If not None, can be "x" or "y", or otherwise
must start with "v" or "h".
require_numeric : bool
If set, raise when the implied dependent variable is not numeric.
Returns
-------
orient : "x" or "y"
Raises
------
ValueError: When `orient` is an unknown string.
TypeError: When dependent variable is not numeric, with `require_numeric`
"""
x_type = None if x is None else variable_type(x)
y_type = None if y is None else variable_type(y)
nonnumeric_dv_error = "{} orientation requires numeric `{}` variable."
single_var_warning = "{} orientation ignored with only `{}` specified."
if x is None:
if str(orient).startswith("h"):
warnings.warn(single_var_warning.format("Horizontal", "y"))
if require_numeric and y_type != "numeric":
raise TypeError(nonnumeric_dv_error.format("Vertical", "y"))
return "x"
elif y is None:
if str(orient).startswith("v"):
warnings.warn(single_var_warning.format("Vertical", "x"))
if require_numeric and x_type != "numeric":
raise TypeError(nonnumeric_dv_error.format("Horizontal", "x"))
return "y"
elif str(orient).startswith("v") or orient == "x":
if require_numeric and y_type != "numeric":
raise TypeError(nonnumeric_dv_error.format("Vertical", "y"))
return "x"
elif str(orient).startswith("h") or orient == "y":
if require_numeric and x_type != "numeric":
raise TypeError(nonnumeric_dv_error.format("Horizontal", "x"))
return "y"
elif orient is not None:
err = (
"`orient` must start with 'v' or 'h' or be None, "
f"but `{repr(orient)}` was passed."
)
raise ValueError(err)
elif x_type != "categorical" and y_type == "categorical":
return "y"
elif x_type != "numeric" and y_type == "numeric":
return "x"
elif x_type == "numeric" and y_type != "numeric":
return "y"
elif require_numeric and "numeric" not in (x_type, y_type):
err = "Neither the `x` nor `y` variable appears to be numeric."
raise TypeError(err)
else:
return "x"
| _base.infer_orient |
seaborn | 10 | seaborn/_base.py | def unique_dashes(n):
"""Build an arbitrarily long list of unique dash styles for lines.
Parameters
----------
n : int
Number of unique dash specs to generate.
Returns
-------
dashes : list of strings or tuples
Valid arguments for the ``dashes`` parameter on
:class:`matplotlib.lines.Line2D`. The first spec is a solid
line (``""``), the remainder are sequences of long and short
dashes.
"""
| /usr/src/app/target_test_cases/failed_tests__base.unique_dashes.txt | def unique_dashes(n):
"""Build an arbitrarily long list of unique dash styles for lines.
Parameters
----------
n : int
Number of unique dash specs to generate.
Returns
-------
dashes : list of strings or tuples
Valid arguments for the ``dashes`` parameter on
:class:`matplotlib.lines.Line2D`. The first spec is a solid
line (``""``), the remainder are sequences of long and short
dashes.
"""
# Start with dash specs that are well distinguishable
dashes = [
"",
(4, 1.5),
(1, 1),
(3, 1.25, 1.5, 1.25),
(5, 1, 1, 1),
]
# Now programmatically build as many as we need
p = 3
while len(dashes) < n:
# Take combinations of long and short dashes
a = itertools.combinations_with_replacement([3, 1.25], p)
b = itertools.combinations_with_replacement([4, 1], p)
# Interleave the combinations, reversing one of the streams
segment_list = itertools.chain(*zip(
list(a)[1:-1][::-1],
list(b)[1:-1]
))
# Now insert the gaps
for segments in segment_list:
gap = min(segments)
spec = tuple(itertools.chain(*((seg, gap) for seg in segments)))
dashes.append(spec)
p += 1
return dashes[:n]
| _base.unique_dashes |
seaborn | 11 | seaborn/_base.py | def unique_markers(n):
"""Build an arbitrarily long list of unique marker styles for points.
Parameters
----------
n : int
Number of unique marker specs to generate.
Returns
-------
markers : list of string or tuples
Values for defining :class:`matplotlib.markers.MarkerStyle` objects.
All markers will be filled.
"""
| /usr/src/app/target_test_cases/failed_tests_unique_markers.txt | def unique_markers(n):
"""Build an arbitrarily long list of unique marker styles for points.
Parameters
----------
n : int
Number of unique marker specs to generate.
Returns
-------
markers : list of string or tuples
Values for defining :class:`matplotlib.markers.MarkerStyle` objects.
All markers will be filled.
"""
# Start with marker specs that are well distinguishable
markers = [
"o",
"X",
(4, 0, 45),
"P",
(4, 0, 0),
(4, 1, 0),
"^",
(4, 1, 45),
"v",
]
# Now generate more from regular polygons of increasing order
s = 5
while len(markers) < n:
a = 360 / (s + 1) / 2
markers.extend([
(s + 1, 1, a),
(s + 1, 0, a),
(s, 1, 0),
(s, 0, 0),
])
s += 1
# Convert to MarkerStyle object, using only exactly what we need
# markers = [mpl.markers.MarkerStyle(m) for m in markers[:n]]
return markers[:n]
| _base.unique_markers |
seaborn | 12 | seaborn/_base.py | def variable_type(vector, boolean_type="numeric"):
"""
Determine whether a vector contains numeric, categorical, or datetime data.
This function differs from the pandas typing API in two ways:
- Python sequences or object-typed PyData objects are considered numeric if
all of their entries are numeric.
- String or mixed-type data are considered categorical even if not
explicitly represented as a :class:`pandas.api.types.CategoricalDtype`.
Parameters
----------
vector : :func:`pandas.Series`, :func:`numpy.ndarray`, or Python sequence
Input data to test.
boolean_type : 'numeric' or 'categorical'
Type to use for vectors containing only 0s and 1s (and NAs).
Returns
-------
var_type : 'numeric', 'categorical', or 'datetime'
Name identifying the type of data in the vector.
"""
| /usr/src/app/target_test_cases/failed_tests_variable_type.txt | def variable_type(vector, boolean_type="numeric"):
"""
Determine whether a vector contains numeric, categorical, or datetime data.
This function differs from the pandas typing API in two ways:
- Python sequences or object-typed PyData objects are considered numeric if
all of their entries are numeric.
- String or mixed-type data are considered categorical even if not
explicitly represented as a :class:`pandas.api.types.CategoricalDtype`.
Parameters
----------
vector : :func:`pandas.Series`, :func:`numpy.ndarray`, or Python sequence
Input data to test.
boolean_type : 'numeric' or 'categorical'
Type to use for vectors containing only 0s and 1s (and NAs).
Returns
-------
var_type : 'numeric', 'categorical', or 'datetime'
Name identifying the type of data in the vector.
"""
vector = pd.Series(vector)
# If a categorical dtype is set, infer categorical
if isinstance(vector.dtype, pd.CategoricalDtype):
return VariableType("categorical")
# Special-case all-na data, which is always "numeric"
if pd.isna(vector).all():
return VariableType("numeric")
# At this point, drop nans to simplify further type inference
vector = vector.dropna()
# Special-case binary/boolean data, allow caller to determine
# This triggers a numpy warning when vector has strings/objects
# https://github.com/numpy/numpy/issues/6784
# Because we reduce with .all(), we are agnostic about whether the
# comparison returns a scalar or vector, so we will ignore the warning.
# It triggers a separate DeprecationWarning when the vector has datetimes:
# https://github.com/numpy/numpy/issues/13548
# This is considered a bug by numpy and will likely go away.
with warnings.catch_warnings():
warnings.simplefilter(
action='ignore', category=(FutureWarning, DeprecationWarning)
)
try:
if np.isin(vector, [0, 1]).all():
return VariableType(boolean_type)
except TypeError:
# .isin comparison is not guaranteed to be possible under NumPy
# casting rules, depending on the (unknown) dtype of 'vector'
pass
# Defer to positive pandas tests
if pd.api.types.is_numeric_dtype(vector):
return VariableType("numeric")
if pd.api.types.is_datetime64_dtype(vector):
return VariableType("datetime")
# --- If we get to here, we need to check the entries
# Check for a collection where everything is a number
def all_numeric(x):
for x_i in x:
if not isinstance(x_i, Number):
return False
return True
if all_numeric(vector):
return VariableType("numeric")
# Check for a collection where everything is a datetime
def all_datetime(x):
for x_i in x:
if not isinstance(x_i, (datetime, np.datetime64)):
return False
return True
if all_datetime(vector):
return VariableType("datetime")
# Otherwise, our final fallback is to consider things categorical
return VariableType("categorical")
| _base.variable_type |
seaborn | 13 | seaborn/_statistics.py | def __init__(self, k_depth, outlier_prop, trust_alpha):
"""
Compute percentiles of a distribution using various tail stopping rules.
Parameters
----------
k_depth: "tukey", "proportion", "trustworthy", or "full"
Stopping rule for choosing tail percentiled to show:
- tukey: Show a similar number of outliers as in a conventional boxplot.
- proportion: Show approximately `outlier_prop` outliers.
- trust_alpha: Use `trust_alpha` level for most extreme tail percentile.
outlier_prop: float
Parameter for `k_depth="proportion"` setting the expected outlier rate.
trust_alpha: float
Parameter for `k_depth="trustworthy"` setting the confidence threshold.
Notes
-----
Based on the proposal in this paper:
https://vita.had.co.nz/papers/letter-value-plot.pdf
"""
| /usr/src/app/target_test_cases/failed_tests__statistics.LetterValues.__init__.txt | def __init__(self, k_depth, outlier_prop, trust_alpha):
"""
Compute percentiles of a distribution using various tail stopping rules.
Parameters
----------
k_depth: "tukey", "proportion", "trustworthy", or "full"
Stopping rule for choosing tail percentiled to show:
- tukey: Show a similar number of outliers as in a conventional boxplot.
- proportion: Show approximately `outlier_prop` outliers.
- trust_alpha: Use `trust_alpha` level for most extreme tail percentile.
outlier_prop: float
Parameter for `k_depth="proportion"` setting the expected outlier rate.
trust_alpha: float
Parameter for `k_depth="trustworthy"` setting the confidence threshold.
Notes
-----
Based on the proposal in this paper:
https://vita.had.co.nz/papers/letter-value-plot.pdf
"""
k_options = ["tukey", "proportion", "trustworthy", "full"]
if isinstance(k_depth, str):
_check_argument("k_depth", k_options, k_depth)
elif not isinstance(k_depth, int):
err = (
"The `k_depth` parameter must be either an integer or string "
f"(one of {k_options}), not {k_depth!r}."
)
raise TypeError(err)
self.k_depth = k_depth
self.outlier_prop = outlier_prop
self.trust_alpha = trust_alpha
| _statistics.LetterValues.__init__ |
seaborn | 14 | seaborn/_statistics.py | def __init__(self, estimator, errorbar=None, **boot_kws):
"""
Data aggregator that produces a weighted estimate and error bar interval.
Parameters
----------
estimator : string
Function (or method name) that maps a vector to a scalar. Currently
supports only "mean".
errorbar : string or (string, number) tuple
Name of errorbar method or a tuple with a method name and a level parameter.
Currently the only supported method is "ci".
boot_kws
Additional keywords are passed to bootstrap when error_method is "ci".
"""
| /usr/src/app/target_test_cases/failed_tests__statistics.WeightedAggregator.__init__.txt | def __init__(self, estimator, errorbar=None, **boot_kws):
"""
Data aggregator that produces a weighted estimate and error bar interval.
Parameters
----------
estimator : string
Function (or method name) that maps a vector to a scalar. Currently
supports only "mean".
errorbar : string or (string, number) tuple
Name of errorbar method or a tuple with a method name and a level parameter.
Currently the only supported method is "ci".
boot_kws
Additional keywords are passed to bootstrap when error_method is "ci".
"""
if estimator != "mean":
# Note that, while other weighted estimators may make sense (e.g. median),
# I'm not aware of an implementation in our dependencies. We can add one
# in seaborn later, if there is sufficient interest. For now, limit to mean.
raise ValueError(f"Weighted estimator must be 'mean', not {estimator!r}.")
self.estimator = estimator
method, level = _validate_errorbar_arg(errorbar)
if method is not None and method != "ci":
# As with the estimator, weighted 'sd' or 'pi' error bars may make sense.
# But we'll keep things simple for now and limit to (bootstrap) CI.
raise ValueError(f"Error bar method must be 'ci', not {method!r}.")
self.error_method = method
self.error_level = level
self.boot_kws = boot_kws
| _statistics.WeightedAggregator.__init__ |
seaborn | 15 | seaborn/algorithms.py | def bootstrap(*args, **kwargs):
"""Resample one or more arrays with replacement and store aggregate values.
Positional arguments are a sequence of arrays to bootstrap along the first
axis and pass to a summary function.
Keyword arguments:
n_boot : int, default=10000
Number of iterations
axis : int, default=None
Will pass axis to ``func`` as a keyword argument.
units : array, default=None
Array of sampling unit IDs. When used the bootstrap resamples units
and then observations within units instead of individual
datapoints.
func : string or callable, default="mean"
Function to call on the args that are passed in. If string, uses as
name of function in the numpy namespace. If nans are present in the
data, will try to use nan-aware version of named function.
seed : Generator | SeedSequence | RandomState | int | None
Seed for the random number generator; useful if you want
reproducible resamples.
Returns
-------
boot_dist: array
array of bootstrapped statistic values
"""
| /usr/src/app/target_test_cases/failed_tests_algorithms.bootstrap.txt | def bootstrap(*args, **kwargs):
"""Resample one or more arrays with replacement and store aggregate values.
Positional arguments are a sequence of arrays to bootstrap along the first
axis and pass to a summary function.
Keyword arguments:
n_boot : int, default=10000
Number of iterations
axis : int, default=None
Will pass axis to ``func`` as a keyword argument.
units : array, default=None
Array of sampling unit IDs. When used the bootstrap resamples units
and then observations within units instead of individual
datapoints.
func : string or callable, default="mean"
Function to call on the args that are passed in. If string, uses as
name of function in the numpy namespace. If nans are present in the
data, will try to use nan-aware version of named function.
seed : Generator | SeedSequence | RandomState | int | None
Seed for the random number generator; useful if you want
reproducible resamples.
Returns
-------
boot_dist: array
array of bootstrapped statistic values
"""
# Ensure list of arrays are same length
if len(np.unique(list(map(len, args)))) > 1:
raise ValueError("All input arrays must have the same length")
n = len(args[0])
# Default keyword arguments
n_boot = kwargs.get("n_boot", 10000)
func = kwargs.get("func", "mean")
axis = kwargs.get("axis", None)
units = kwargs.get("units", None)
random_seed = kwargs.get("random_seed", None)
if random_seed is not None:
msg = "`random_seed` has been renamed to `seed` and will be removed"
warnings.warn(msg)
seed = kwargs.get("seed", random_seed)
if axis is None:
func_kwargs = dict()
else:
func_kwargs = dict(axis=axis)
# Initialize the resampler
if isinstance(seed, np.random.RandomState):
rng = seed
else:
rng = np.random.default_rng(seed)
# Coerce to arrays
args = list(map(np.asarray, args))
if units is not None:
units = np.asarray(units)
if isinstance(func, str):
# Allow named numpy functions
f = getattr(np, func)
# Try to use nan-aware version of function if necessary
missing_data = np.isnan(np.sum(np.column_stack(args)))
if missing_data and not func.startswith("nan"):
nanf = getattr(np, f"nan{func}", None)
if nanf is None:
msg = f"Data contain nans but no nan-aware version of `{func}` found"
warnings.warn(msg, UserWarning)
else:
f = nanf
else:
f = func
# Handle numpy changes
try:
integers = rng.integers
except AttributeError:
integers = rng.randint
# Do the bootstrap
if units is not None:
return _structured_bootstrap(args, n_boot, units, f,
func_kwargs, integers)
boot_dist = []
for i in range(int(n_boot)):
resampler = integers(0, n, n, dtype=np.intp) # intp is indexing dtype
sample = [a.take(resampler, axis=0) for a in args]
boot_dist.append(f(*sample, **func_kwargs))
return np.array(boot_dist)
| algorithms.bootstrap |
seaborn | 16 | seaborn/axisgrid.py | def facet_data(self):
"""Generator for name indices and data subsets for each facet.
Yields
------
(i, j, k), data_ijk : tuple of ints, DataFrame
The ints provide an index into the {row, col, hue}_names attribute,
and the dataframe contains a subset of the full data corresponding
to each facet. The generator yields subsets that correspond with
the self.axes.flat iterator, or self.axes[i, j] when `col_wrap`
is None.
"""
| /usr/src/app/target_test_cases/failed_tests_axisgrid.FacetGrid.facet_data.txt | def facet_data(self):
"""Generator for name indices and data subsets for each facet.
Yields
------
(i, j, k), data_ijk : tuple of ints, DataFrame
The ints provide an index into the {row, col, hue}_names attribute,
and the dataframe contains a subset of the full data corresponding
to each facet. The generator yields subsets that correspond with
the self.axes.flat iterator, or self.axes[i, j] when `col_wrap`
is None.
"""
data = self.data
# Construct masks for the row variable
if self.row_names:
row_masks = [data[self._row_var] == n for n in self.row_names]
else:
row_masks = [np.repeat(True, len(self.data))]
# Construct masks for the column variable
if self.col_names:
col_masks = [data[self._col_var] == n for n in self.col_names]
else:
col_masks = [np.repeat(True, len(self.data))]
# Construct masks for the hue variable
if self.hue_names:
hue_masks = [data[self._hue_var] == n for n in self.hue_names]
else:
hue_masks = [np.repeat(True, len(self.data))]
# Here is the main generator loop
for (i, row), (j, col), (k, hue) in product(enumerate(row_masks),
enumerate(col_masks),
enumerate(hue_masks)):
data_ijk = data[row & col & hue & self._not_na]
yield (i, j, k), data_ijk
| axisgrid.FacetGrid.facet_data |
seaborn | 17 | seaborn/axisgrid.py | def map(self, func, *args, **kwargs):
"""Apply a plotting function to each facet's subset of the data.
Parameters
----------
func : callable
A plotting function that takes data and keyword arguments. It
must plot to the currently active matplotlib Axes and take a
`color` keyword argument. If faceting on the `hue` dimension,
it must also take a `label` keyword argument.
args : strings
Column names in self.data that identify variables with data to
plot. The data for each variable is passed to `func` in the
order the variables are specified in the call.
kwargs : keyword arguments
All keyword arguments are passed to the plotting function.
Returns
-------
self : object
Returns self.
"""
| /usr/src/app/target_test_cases/failed_tests_axisgrid.FacetGrid.map.txt | def map(self, func, *args, **kwargs):
"""Apply a plotting function to each facet's subset of the data.
Parameters
----------
func : callable
A plotting function that takes data and keyword arguments. It
must plot to the currently active matplotlib Axes and take a
`color` keyword argument. If faceting on the `hue` dimension,
it must also take a `label` keyword argument.
args : strings
Column names in self.data that identify variables with data to
plot. The data for each variable is passed to `func` in the
order the variables are specified in the call.
kwargs : keyword arguments
All keyword arguments are passed to the plotting function.
Returns
-------
self : object
Returns self.
"""
# If color was a keyword argument, grab it here
kw_color = kwargs.pop("color", None)
# How we use the function depends on where it comes from
func_module = str(getattr(func, "__module__", ""))
# Check for categorical plots without order information
if func_module == "seaborn.categorical":
if "order" not in kwargs:
warning = ("Using the {} function without specifying "
"`order` is likely to produce an incorrect "
"plot.".format(func.__name__))
warnings.warn(warning)
if len(args) == 3 and "hue_order" not in kwargs:
warning = ("Using the {} function without specifying "
"`hue_order` is likely to produce an incorrect "
"plot.".format(func.__name__))
warnings.warn(warning)
# Iterate over the data subsets
for (row_i, col_j, hue_k), data_ijk in self.facet_data():
# If this subset is null, move on
if not data_ijk.values.size:
continue
# Get the current axis
modify_state = not func_module.startswith("seaborn")
ax = self.facet_axis(row_i, col_j, modify_state)
# Decide what color to plot with
kwargs["color"] = self._facet_color(hue_k, kw_color)
# Insert the other hue aesthetics if appropriate
for kw, val_list in self.hue_kws.items():
kwargs[kw] = val_list[hue_k]
# Insert a label in the keyword arguments for the legend
if self._hue_var is not None:
kwargs["label"] = utils.to_utf8(self.hue_names[hue_k])
# Get the actual data we are going to plot with
plot_data = data_ijk[list(args)]
if self._dropna:
plot_data = plot_data.dropna()
plot_args = [v for k, v in plot_data.items()]
# Some matplotlib functions don't handle pandas objects correctly
if func_module.startswith("matplotlib"):
plot_args = [v.values for v in plot_args]
# Draw the plot
self._facet_plot(func, ax, plot_args, kwargs)
# Finalize the annotations and layout
self._finalize_grid(args[:2])
return self
| axisgrid.FacetGrid.map |
seaborn | 18 | seaborn/axisgrid.py | def map_dataframe(self, func, *args, **kwargs):
"""Like ``.map`` but passes args as strings and inserts data in kwargs.
This method is suitable for plotting with functions that accept a
long-form DataFrame as a `data` keyword argument and access the
data in that DataFrame using string variable names.
Parameters
----------
func : callable
A plotting function that takes data and keyword arguments. Unlike
the `map` method, a function used here must "understand" Pandas
objects. It also must plot to the currently active matplotlib Axes
and take a `color` keyword argument. If faceting on the `hue`
dimension, it must also take a `label` keyword argument.
args : strings
Column names in self.data that identify variables with data to
plot. The data for each variable is passed to `func` in the
order the variables are specified in the call.
kwargs : keyword arguments
All keyword arguments are passed to the plotting function.
Returns
-------
self : object
Returns self.
"""
| /usr/src/app/target_test_cases/failed_tests_axisgrid.FacetGrid.map_dataframe.txt | def map_dataframe(self, func, *args, **kwargs):
"""Like ``.map`` but passes args as strings and inserts data in kwargs.
This method is suitable for plotting with functions that accept a
long-form DataFrame as a `data` keyword argument and access the
data in that DataFrame using string variable names.
Parameters
----------
func : callable
A plotting function that takes data and keyword arguments. Unlike
the `map` method, a function used here must "understand" Pandas
objects. It also must plot to the currently active matplotlib Axes
and take a `color` keyword argument. If faceting on the `hue`
dimension, it must also take a `label` keyword argument.
args : strings
Column names in self.data that identify variables with data to
plot. The data for each variable is passed to `func` in the
order the variables are specified in the call.
kwargs : keyword arguments
All keyword arguments are passed to the plotting function.
Returns
-------
self : object
Returns self.
"""
# If color was a keyword argument, grab it here
kw_color = kwargs.pop("color", None)
# Iterate over the data subsets
for (row_i, col_j, hue_k), data_ijk in self.facet_data():
# If this subset is null, move on
if not data_ijk.values.size:
continue
# Get the current axis
modify_state = not str(func.__module__).startswith("seaborn")
ax = self.facet_axis(row_i, col_j, modify_state)
# Decide what color to plot with
kwargs["color"] = self._facet_color(hue_k, kw_color)
# Insert the other hue aesthetics if appropriate
for kw, val_list in self.hue_kws.items():
kwargs[kw] = val_list[hue_k]
# Insert a label in the keyword arguments for the legend
if self._hue_var is not None:
kwargs["label"] = self.hue_names[hue_k]
# Stick the facet dataframe into the kwargs
if self._dropna:
data_ijk = data_ijk.dropna()
kwargs["data"] = data_ijk
# Draw the plot
self._facet_plot(func, ax, args, kwargs)
# For axis labels, prefer to use positional args for backcompat
# but also extract the x/y kwargs and use if no corresponding arg
axis_labels = [kwargs.get("x", None), kwargs.get("y", None)]
for i, val in enumerate(args[:2]):
axis_labels[i] = val
self._finalize_grid(axis_labels)
return self
| axisgrid.FacetGrid.map_dataframe |
seaborn | 19 | seaborn/axisgrid.py | def refline(self, *, x=None, y=None, color='.5', linestyle='--', **line_kws):
"""Add a reference line(s) to each facet.
Parameters
----------
x, y : numeric
Value(s) to draw the line(s) at.
color : :mod:`matplotlib color <matplotlib.colors>`
Specifies the color of the reference line(s). Pass ``color=None`` to
use ``hue`` mapping.
linestyle : str
Specifies the style of the reference line(s).
line_kws : key, value mappings
Other keyword arguments are passed to :meth:`matplotlib.axes.Axes.axvline`
when ``x`` is not None and :meth:`matplotlib.axes.Axes.axhline` when ``y``
is not None.
Returns
-------
:class:`FacetGrid` instance
Returns ``self`` for easy method chaining.
"""
| /usr/src/app/target_test_cases/failed_tests_axisgrid.FacetGrid.refline.txt | def refline(self, *, x=None, y=None, color='.5', linestyle='--', **line_kws):
"""Add a reference line(s) to each facet.
Parameters
----------
x, y : numeric
Value(s) to draw the line(s) at.
color : :mod:`matplotlib color <matplotlib.colors>`
Specifies the color of the reference line(s). Pass ``color=None`` to
use ``hue`` mapping.
linestyle : str
Specifies the style of the reference line(s).
line_kws : key, value mappings
Other keyword arguments are passed to :meth:`matplotlib.axes.Axes.axvline`
when ``x`` is not None and :meth:`matplotlib.axes.Axes.axhline` when ``y``
is not None.
Returns
-------
:class:`FacetGrid` instance
Returns ``self`` for easy method chaining.
"""
line_kws['color'] = color
line_kws['linestyle'] = linestyle
if x is not None:
self.map(plt.axvline, x=x, **line_kws)
if y is not None:
self.map(plt.axhline, y=y, **line_kws)
return self
| axisgrid.FacetGrid.refline |
seaborn | 20 | seaborn/axisgrid.py | def set_titles(self, template=None, row_template=None, col_template=None, **kwargs):
"""Draw titles either above each facet or on the grid margins.
Parameters
----------
template : string
Template for all titles with the formatting keys {col_var} and
{col_name} (if using a `col` faceting variable) and/or {row_var}
and {row_name} (if using a `row` faceting variable).
row_template:
Template for the row variable when titles are drawn on the grid
margins. Must have {row_var} and {row_name} formatting keys.
col_template:
Template for the column variable when titles are drawn on the grid
margins. Must have {col_var} and {col_name} formatting keys.
Returns
-------
self: object
Returns self.
"""
| /usr/src/app/target_test_cases/failed_tests_axisgrid.FacetGrid.set_titles.txt | def set_titles(self, template=None, row_template=None, col_template=None, **kwargs):
"""Draw titles either above each facet or on the grid margins.
Parameters
----------
template : string
Template for all titles with the formatting keys {col_var} and
{col_name} (if using a `col` faceting variable) and/or {row_var}
and {row_name} (if using a `row` faceting variable).
row_template:
Template for the row variable when titles are drawn on the grid
margins. Must have {row_var} and {row_name} formatting keys.
col_template:
Template for the column variable when titles are drawn on the grid
margins. Must have {col_var} and {col_name} formatting keys.
Returns
-------
self: object
Returns self.
"""
args = dict(row_var=self._row_var, col_var=self._col_var)
kwargs["size"] = kwargs.pop("size", mpl.rcParams["axes.labelsize"])
# Establish default templates
if row_template is None:
row_template = "{row_var} = {row_name}"
if col_template is None:
col_template = "{col_var} = {col_name}"
if template is None:
if self._row_var is None:
template = col_template
elif self._col_var is None:
template = row_template
else:
template = " | ".join([row_template, col_template])
row_template = utils.to_utf8(row_template)
col_template = utils.to_utf8(col_template)
template = utils.to_utf8(template)
if self._margin_titles:
# Remove any existing title texts
for text in self._margin_titles_texts:
text.remove()
self._margin_titles_texts = []
if self.row_names is not None:
# Draw the row titles on the right edge of the grid
for i, row_name in enumerate(self.row_names):
ax = self.axes[i, -1]
args.update(dict(row_name=row_name))
title = row_template.format(**args)
text = ax.annotate(
title, xy=(1.02, .5), xycoords="axes fraction",
rotation=270, ha="left", va="center",
**kwargs
)
self._margin_titles_texts.append(text)
if self.col_names is not None:
# Draw the column titles as normal titles
for j, col_name in enumerate(self.col_names):
args.update(dict(col_name=col_name))
title = col_template.format(**args)
self.axes[0, j].set_title(title, **kwargs)
return self
# Otherwise title each facet with all the necessary information
if (self._row_var is not None) and (self._col_var is not None):
for i, row_name in enumerate(self.row_names):
for j, col_name in enumerate(self.col_names):
args.update(dict(row_name=row_name, col_name=col_name))
title = template.format(**args)
self.axes[i, j].set_title(title, **kwargs)
elif self.row_names is not None and len(self.row_names):
for i, row_name in enumerate(self.row_names):
args.update(dict(row_name=row_name))
title = template.format(**args)
self.axes[i, 0].set_title(title, **kwargs)
elif self.col_names is not None and len(self.col_names):
for i, col_name in enumerate(self.col_names):
args.update(dict(col_name=col_name))
title = template.format(**args)
# Index the flat array so col_wrap works
self.axes.flat[i].set_title(title, **kwargs)
return self
| axisgrid.FacetGrid.set_titles |
seaborn | 21 | seaborn/axisgrid.py | def add_legend(self, legend_data=None, title=None, label_order=None,
adjust_subtitles=False, **kwargs):
"""Draw a legend, maybe placing it outside axes and resizing the figure.
Parameters
----------
legend_data : dict
Dictionary mapping label names (or two-element tuples where the
second element is a label name) to matplotlib artist handles. The
default reads from ``self._legend_data``.
title : string
Title for the legend. The default reads from ``self._hue_var``.
label_order : list of labels
The order that the legend entries should appear in. The default
reads from ``self.hue_names``.
adjust_subtitles : bool
If True, modify entries with invisible artists to left-align
the labels and set the font size to that of a title.
kwargs : key, value pairings
Other keyword arguments are passed to the underlying legend methods
on the Figure or Axes object.
Returns
-------
self : Grid instance
Returns self for easy chaining.
"""
| /usr/src/app/target_test_cases/failed_tests_axisgrid.Grid.add_legend.txt | def add_legend(self, legend_data=None, title=None, label_order=None,
adjust_subtitles=False, **kwargs):
"""Draw a legend, maybe placing it outside axes and resizing the figure.
Parameters
----------
legend_data : dict
Dictionary mapping label names (or two-element tuples where the
second element is a label name) to matplotlib artist handles. The
default reads from ``self._legend_data``.
title : string
Title for the legend. The default reads from ``self._hue_var``.
label_order : list of labels
The order that the legend entries should appear in. The default
reads from ``self.hue_names``.
adjust_subtitles : bool
If True, modify entries with invisible artists to left-align
the labels and set the font size to that of a title.
kwargs : key, value pairings
Other keyword arguments are passed to the underlying legend methods
on the Figure or Axes object.
Returns
-------
self : Grid instance
Returns self for easy chaining.
"""
# Find the data for the legend
if legend_data is None:
legend_data = self._legend_data
if label_order is None:
if self.hue_names is None:
label_order = list(legend_data.keys())
else:
label_order = list(map(utils.to_utf8, self.hue_names))
blank_handle = mpl.patches.Patch(alpha=0, linewidth=0)
handles = [legend_data.get(lab, blank_handle) for lab in label_order]
title = self._hue_var if title is None else title
title_size = mpl.rcParams["legend.title_fontsize"]
# Unpack nested labels from a hierarchical legend
labels = []
for entry in label_order:
if isinstance(entry, tuple):
_, label = entry
else:
label = entry
labels.append(label)
# Set default legend kwargs
kwargs.setdefault("scatterpoints", 1)
if self._legend_out:
kwargs.setdefault("frameon", False)
kwargs.setdefault("loc", "center right")
# Draw a full-figure legend outside the grid
figlegend = self._figure.legend(handles, labels, **kwargs)
self._legend = figlegend
figlegend.set_title(title, prop={"size": title_size})
if adjust_subtitles:
adjust_legend_subtitles(figlegend)
# Draw the plot to set the bounding boxes correctly
_draw_figure(self._figure)
# Calculate and set the new width of the figure so the legend fits
legend_width = figlegend.get_window_extent().width / self._figure.dpi
fig_width, fig_height = self._figure.get_size_inches()
self._figure.set_size_inches(fig_width + legend_width, fig_height)
# Draw the plot again to get the new transformations
_draw_figure(self._figure)
# Now calculate how much space we need on the right side
legend_width = figlegend.get_window_extent().width / self._figure.dpi
space_needed = legend_width / (fig_width + legend_width)
margin = .04 if self._margin_titles else .01
self._space_needed = margin + space_needed
right = 1 - self._space_needed
# Place the subplot axes to give space for the legend
self._figure.subplots_adjust(right=right)
self._tight_layout_rect[2] = right
else:
# Draw a legend in the first axis
ax = self.axes.flat[0]
kwargs.setdefault("loc", "best")
leg = ax.legend(handles, labels, **kwargs)
leg.set_title(title, prop={"size": title_size})
self._legend = leg
if adjust_subtitles:
adjust_legend_subtitles(leg)
return self
| axisgrid.Grid.add_legend |
seaborn | 22 | seaborn/axisgrid.py | def tick_params(self, axis='both', **kwargs):
"""Modify the ticks, tick labels, and gridlines.
Parameters
----------
axis : {'x', 'y', 'both'}
The axis on which to apply the formatting.
kwargs : keyword arguments
Additional keyword arguments to pass to
:meth:`matplotlib.axes.Axes.tick_params`.
Returns
-------
self : Grid instance
Returns self for easy chaining.
"""
| /usr/src/app/target_test_cases/failed_tests_axisgrid.Grid.tick_params.txt | def tick_params(self, axis='both', **kwargs):
"""Modify the ticks, tick labels, and gridlines.
Parameters
----------
axis : {'x', 'y', 'both'}
The axis on which to apply the formatting.
kwargs : keyword arguments
Additional keyword arguments to pass to
:meth:`matplotlib.axes.Axes.tick_params`.
Returns
-------
self : Grid instance
Returns self for easy chaining.
"""
for ax in self.figure.axes:
ax.tick_params(axis=axis, **kwargs)
return self
| axisgrid.Grid.tick_params |
seaborn | 23 | seaborn/axisgrid.py | def plot(self, joint_func, marginal_func, **kwargs):
"""Draw the plot by passing functions for joint and marginal axes.
This method passes the ``kwargs`` dictionary to both functions. If you
need more control, call :meth:`JointGrid.plot_joint` and
:meth:`JointGrid.plot_marginals` directly with specific parameters.
Parameters
----------
joint_func, marginal_func : callables
Functions to draw the bivariate and univariate plots. See methods
referenced above for information about the required characteristics
of these functions.
kwargs
Additional keyword arguments are passed to both functions.
Returns
-------
:class:`JointGrid` instance
Returns ``self`` for easy method chaining.
"""
| /usr/src/app/target_test_cases/failed_tests_axisgrid.JointGrid.plot.txt | def plot(self, joint_func, marginal_func, **kwargs):
"""Draw the plot by passing functions for joint and marginal axes.
This method passes the ``kwargs`` dictionary to both functions. If you
need more control, call :meth:`JointGrid.plot_joint` and
:meth:`JointGrid.plot_marginals` directly with specific parameters.
Parameters
----------
joint_func, marginal_func : callables
Functions to draw the bivariate and univariate plots. See methods
referenced above for information about the required characteristics
of these functions.
kwargs
Additional keyword arguments are passed to both functions.
Returns
-------
:class:`JointGrid` instance
Returns ``self`` for easy method chaining.
"""
self.plot_marginals(marginal_func, **kwargs)
self.plot_joint(joint_func, **kwargs)
return self
| axisgrid.JointGrid.plot |
seaborn | 24 | seaborn/axisgrid.py | def plot_joint(self, func, **kwargs):
"""Draw a bivariate plot on the joint axes of the grid.
Parameters
----------
func : plotting callable
If a seaborn function, it should accept ``x`` and ``y``. Otherwise,
it must accept ``x`` and ``y`` vectors of data as the first two
positional arguments, and it must plot on the "current" axes.
If ``hue`` was defined in the class constructor, the function must
accept ``hue`` as a parameter.
kwargs
Keyword argument are passed to the plotting function.
Returns
-------
:class:`JointGrid` instance
Returns ``self`` for easy method chaining.
"""
| /usr/src/app/target_test_cases/failed_tests_axisgrid.JointGrid.plot_joint.txt | def plot_joint(self, func, **kwargs):
"""Draw a bivariate plot on the joint axes of the grid.
Parameters
----------
func : plotting callable
If a seaborn function, it should accept ``x`` and ``y``. Otherwise,
it must accept ``x`` and ``y`` vectors of data as the first two
positional arguments, and it must plot on the "current" axes.
If ``hue`` was defined in the class constructor, the function must
accept ``hue`` as a parameter.
kwargs
Keyword argument are passed to the plotting function.
Returns
-------
:class:`JointGrid` instance
Returns ``self`` for easy method chaining.
"""
kwargs = kwargs.copy()
if str(func.__module__).startswith("seaborn"):
kwargs["ax"] = self.ax_joint
else:
plt.sca(self.ax_joint)
if self.hue is not None:
kwargs["hue"] = self.hue
self._inject_kwargs(func, kwargs, self._hue_params)
if str(func.__module__).startswith("seaborn"):
func(x=self.x, y=self.y, **kwargs)
else:
func(self.x, self.y, **kwargs)
return self
| axisgrid.JointGrid.plot_joint |
seaborn | 25 | seaborn/axisgrid.py | def plot_marginals(self, func, **kwargs):
"""Draw univariate plots on each marginal axes.
Parameters
----------
func : plotting callable
If a seaborn function, it should accept ``x`` and ``y`` and plot
when only one of them is defined. Otherwise, it must accept a vector
of data as the first positional argument and determine its orientation
using the ``vertical`` parameter, and it must plot on the "current" axes.
If ``hue`` was defined in the class constructor, it must accept ``hue``
as a parameter.
kwargs
Keyword argument are passed to the plotting function.
Returns
-------
:class:`JointGrid` instance
Returns ``self`` for easy method chaining.
"""
| /usr/src/app/target_test_cases/failed_tests_axisgrid.JointGrid.plot_marginals.txt | def plot_marginals(self, func, **kwargs):
"""Draw univariate plots on each marginal axes.
Parameters
----------
func : plotting callable
If a seaborn function, it should accept ``x`` and ``y`` and plot
when only one of them is defined. Otherwise, it must accept a vector
of data as the first positional argument and determine its orientation
using the ``vertical`` parameter, and it must plot on the "current" axes.
If ``hue`` was defined in the class constructor, it must accept ``hue``
as a parameter.
kwargs
Keyword argument are passed to the plotting function.
Returns
-------
:class:`JointGrid` instance
Returns ``self`` for easy method chaining.
"""
seaborn_func = (
str(func.__module__).startswith("seaborn")
# deprecated distplot has a legacy API, special case it
and not func.__name__ == "distplot"
)
func_params = signature(func).parameters
kwargs = kwargs.copy()
if self.hue is not None:
kwargs["hue"] = self.hue
self._inject_kwargs(func, kwargs, self._hue_params)
if "legend" in func_params:
kwargs.setdefault("legend", False)
if "orientation" in func_params:
# e.g. plt.hist
orient_kw_x = {"orientation": "vertical"}
orient_kw_y = {"orientation": "horizontal"}
elif "vertical" in func_params:
# e.g. sns.distplot (also how did this get backwards?)
orient_kw_x = {"vertical": False}
orient_kw_y = {"vertical": True}
if seaborn_func:
func(x=self.x, ax=self.ax_marg_x, **kwargs)
else:
plt.sca(self.ax_marg_x)
func(self.x, **orient_kw_x, **kwargs)
if seaborn_func:
func(y=self.y, ax=self.ax_marg_y, **kwargs)
else:
plt.sca(self.ax_marg_y)
func(self.y, **orient_kw_y, **kwargs)
self.ax_marg_x.yaxis.get_label().set_visible(False)
self.ax_marg_y.xaxis.get_label().set_visible(False)
return self
| axisgrid.JointGrid.plot_marginals |
seaborn | 26 | seaborn/axisgrid.py | def refline(
self, *, x=None, y=None, joint=True, marginal=True,
color='.5', linestyle='--', **line_kws
):
"""Add a reference line(s) to joint and/or marginal axes.
Parameters
----------
x, y : numeric
Value(s) to draw the line(s) at.
joint, marginal : bools
Whether to add the reference line(s) to the joint/marginal axes.
color : :mod:`matplotlib color <matplotlib.colors>`
Specifies the color of the reference line(s).
linestyle : str
Specifies the style of the reference line(s).
line_kws : key, value mappings
Other keyword arguments are passed to :meth:`matplotlib.axes.Axes.axvline`
when ``x`` is not None and :meth:`matplotlib.axes.Axes.axhline` when ``y``
is not None.
Returns
-------
:class:`JointGrid` instance
Returns ``self`` for easy method chaining.
"""
| /usr/src/app/target_test_cases/failed_tests_axisgrid.JointGrid.refline.txt | def refline(
self, *, x=None, y=None, joint=True, marginal=True,
color='.5', linestyle='--', **line_kws
):
"""Add a reference line(s) to joint and/or marginal axes.
Parameters
----------
x, y : numeric
Value(s) to draw the line(s) at.
joint, marginal : bools
Whether to add the reference line(s) to the joint/marginal axes.
color : :mod:`matplotlib color <matplotlib.colors>`
Specifies the color of the reference line(s).
linestyle : str
Specifies the style of the reference line(s).
line_kws : key, value mappings
Other keyword arguments are passed to :meth:`matplotlib.axes.Axes.axvline`
when ``x`` is not None and :meth:`matplotlib.axes.Axes.axhline` when ``y``
is not None.
Returns
-------
:class:`JointGrid` instance
Returns ``self`` for easy method chaining.
"""
line_kws['color'] = color
line_kws['linestyle'] = linestyle
if x is not None:
if joint:
self.ax_joint.axvline(x, **line_kws)
if marginal:
self.ax_marg_x.axvline(x, **line_kws)
if y is not None:
if joint:
self.ax_joint.axhline(y, **line_kws)
if marginal:
self.ax_marg_y.axhline(y, **line_kws)
return self
| axisgrid.JointGrid.refline |
seaborn | 27 | seaborn/axisgrid.py | def set_axis_labels(self, xlabel="", ylabel="", **kwargs):
"""Set axis labels on the bivariate axes.
Parameters
----------
xlabel, ylabel : strings
Label names for the x and y variables.
kwargs : key, value mappings
Other keyword arguments are passed to the following functions:
- :meth:`matplotlib.axes.Axes.set_xlabel`
- :meth:`matplotlib.axes.Axes.set_ylabel`
Returns
-------
:class:`JointGrid` instance
Returns ``self`` for easy method chaining.
"""
| /usr/src/app/target_test_cases/failed_tests_axisgrid.JointGrid.set_axis_labels.txt | def set_axis_labels(self, xlabel="", ylabel="", **kwargs):
"""Set axis labels on the bivariate axes.
Parameters
----------
xlabel, ylabel : strings
Label names for the x and y variables.
kwargs : key, value mappings
Other keyword arguments are passed to the following functions:
- :meth:`matplotlib.axes.Axes.set_xlabel`
- :meth:`matplotlib.axes.Axes.set_ylabel`
Returns
-------
:class:`JointGrid` instance
Returns ``self`` for easy method chaining.
"""
self.ax_joint.set_xlabel(xlabel, **kwargs)
self.ax_joint.set_ylabel(ylabel, **kwargs)
return self
| axisgrid.JointGrid.set_axis_labels |
seaborn | 28 | seaborn/axisgrid.py | def __init__(
self, data, *, hue=None, vars=None, x_vars=None, y_vars=None,
hue_order=None, palette=None, hue_kws=None, corner=False, diag_sharey=True,
height=2.5, aspect=1, layout_pad=.5, despine=True, dropna=False,
):
"""Initialize the plot figure and PairGrid object.
Parameters
----------
data : DataFrame
Tidy (long-form) dataframe where each column is a variable and
each row is an observation.
hue : string (variable name)
Variable in ``data`` to map plot aspects to different colors. This
variable will be excluded from the default x and y variables.
vars : list of variable names
Variables within ``data`` to use, otherwise use every column with
a numeric datatype.
{x, y}_vars : lists of variable names
Variables within ``data`` to use separately for the rows and
columns of the figure; i.e. to make a non-square plot.
hue_order : list of strings
Order for the levels of the hue variable in the palette
palette : dict or seaborn color palette
Set of colors for mapping the ``hue`` variable. If a dict, keys
should be values in the ``hue`` variable.
hue_kws : dictionary of param -> list of values mapping
Other keyword arguments to insert into the plotting call to let
other plot attributes vary across levels of the hue variable (e.g.
the markers in a scatterplot).
corner : bool
If True, don't add axes to the upper (off-diagonal) triangle of the
grid, making this a "corner" plot.
height : scalar
Height (in inches) of each facet.
aspect : scalar
Aspect * height gives the width (in inches) of each facet.
layout_pad : scalar
Padding between axes; passed to ``fig.tight_layout``.
despine : boolean
Remove the top and right spines from the plots.
dropna : boolean
Drop missing values from the data before plotting.
See Also
--------
pairplot : Easily drawing common uses of :class:`PairGrid`.
FacetGrid : Subplot grid for plotting conditional relationships.
Examples
--------
.. include:: ../docstrings/PairGrid.rst
"""
| /usr/src/app/target_test_cases/failed_tests_axisgrid.PairGrid.__init__.txt | def __init__(
self, data, *, hue=None, vars=None, x_vars=None, y_vars=None,
hue_order=None, palette=None, hue_kws=None, corner=False, diag_sharey=True,
height=2.5, aspect=1, layout_pad=.5, despine=True, dropna=False,
):
"""Initialize the plot figure and PairGrid object.
Parameters
----------
data : DataFrame
Tidy (long-form) dataframe where each column is a variable and
each row is an observation.
hue : string (variable name)
Variable in ``data`` to map plot aspects to different colors. This
variable will be excluded from the default x and y variables.
vars : list of variable names
Variables within ``data`` to use, otherwise use every column with
a numeric datatype.
{x, y}_vars : lists of variable names
Variables within ``data`` to use separately for the rows and
columns of the figure; i.e. to make a non-square plot.
hue_order : list of strings
Order for the levels of the hue variable in the palette
palette : dict or seaborn color palette
Set of colors for mapping the ``hue`` variable. If a dict, keys
should be values in the ``hue`` variable.
hue_kws : dictionary of param -> list of values mapping
Other keyword arguments to insert into the plotting call to let
other plot attributes vary across levels of the hue variable (e.g.
the markers in a scatterplot).
corner : bool
If True, don't add axes to the upper (off-diagonal) triangle of the
grid, making this a "corner" plot.
height : scalar
Height (in inches) of each facet.
aspect : scalar
Aspect * height gives the width (in inches) of each facet.
layout_pad : scalar
Padding between axes; passed to ``fig.tight_layout``.
despine : boolean
Remove the top and right spines from the plots.
dropna : boolean
Drop missing values from the data before plotting.
See Also
--------
pairplot : Easily drawing common uses of :class:`PairGrid`.
FacetGrid : Subplot grid for plotting conditional relationships.
Examples
--------
.. include:: ../docstrings/PairGrid.rst
"""
super().__init__()
data = handle_data_source(data)
# Sort out the variables that define the grid
numeric_cols = self._find_numeric_cols(data)
if hue in numeric_cols:
numeric_cols.remove(hue)
if vars is not None:
x_vars = list(vars)
y_vars = list(vars)
if x_vars is None:
x_vars = numeric_cols
if y_vars is None:
y_vars = numeric_cols
if np.isscalar(x_vars):
x_vars = [x_vars]
if np.isscalar(y_vars):
y_vars = [y_vars]
self.x_vars = x_vars = list(x_vars)
self.y_vars = y_vars = list(y_vars)
self.square_grid = self.x_vars == self.y_vars
if not x_vars:
raise ValueError("No variables found for grid columns.")
if not y_vars:
raise ValueError("No variables found for grid rows.")
# Create the figure and the array of subplots
figsize = len(x_vars) * height * aspect, len(y_vars) * height
with _disable_autolayout():
fig = plt.figure(figsize=figsize)
axes = fig.subplots(len(y_vars), len(x_vars),
sharex="col", sharey="row",
squeeze=False)
# Possibly remove upper axes to make a corner grid
# Note: setting up the axes is usually the most time-intensive part
# of using the PairGrid. We are foregoing the speed improvement that
# we would get by just not setting up the hidden axes so that we can
# avoid implementing fig.subplots ourselves. But worth thinking about.
self._corner = corner
if corner:
hide_indices = np.triu_indices_from(axes, 1)
for i, j in zip(*hide_indices):
axes[i, j].remove()
axes[i, j] = None
self._figure = fig
self.axes = axes
self.data = data
# Save what we are going to do with the diagonal
self.diag_sharey = diag_sharey
self.diag_vars = None
self.diag_axes = None
self._dropna = dropna
# Label the axes
self._add_axis_labels()
# Sort out the hue variable
self._hue_var = hue
if hue is None:
self.hue_names = hue_order = ["_nolegend_"]
self.hue_vals = pd.Series(["_nolegend_"] * len(data),
index=data.index)
else:
# We need hue_order and hue_names because the former is used to control
# the order of drawing and the latter is used to control the order of
# the legend. hue_names can become string-typed while hue_order must
# retain the type of the input data. This is messy but results from
# the fact that PairGrid can implement the hue-mapping logic itself
# (and was originally written exclusively that way) but now can delegate
# to the axes-level functions, while always handling legend creation.
# See GH2307
hue_names = hue_order = categorical_order(data[hue], hue_order)
if dropna:
# Filter NA from the list of unique hue names
hue_names = list(filter(pd.notnull, hue_names))
self.hue_names = hue_names
self.hue_vals = data[hue]
# Additional dict of kwarg -> list of values for mapping the hue var
self.hue_kws = hue_kws if hue_kws is not None else {}
self._orig_palette = palette
self._hue_order = hue_order
self.palette = self._get_palette(data, hue, hue_order, palette)
self._legend_data = {}
# Make the plot look nice
for ax in axes[:-1, :].flat:
if ax is None:
continue
for label in ax.get_xticklabels():
label.set_visible(False)
ax.xaxis.offsetText.set_visible(False)
ax.xaxis.label.set_visible(False)
for ax in axes[:, 1:].flat:
if ax is None:
continue
for label in ax.get_yticklabels():
label.set_visible(False)
ax.yaxis.offsetText.set_visible(False)
ax.yaxis.label.set_visible(False)
self._tight_layout_rect = [.01, .01, .99, .99]
self._tight_layout_pad = layout_pad
self._despine = despine
if despine:
utils.despine(fig=fig)
self.tight_layout(pad=layout_pad)
| axisgrid.PairGrid.__init__ |
seaborn | 29 | seaborn/axisgrid.py | def pairplot(
data, *,
hue=None, hue_order=None, palette=None,
vars=None, x_vars=None, y_vars=None,
kind="scatter", diag_kind="auto", markers=None,
height=2.5, aspect=1, corner=False, dropna=False,
plot_kws=None, diag_kws=None, grid_kws=None, size=None,
):
"""Plot pairwise relationships in a dataset.
By default, this function will create a grid of Axes such that each numeric
variable in ``data`` will by shared across the y-axes across a single row and
the x-axes across a single column. The diagonal plots are treated
differently: a univariate distribution plot is drawn to show the marginal
distribution of the data in each column.
It is also possible to show a subset of variables or plot different
variables on the rows and columns.
This is a high-level interface for :class:`PairGrid` that is intended to
make it easy to draw a few common styles. You should use :class:`PairGrid`
directly if you need more flexibility.
Parameters
----------
data : `pandas.DataFrame`
Tidy (long-form) dataframe where each column is a variable and
each row is an observation.
hue : name of variable in ``data``
Variable in ``data`` to map plot aspects to different colors.
hue_order : list of strings
Order for the levels of the hue variable in the palette
palette : dict or seaborn color palette
Set of colors for mapping the ``hue`` variable. If a dict, keys
should be values in the ``hue`` variable.
vars : list of variable names
Variables within ``data`` to use, otherwise use every column with
a numeric datatype.
{x, y}_vars : lists of variable names
Variables within ``data`` to use separately for the rows and
columns of the figure; i.e. to make a non-square plot.
kind : {'scatter', 'kde', 'hist', 'reg'}
Kind of plot to make.
diag_kind : {'auto', 'hist', 'kde', None}
Kind of plot for the diagonal subplots. If 'auto', choose based on
whether or not ``hue`` is used.
markers : single matplotlib marker code or list
Either the marker to use for all scatterplot points or a list of markers
with a length the same as the number of levels in the hue variable so that
differently colored points will also have different scatterplot
markers.
height : scalar
Height (in inches) of each facet.
aspect : scalar
Aspect * height gives the width (in inches) of each facet.
corner : bool
If True, don't add axes to the upper (off-diagonal) triangle of the
grid, making this a "corner" plot.
dropna : boolean
Drop missing values from the data before plotting.
{plot, diag, grid}_kws : dicts
Dictionaries of keyword arguments. ``plot_kws`` are passed to the
bivariate plotting function, ``diag_kws`` are passed to the univariate
plotting function, and ``grid_kws`` are passed to the :class:`PairGrid`
constructor.
Returns
-------
grid : :class:`PairGrid`
Returns the underlying :class:`PairGrid` instance for further tweaking.
See Also
--------
PairGrid : Subplot grid for more flexible plotting of pairwise relationships.
JointGrid : Grid for plotting joint and marginal distributions of two variables.
Examples
--------
.. include:: ../docstrings/pairplot.rst
"""
| /usr/src/app/target_test_cases/failed_tests_axisgrid.pairplot.txt | def pairplot(
data, *,
hue=None, hue_order=None, palette=None,
vars=None, x_vars=None, y_vars=None,
kind="scatter", diag_kind="auto", markers=None,
height=2.5, aspect=1, corner=False, dropna=False,
plot_kws=None, diag_kws=None, grid_kws=None, size=None,
):
"""Plot pairwise relationships in a dataset.
By default, this function will create a grid of Axes such that each numeric
variable in ``data`` will by shared across the y-axes across a single row and
the x-axes across a single column. The diagonal plots are treated
differently: a univariate distribution plot is drawn to show the marginal
distribution of the data in each column.
It is also possible to show a subset of variables or plot different
variables on the rows and columns.
This is a high-level interface for :class:`PairGrid` that is intended to
make it easy to draw a few common styles. You should use :class:`PairGrid`
directly if you need more flexibility.
Parameters
----------
data : `pandas.DataFrame`
Tidy (long-form) dataframe where each column is a variable and
each row is an observation.
hue : name of variable in ``data``
Variable in ``data`` to map plot aspects to different colors.
hue_order : list of strings
Order for the levels of the hue variable in the palette
palette : dict or seaborn color palette
Set of colors for mapping the ``hue`` variable. If a dict, keys
should be values in the ``hue`` variable.
vars : list of variable names
Variables within ``data`` to use, otherwise use every column with
a numeric datatype.
{x, y}_vars : lists of variable names
Variables within ``data`` to use separately for the rows and
columns of the figure; i.e. to make a non-square plot.
kind : {'scatter', 'kde', 'hist', 'reg'}
Kind of plot to make.
diag_kind : {'auto', 'hist', 'kde', None}
Kind of plot for the diagonal subplots. If 'auto', choose based on
whether or not ``hue`` is used.
markers : single matplotlib marker code or list
Either the marker to use for all scatterplot points or a list of markers
with a length the same as the number of levels in the hue variable so that
differently colored points will also have different scatterplot
markers.
height : scalar
Height (in inches) of each facet.
aspect : scalar
Aspect * height gives the width (in inches) of each facet.
corner : bool
If True, don't add axes to the upper (off-diagonal) triangle of the
grid, making this a "corner" plot.
dropna : boolean
Drop missing values from the data before plotting.
{plot, diag, grid}_kws : dicts
Dictionaries of keyword arguments. ``plot_kws`` are passed to the
bivariate plotting function, ``diag_kws`` are passed to the univariate
plotting function, and ``grid_kws`` are passed to the :class:`PairGrid`
constructor.
Returns
-------
grid : :class:`PairGrid`
Returns the underlying :class:`PairGrid` instance for further tweaking.
See Also
--------
PairGrid : Subplot grid for more flexible plotting of pairwise relationships.
JointGrid : Grid for plotting joint and marginal distributions of two variables.
Examples
--------
.. include:: ../docstrings/pairplot.rst
"""
# Avoid circular import
from .distributions import histplot, kdeplot
# Handle deprecations
if size is not None:
height = size
msg = ("The `size` parameter has been renamed to `height`; "
"please update your code.")
warnings.warn(msg, UserWarning)
if not isinstance(data, pd.DataFrame):
raise TypeError(
f"'data' must be pandas DataFrame object, not: {type(data)}")
plot_kws = {} if plot_kws is None else plot_kws.copy()
diag_kws = {} if diag_kws is None else diag_kws.copy()
grid_kws = {} if grid_kws is None else grid_kws.copy()
# Resolve "auto" diag kind
if diag_kind == "auto":
if hue is None:
diag_kind = "kde" if kind == "kde" else "hist"
else:
diag_kind = "hist" if kind == "hist" else "kde"
# Set up the PairGrid
grid_kws.setdefault("diag_sharey", diag_kind == "hist")
grid = PairGrid(data, vars=vars, x_vars=x_vars, y_vars=y_vars, hue=hue,
hue_order=hue_order, palette=palette, corner=corner,
height=height, aspect=aspect, dropna=dropna, **grid_kws)
# Add the markers here as PairGrid has figured out how many levels of the
# hue variable are needed and we don't want to duplicate that process
if markers is not None:
if kind == "reg":
# Needed until regplot supports style
if grid.hue_names is None:
n_markers = 1
else:
n_markers = len(grid.hue_names)
if not isinstance(markers, list):
markers = [markers] * n_markers
if len(markers) != n_markers:
raise ValueError("markers must be a singleton or a list of "
"markers for each level of the hue variable")
grid.hue_kws = {"marker": markers}
elif kind == "scatter":
if isinstance(markers, str):
plot_kws["marker"] = markers
elif hue is not None:
plot_kws["style"] = data[hue]
plot_kws["markers"] = markers
# Draw the marginal plots on the diagonal
diag_kws = diag_kws.copy()
diag_kws.setdefault("legend", False)
if diag_kind == "hist":
grid.map_diag(histplot, **diag_kws)
elif diag_kind == "kde":
diag_kws.setdefault("fill", True)
diag_kws.setdefault("warn_singular", False)
grid.map_diag(kdeplot, **diag_kws)
# Maybe plot on the off-diagonals
if diag_kind is not None:
plotter = grid.map_offdiag
else:
plotter = grid.map
if kind == "scatter":
from .relational import scatterplot # Avoid circular import
plotter(scatterplot, **plot_kws)
elif kind == "reg":
from .regression import regplot # Avoid circular import
plotter(regplot, **plot_kws)
elif kind == "kde":
from .distributions import kdeplot # Avoid circular import
plot_kws.setdefault("warn_singular", False)
plotter(kdeplot, **plot_kws)
elif kind == "hist":
from .distributions import histplot # Avoid circular import
plotter(histplot, **plot_kws)
# Add a legend
if hue is not None:
grid.add_legend()
grid.tight_layout()
return grid
| axisgrid.pairplot |
seaborn | 30 | seaborn/_marks/base.py | def _resolve(
self,
data: DataFrame | dict[str, Any],
name: str,
scales: dict[str, Scale] | None = None,
) -> Any:
"""Obtain default, specified, or mapped value for a named feature.
Parameters
----------
data : DataFrame or dict with scalar values
Container with data values for features that will be semantically mapped.
name : string
Identity of the feature / semantic.
scales: dict
Mapping from variable to corresponding scale object.
Returns
-------
value or array of values
Outer return type depends on whether `data` is a dict (implying that
we want a single value) or DataFrame (implying that we want an array
of values with matching length).
"""
| /usr/src/app/target_test_cases/failed_tests_base.Mark._resolve.txt | def _resolve(
self,
data: DataFrame | dict[str, Any],
name: str,
scales: dict[str, Scale] | None = None,
) -> Any:
"""Obtain default, specified, or mapped value for a named feature.
Parameters
----------
data : DataFrame or dict with scalar values
Container with data values for features that will be semantically mapped.
name : string
Identity of the feature / semantic.
scales: dict
Mapping from variable to corresponding scale object.
Returns
-------
value or array of values
Outer return type depends on whether `data` is a dict (implying that
we want a single value) or DataFrame (implying that we want an array
of values with matching length).
"""
feature = self._mappable_props[name]
prop = PROPERTIES.get(name, Property(name))
directly_specified = not isinstance(feature, Mappable)
return_multiple = isinstance(data, pd.DataFrame)
return_array = return_multiple and not name.endswith("style")
# Special case width because it needs to be resolved and added to the dataframe
# during layer prep (so the Move operations use it properly).
# TODO how does width *scaling* work, e.g. for violin width by count?
if name == "width":
directly_specified = directly_specified and name not in data
if directly_specified:
feature = prop.standardize(feature)
if return_multiple:
feature = [feature] * len(data)
if return_array:
feature = np.array(feature)
return feature
if name in data:
if scales is None or name not in scales:
# TODO Might this obviate the identity scale? Just don't add a scale?
feature = data[name]
else:
scale = scales[name]
value = data[name]
try:
feature = scale(value)
except Exception as err:
raise PlotSpecError._during("Scaling operation", name) from err
if return_array:
feature = np.asarray(feature)
return feature
if feature.depend is not None:
# TODO add source_func or similar to transform the source value?
# e.g. set linewidth as a proportion of pointsize?
return self._resolve(data, feature.depend, scales)
default = prop.standardize(feature.default)
if return_multiple:
default = [default] * len(data)
if return_array:
default = np.array(default)
return default
| base.Mark._resolve |
seaborn | 31 | seaborn/_marks/base.py | def resolve_color(
mark: Mark,
data: DataFrame | dict,
prefix: str = "",
scales: dict[str, Scale] | None = None,
) -> RGBATuple | ndarray:
"""
Obtain a default, specified, or mapped value for a color feature.
This method exists separately to support the relationship between a
color and its corresponding alpha. We want to respect alpha values that
are passed in specified (or mapped) color values but also make use of a
separate `alpha` variable, which can be mapped. This approach may also
be extended to support mapping of specific color channels (i.e.
luminance, chroma) in the future.
Parameters
----------
mark :
Mark with the color property.
data :
Container with data values for features that will be semantically mapped.
prefix :
Support "color", "fillcolor", etc.
"""
| /usr/src/app/target_test_cases/failed_tests_resolve_color.txt | def resolve_color(
mark: Mark,
data: DataFrame | dict,
prefix: str = "",
scales: dict[str, Scale] | None = None,
) -> RGBATuple | ndarray:
"""
Obtain a default, specified, or mapped value for a color feature.
This method exists separately to support the relationship between a
color and its corresponding alpha. We want to respect alpha values that
are passed in specified (or mapped) color values but also make use of a
separate `alpha` variable, which can be mapped. This approach may also
be extended to support mapping of specific color channels (i.e.
luminance, chroma) in the future.
Parameters
----------
mark :
Mark with the color property.
data :
Container with data values for features that will be semantically mapped.
prefix :
Support "color", "fillcolor", etc.
"""
color = mark._resolve(data, f"{prefix}color", scales)
if f"{prefix}alpha" in mark._mappable_props:
alpha = mark._resolve(data, f"{prefix}alpha", scales)
else:
alpha = mark._resolve(data, "alpha", scales)
def visible(x, axis=None):
"""Detect "invisible" colors to set alpha appropriately."""
# TODO First clause only needed to handle non-rgba arrays,
# which we are trying to handle upstream
return np.array(x).dtype.kind != "f" or np.isfinite(x).all(axis)
# Second check here catches vectors of strings with identity scale
# It could probably be handled better upstream. This is a tricky problem
if np.ndim(color) < 2 and all(isinstance(x, float) for x in color):
if len(color) == 4:
return mpl.colors.to_rgba(color)
alpha = alpha if visible(color) else np.nan
return mpl.colors.to_rgba(color, alpha)
else:
if np.ndim(color) == 2 and color.shape[1] == 4:
return mpl.colors.to_rgba_array(color)
alpha = np.where(visible(color, axis=1), alpha, np.nan)
return mpl.colors.to_rgba_array(color, alpha)
# TODO should we be implementing fill here too?
# (i.e. set fillalpha to 0 when fill=False)
| base.resolve_color |
seaborn | 32 | seaborn/_core/rules.py | def categorical_order(vector: Series, order: list | None = None) -> list:
"""
Return a list of unique data values using seaborn's ordering rules.
Parameters
----------
vector : Series
Vector of "categorical" values
order : list
Desired order of category levels to override the order determined
from the `data` object.
Returns
-------
order : list
Ordered list of category levels not including null values.
"""
| /usr/src/app/target_test_cases/failed_tests_rules.categorical_order.txt | def categorical_order(vector: Series, order: list | None = None) -> list:
"""
Return a list of unique data values using seaborn's ordering rules.
Parameters
----------
vector : Series
Vector of "categorical" values
order : list
Desired order of category levels to override the order determined
from the `data` object.
Returns
-------
order : list
Ordered list of category levels not including null values.
"""
if order is not None:
return order
if vector.dtype.name == "category":
order = list(vector.cat.categories)
else:
order = list(filter(pd.notnull, vector.unique()))
if variable_type(pd.Series(order)) == "numeric":
order.sort()
return order
| categorical_order |
seaborn | 33 | seaborn/palettes.py | def color_palette(palette=None, n_colors=None, desat=None, as_cmap=False):
"""Return a list of colors or continuous colormap defining a palette.
Possible ``palette`` values include:
- Name of a seaborn palette (deep, muted, bright, pastel, dark, colorblind)
- Name of matplotlib colormap
- 'husl' or 'hls'
- 'ch:<cubehelix arguments>'
- 'light:<color>', 'dark:<color>', 'blend:<color>,<color>',
- A sequence of colors in any format matplotlib accepts
Calling this function with ``palette=None`` will return the current
matplotlib color cycle.
This function can also be used in a ``with`` statement to temporarily
set the color cycle for a plot or set of plots.
See the :ref:`tutorial <palette_tutorial>` for more information.
Parameters
----------
palette : None, string, or sequence, optional
Name of palette or None to return current palette. If a sequence, input
colors are used but possibly cycled and desaturated.
n_colors : int, optional
Number of colors in the palette. If ``None``, the default will depend
on how ``palette`` is specified. Named palettes default to 6 colors,
but grabbing the current palette or passing in a list of colors will
not change the number of colors unless this is specified. Asking for
more colors than exist in the palette will cause it to cycle. Ignored
when ``as_cmap`` is True.
desat : float, optional
Proportion to desaturate each color by.
as_cmap : bool
If True, return a :class:`matplotlib.colors.ListedColormap`.
Returns
-------
list of RGB tuples or :class:`matplotlib.colors.ListedColormap`
See Also
--------
set_palette : Set the default color cycle for all plots.
set_color_codes : Reassign color codes like ``"b"``, ``"g"``, etc. to
colors from one of the seaborn palettes.
Examples
--------
.. include:: ../docstrings/color_palette.rst
"""
| /usr/src/app/target_test_cases/failed_tests_color_palette.txt | def color_palette(palette=None, n_colors=None, desat=None, as_cmap=False):
"""Return a list of colors or continuous colormap defining a palette.
Possible ``palette`` values include:
- Name of a seaborn palette (deep, muted, bright, pastel, dark, colorblind)
- Name of matplotlib colormap
- 'husl' or 'hls'
- 'ch:<cubehelix arguments>'
- 'light:<color>', 'dark:<color>', 'blend:<color>,<color>',
- A sequence of colors in any format matplotlib accepts
Calling this function with ``palette=None`` will return the current
matplotlib color cycle.
This function can also be used in a ``with`` statement to temporarily
set the color cycle for a plot or set of plots.
See the :ref:`tutorial <palette_tutorial>` for more information.
Parameters
----------
palette : None, string, or sequence, optional
Name of palette or None to return current palette. If a sequence, input
colors are used but possibly cycled and desaturated.
n_colors : int, optional
Number of colors in the palette. If ``None``, the default will depend
on how ``palette`` is specified. Named palettes default to 6 colors,
but grabbing the current palette or passing in a list of colors will
not change the number of colors unless this is specified. Asking for
more colors than exist in the palette will cause it to cycle. Ignored
when ``as_cmap`` is True.
desat : float, optional
Proportion to desaturate each color by.
as_cmap : bool
If True, return a :class:`matplotlib.colors.ListedColormap`.
Returns
-------
list of RGB tuples or :class:`matplotlib.colors.ListedColormap`
See Also
--------
set_palette : Set the default color cycle for all plots.
set_color_codes : Reassign color codes like ``"b"``, ``"g"``, etc. to
colors from one of the seaborn palettes.
Examples
--------
.. include:: ../docstrings/color_palette.rst
"""
if palette is None:
palette = get_color_cycle()
if n_colors is None:
n_colors = len(palette)
elif not isinstance(palette, str):
palette = palette
if n_colors is None:
n_colors = len(palette)
else:
if n_colors is None:
# Use all colors in a qualitative palette or 6 of another kind
n_colors = QUAL_PALETTE_SIZES.get(palette, 6)
if palette in SEABORN_PALETTES:
# Named "seaborn variant" of matplotlib default color cycle
palette = SEABORN_PALETTES[palette]
elif palette == "hls":
# Evenly spaced colors in cylindrical RGB space
palette = hls_palette(n_colors, as_cmap=as_cmap)
elif palette == "husl":
# Evenly spaced colors in cylindrical Lab space
palette = husl_palette(n_colors, as_cmap=as_cmap)
elif palette.lower() == "jet":
# Paternalism
raise ValueError("No.")
elif palette.startswith("ch:"):
# Cubehelix palette with params specified in string
args, kwargs = _parse_cubehelix_args(palette)
palette = cubehelix_palette(n_colors, *args, **kwargs, as_cmap=as_cmap)
elif palette.startswith("light:"):
# light palette to color specified in string
_, color = palette.split(":")
reverse = color.endswith("_r")
if reverse:
color = color[:-2]
palette = light_palette(color, n_colors, reverse=reverse, as_cmap=as_cmap)
elif palette.startswith("dark:"):
# light palette to color specified in string
_, color = palette.split(":")
reverse = color.endswith("_r")
if reverse:
color = color[:-2]
palette = dark_palette(color, n_colors, reverse=reverse, as_cmap=as_cmap)
elif palette.startswith("blend:"):
# blend palette between colors specified in string
_, colors = palette.split(":")
colors = colors.split(",")
palette = blend_palette(colors, n_colors, as_cmap=as_cmap)
else:
try:
# Perhaps a named matplotlib colormap?
palette = mpl_palette(palette, n_colors, as_cmap=as_cmap)
except (ValueError, KeyError): # Error class changed in mpl36
raise ValueError(f"{palette!r} is not a valid palette name")
if desat is not None:
palette = [desaturate(c, desat) for c in palette]
if not as_cmap:
# Always return as many colors as we asked for
pal_cycle = cycle(palette)
palette = [next(pal_cycle) for _ in range(n_colors)]
# Always return in r, g, b tuple format
try:
palette = map(mpl.colors.colorConverter.to_rgb, palette)
palette = _ColorPalette(palette)
except ValueError:
raise ValueError(f"Could not generate a palette for {palette}")
return palette
| color_palette |
seaborn | 34 | seaborn/utils.py | def desaturate(color, prop):
"""Decrease the saturation channel of a color by some percent.
Parameters
----------
color : matplotlib color
hex, rgb-tuple, or html color name
prop : float
saturation channel of color will be multiplied by this value
Returns
-------
new_color : rgb tuple
desaturated color code in RGB tuple representation
"""
| /usr/src/app/target_test_cases/failed_tests_desaturate.txt | def desaturate(color, prop):
"""Decrease the saturation channel of a color by some percent.
Parameters
----------
color : matplotlib color
hex, rgb-tuple, or html color name
prop : float
saturation channel of color will be multiplied by this value
Returns
-------
new_color : rgb tuple
desaturated color code in RGB tuple representation
"""
# Check inputs
if not 0 <= prop <= 1:
raise ValueError("prop must be between 0 and 1")
# Get rgb tuple rep
rgb = to_rgb(color)
# Short circuit to avoid floating point issues
if prop == 1:
return rgb
# Convert to hls
h, l, s = colorsys.rgb_to_hls(*rgb)
# Desaturate the saturation channel
s *= prop
# Convert back to rgb
new_color = colorsys.hls_to_rgb(h, l, s)
return new_color
| desaturate |
seaborn | 35 | seaborn/_core/groupby.py | def __init__(self, order: list[str] | dict[str, list | None]):
"""
Initialize the GroupBy from grouping variables and optional level orders.
Parameters
----------
order
List of variable names or dict mapping names to desired level orders.
Level order values can be None to use default ordering rules. The
variables can include names that are not expected to appear in the
data; these will be dropped before the groups are defined.
"""
| /usr/src/app/target_test_cases/failed_tests_groupby.GroupBy.__init__.txt | def __init__(self, order: list[str] | dict[str, list | None]):
"""
Initialize the GroupBy from grouping variables and optional level orders.
Parameters
----------
order
List of variable names or dict mapping names to desired level orders.
Level order values can be None to use default ordering rules. The
variables can include names that are not expected to appear in the
data; these will be dropped before the groups are defined.
"""
if not order:
raise ValueError("GroupBy requires at least one grouping variable")
if isinstance(order, list):
order = {k: None for k in order}
self.order = order
| groupby.GroupBy.__init__ |
seaborn | 36 | seaborn/external/kde.py | def evaluate(self, points):
"""Evaluate the estimated pdf on a set of points.
Parameters
----------
points : (# of dimensions, # of points)-array
Alternatively, a (# of dimensions,) vector can be passed in and
treated as a single point.
Returns
-------
values : (# of points,)-array
The values at each point.
Raises
------
ValueError : if the dimensionality of the input points is different than
the dimensionality of the KDE.
"""
| /usr/src/app/target_test_cases/failed_tests_kde.gaussian_kde.evaluate.txt | def evaluate(self, points):
"""Evaluate the estimated pdf on a set of points.
Parameters
----------
points : (# of dimensions, # of points)-array
Alternatively, a (# of dimensions,) vector can be passed in and
treated as a single point.
Returns
-------
values : (# of points,)-array
The values at each point.
Raises
------
ValueError : if the dimensionality of the input points is different than
the dimensionality of the KDE.
"""
points = atleast_2d(asarray(points))
d, m = points.shape
if d != self.d:
if d == 1 and m == self.d:
# points was passed in as a row vector
points = reshape(points, (self.d, 1))
m = 1
else:
msg = f"points have dimension {d}, dataset has dimension {self.d}"
raise ValueError(msg)
output_dtype = np.common_type(self.covariance, points)
result = zeros((m,), dtype=output_dtype)
whitening = linalg.cholesky(self.inv_cov)
scaled_dataset = dot(whitening, self.dataset)
scaled_points = dot(whitening, points)
if m >= self.n:
# there are more points than data, so loop over data
for i in range(self.n):
diff = scaled_dataset[:, i, newaxis] - scaled_points
energy = sum(diff * diff, axis=0) / 2.0
result += self.weights[i]*exp(-energy)
else:
# loop over points
for i in range(m):
diff = scaled_dataset - scaled_points[:, i, newaxis]
energy = sum(diff * diff, axis=0) / 2.0
result[i] = sum(exp(-energy)*self.weights, axis=0)
result = result / self._norm_factor
return result
| kde.gaussian_kde.evaluate |
seaborn | 37 | seaborn/external/kde.py | def set_bandwidth(self, bw_method=None):
"""Compute the estimator bandwidth with given method.
The new bandwidth calculated after a call to `set_bandwidth` is used
for subsequent evaluations of the estimated density.
Parameters
----------
bw_method : str, scalar or callable, optional
The method used to calculate the estimator bandwidth. This can be
'scott', 'silverman', a scalar constant or a callable. If a
scalar, this will be used directly as `kde.factor`. If a callable,
it should take a `gaussian_kde` instance as only parameter and
return a scalar. If None (default), nothing happens; the current
`kde.covariance_factor` method is kept.
Notes
-----
.. versionadded:: 0.11
"""
| /usr/src/app/target_test_cases/failed_tests_kde.gaussian_kde.set_bandwidth.txt | def set_bandwidth(self, bw_method=None):
"""Compute the estimator bandwidth with given method.
The new bandwidth calculated after a call to `set_bandwidth` is used
for subsequent evaluations of the estimated density.
Parameters
----------
bw_method : str, scalar or callable, optional
The method used to calculate the estimator bandwidth. This can be
'scott', 'silverman', a scalar constant or a callable. If a
scalar, this will be used directly as `kde.factor`. If a callable,
it should take a `gaussian_kde` instance as only parameter and
return a scalar. If None (default), nothing happens; the current
`kde.covariance_factor` method is kept.
Notes
-----
.. versionadded:: 0.11
"""
if bw_method is None:
pass
elif bw_method == 'scott':
self.covariance_factor = self.scotts_factor
elif bw_method == 'silverman':
self.covariance_factor = self.silverman_factor
elif np.isscalar(bw_method) and not isinstance(bw_method, str):
self._bw_method = 'use constant'
self.covariance_factor = lambda: bw_method
elif callable(bw_method):
self._bw_method = bw_method
self.covariance_factor = lambda: self._bw_method(self)
else:
msg = "`bw_method` should be 'scott', 'silverman', a scalar " \
"or a callable."
raise ValueError(msg)
self._compute_covariance()
| kde.gaussian_kde.set_bandwidth |
seaborn | 38 | seaborn/utils.py | def load_dataset(name, cache=True, data_home=None, **kws):
"""Load an example dataset from the online repository (requires internet).
This function provides quick access to a small number of example datasets
that are useful for documenting seaborn or generating reproducible examples
for bug reports. It is not necessary for normal usage.
Note that some of the datasets have a small amount of preprocessing applied
to define a proper ordering for categorical variables.
Use :func:`get_dataset_names` to see a list of available datasets.
Parameters
----------
name : str
Name of the dataset (``{name}.csv`` on
https://github.com/mwaskom/seaborn-data).
cache : boolean, optional
If True, try to load from the local cache first, and save to the cache
if a download is required.
data_home : string, optional
The directory in which to cache data; see :func:`get_data_home`.
kws : keys and values, optional
Additional keyword arguments are passed to passed through to
:func:`pandas.read_csv`.
Returns
-------
df : :class:`pandas.DataFrame`
Tabular data, possibly with some preprocessing applied.
"""
| /usr/src/app/target_test_cases/failed_tests_load_dataset.txt | def load_dataset(name, cache=True, data_home=None, **kws):
"""Load an example dataset from the online repository (requires internet).
This function provides quick access to a small number of example datasets
that are useful for documenting seaborn or generating reproducible examples
for bug reports. It is not necessary for normal usage.
Note that some of the datasets have a small amount of preprocessing applied
to define a proper ordering for categorical variables.
Use :func:`get_dataset_names` to see a list of available datasets.
Parameters
----------
name : str
Name of the dataset (``{name}.csv`` on
https://github.com/mwaskom/seaborn-data).
cache : boolean, optional
If True, try to load from the local cache first, and save to the cache
if a download is required.
data_home : string, optional
The directory in which to cache data; see :func:`get_data_home`.
kws : keys and values, optional
Additional keyword arguments are passed to passed through to
:func:`pandas.read_csv`.
Returns
-------
df : :class:`pandas.DataFrame`
Tabular data, possibly with some preprocessing applied.
"""
# A common beginner mistake is to assume that one's personal data needs
# to be passed through this function to be usable with seaborn.
# Let's provide a more helpful error than you would otherwise get.
if isinstance(name, pd.DataFrame):
err = (
"This function accepts only strings (the name of an example dataset). "
"You passed a pandas DataFrame. If you have your own dataset, "
"it is not necessary to use this function before plotting."
)
raise TypeError(err)
url = f"{DATASET_SOURCE}/{name}.csv"
if cache:
cache_path = os.path.join(get_data_home(data_home), os.path.basename(url))
if not os.path.exists(cache_path):
if name not in get_dataset_names():
raise ValueError(f"'{name}' is not one of the example datasets.")
urlretrieve(url, cache_path)
full_path = cache_path
else:
full_path = url
df = pd.read_csv(full_path, **kws)
if df.iloc[-1].isnull().all():
df = df.iloc[:-1]
# Set some columns as a categorical type with ordered levels
if name == "tips":
df["day"] = pd.Categorical(df["day"], ["Thur", "Fri", "Sat", "Sun"])
df["sex"] = pd.Categorical(df["sex"], ["Male", "Female"])
df["time"] = pd.Categorical(df["time"], ["Lunch", "Dinner"])
df["smoker"] = pd.Categorical(df["smoker"], ["Yes", "No"])
elif name == "flights":
months = df["month"].str[:3]
df["month"] = pd.Categorical(months, months.unique())
elif name == "exercise":
df["time"] = pd.Categorical(df["time"], ["1 min", "15 min", "30 min"])
df["kind"] = pd.Categorical(df["kind"], ["rest", "walking", "running"])
df["diet"] = pd.Categorical(df["diet"], ["no fat", "low fat"])
elif name == "titanic":
df["class"] = pd.Categorical(df["class"], ["First", "Second", "Third"])
df["deck"] = pd.Categorical(df["deck"], list("ABCDEFG"))
elif name == "penguins":
df["sex"] = df["sex"].str.title()
elif name == "diamonds":
df["color"] = pd.Categorical(
df["color"], ["D", "E", "F", "G", "H", "I", "J"],
)
df["clarity"] = pd.Categorical(
df["clarity"], ["IF", "VVS1", "VVS2", "VS1", "VS2", "SI1", "SI2", "I1"],
)
df["cut"] = pd.Categorical(
df["cut"], ["Ideal", "Premium", "Very Good", "Good", "Fair"],
)
elif name == "taxis":
df["pickup"] = pd.to_datetime(df["pickup"])
df["dropoff"] = pd.to_datetime(df["dropoff"])
elif name == "seaice":
df["Date"] = pd.to_datetime(df["Date"])
elif name == "dowjones":
df["Date"] = pd.to_datetime(df["Date"])
return df
| load_dataset |
seaborn | 39 | seaborn/matrix.py | def clustermap(
data, *,
pivot_kws=None, method='average', metric='euclidean',
z_score=None, standard_scale=None, figsize=(10, 10),
cbar_kws=None, row_cluster=True, col_cluster=True,
row_linkage=None, col_linkage=None,
row_colors=None, col_colors=None, mask=None,
dendrogram_ratio=.2, colors_ratio=0.03,
cbar_pos=(.02, .8, .05, .18), tree_kws=None,
**kwargs
):
"""
Plot a matrix dataset as a hierarchically-clustered heatmap.
This function requires scipy to be available.
Parameters
----------
data : 2D array-like
Rectangular data for clustering. Cannot contain NAs.
pivot_kws : dict, optional
If `data` is a tidy dataframe, can provide keyword arguments for
pivot to create a rectangular dataframe.
method : str, optional
Linkage method to use for calculating clusters. See
:func:`scipy.cluster.hierarchy.linkage` documentation for more
information.
metric : str, optional
Distance metric to use for the data. See
:func:`scipy.spatial.distance.pdist` documentation for more options.
To use different metrics (or methods) for rows and columns, you may
construct each linkage matrix yourself and provide them as
`{row,col}_linkage`.
z_score : int or None, optional
Either 0 (rows) or 1 (columns). Whether or not to calculate z-scores
for the rows or the columns. Z scores are: z = (x - mean)/std, so
values in each row (column) will get the mean of the row (column)
subtracted, then divided by the standard deviation of the row (column).
This ensures that each row (column) has mean of 0 and variance of 1.
standard_scale : int or None, optional
Either 0 (rows) or 1 (columns). Whether or not to standardize that
dimension, meaning for each row or column, subtract the minimum and
divide each by its maximum.
figsize : tuple of (width, height), optional
Overall size of the figure.
cbar_kws : dict, optional
Keyword arguments to pass to `cbar_kws` in :func:`heatmap`, e.g. to
add a label to the colorbar.
{row,col}_cluster : bool, optional
If ``True``, cluster the {rows, columns}.
{row,col}_linkage : :class:`numpy.ndarray`, optional
Precomputed linkage matrix for the rows or columns. See
:func:`scipy.cluster.hierarchy.linkage` for specific formats.
{row,col}_colors : list-like or pandas DataFrame/Series, optional
List of colors to label for either the rows or columns. Useful to evaluate
whether samples within a group are clustered together. Can use nested lists or
DataFrame for multiple color levels of labeling. If given as a
:class:`pandas.DataFrame` or :class:`pandas.Series`, labels for the colors are
extracted from the DataFrames column names or from the name of the Series.
DataFrame/Series colors are also matched to the data by their index, ensuring
colors are drawn in the correct order.
mask : bool array or DataFrame, optional
If passed, data will not be shown in cells where `mask` is True.
Cells with missing values are automatically masked. Only used for
visualizing, not for calculating.
{dendrogram,colors}_ratio : float, or pair of floats, optional
Proportion of the figure size devoted to the two marginal elements. If
a pair is given, they correspond to (row, col) ratios.
cbar_pos : tuple of (left, bottom, width, height), optional
Position of the colorbar axes in the figure. Setting to ``None`` will
disable the colorbar.
tree_kws : dict, optional
Parameters for the :class:`matplotlib.collections.LineCollection`
that is used to plot the lines of the dendrogram tree.
kwargs : other keyword arguments
All other keyword arguments are passed to :func:`heatmap`.
Returns
-------
:class:`ClusterGrid`
A :class:`ClusterGrid` instance.
See Also
--------
heatmap : Plot rectangular data as a color-encoded matrix.
Notes
-----
The returned object has a ``savefig`` method that should be used if you
want to save the figure object without clipping the dendrograms.
To access the reordered row indices, use:
``clustergrid.dendrogram_row.reordered_ind``
Column indices, use:
``clustergrid.dendrogram_col.reordered_ind``
Examples
--------
.. include:: ../docstrings/clustermap.rst
"""
| /usr/src/app/target_test_cases/failed_tests_matrix.clustermap.txt | def clustermap(
data, *,
pivot_kws=None, method='average', metric='euclidean',
z_score=None, standard_scale=None, figsize=(10, 10),
cbar_kws=None, row_cluster=True, col_cluster=True,
row_linkage=None, col_linkage=None,
row_colors=None, col_colors=None, mask=None,
dendrogram_ratio=.2, colors_ratio=0.03,
cbar_pos=(.02, .8, .05, .18), tree_kws=None,
**kwargs
):
"""
Plot a matrix dataset as a hierarchically-clustered heatmap.
This function requires scipy to be available.
Parameters
----------
data : 2D array-like
Rectangular data for clustering. Cannot contain NAs.
pivot_kws : dict, optional
If `data` is a tidy dataframe, can provide keyword arguments for
pivot to create a rectangular dataframe.
method : str, optional
Linkage method to use for calculating clusters. See
:func:`scipy.cluster.hierarchy.linkage` documentation for more
information.
metric : str, optional
Distance metric to use for the data. See
:func:`scipy.spatial.distance.pdist` documentation for more options.
To use different metrics (or methods) for rows and columns, you may
construct each linkage matrix yourself and provide them as
`{row,col}_linkage`.
z_score : int or None, optional
Either 0 (rows) or 1 (columns). Whether or not to calculate z-scores
for the rows or the columns. Z scores are: z = (x - mean)/std, so
values in each row (column) will get the mean of the row (column)
subtracted, then divided by the standard deviation of the row (column).
This ensures that each row (column) has mean of 0 and variance of 1.
standard_scale : int or None, optional
Either 0 (rows) or 1 (columns). Whether or not to standardize that
dimension, meaning for each row or column, subtract the minimum and
divide each by its maximum.
figsize : tuple of (width, height), optional
Overall size of the figure.
cbar_kws : dict, optional
Keyword arguments to pass to `cbar_kws` in :func:`heatmap`, e.g. to
add a label to the colorbar.
{row,col}_cluster : bool, optional
If ``True``, cluster the {rows, columns}.
{row,col}_linkage : :class:`numpy.ndarray`, optional
Precomputed linkage matrix for the rows or columns. See
:func:`scipy.cluster.hierarchy.linkage` for specific formats.
{row,col}_colors : list-like or pandas DataFrame/Series, optional
List of colors to label for either the rows or columns. Useful to evaluate
whether samples within a group are clustered together. Can use nested lists or
DataFrame for multiple color levels of labeling. If given as a
:class:`pandas.DataFrame` or :class:`pandas.Series`, labels for the colors are
extracted from the DataFrames column names or from the name of the Series.
DataFrame/Series colors are also matched to the data by their index, ensuring
colors are drawn in the correct order.
mask : bool array or DataFrame, optional
If passed, data will not be shown in cells where `mask` is True.
Cells with missing values are automatically masked. Only used for
visualizing, not for calculating.
{dendrogram,colors}_ratio : float, or pair of floats, optional
Proportion of the figure size devoted to the two marginal elements. If
a pair is given, they correspond to (row, col) ratios.
cbar_pos : tuple of (left, bottom, width, height), optional
Position of the colorbar axes in the figure. Setting to ``None`` will
disable the colorbar.
tree_kws : dict, optional
Parameters for the :class:`matplotlib.collections.LineCollection`
that is used to plot the lines of the dendrogram tree.
kwargs : other keyword arguments
All other keyword arguments are passed to :func:`heatmap`.
Returns
-------
:class:`ClusterGrid`
A :class:`ClusterGrid` instance.
See Also
--------
heatmap : Plot rectangular data as a color-encoded matrix.
Notes
-----
The returned object has a ``savefig`` method that should be used if you
want to save the figure object without clipping the dendrograms.
To access the reordered row indices, use:
``clustergrid.dendrogram_row.reordered_ind``
Column indices, use:
``clustergrid.dendrogram_col.reordered_ind``
Examples
--------
.. include:: ../docstrings/clustermap.rst
"""
if _no_scipy:
raise RuntimeError("clustermap requires scipy to be available")
plotter = ClusterGrid(data, pivot_kws=pivot_kws, figsize=figsize,
row_colors=row_colors, col_colors=col_colors,
z_score=z_score, standard_scale=standard_scale,
mask=mask, dendrogram_ratio=dendrogram_ratio,
colors_ratio=colors_ratio, cbar_pos=cbar_pos)
return plotter.plot(metric=metric, method=method,
colorbar_kws=cbar_kws,
row_cluster=row_cluster, col_cluster=col_cluster,
row_linkage=row_linkage, col_linkage=col_linkage,
tree_kws=tree_kws, **kwargs)
| matrix.clustermap |
seaborn | 40 | seaborn/matrix.py | def dendrogram(
data, *,
linkage=None, axis=1, label=True, metric='euclidean',
method='average', rotate=False, tree_kws=None, ax=None
):
"""Draw a tree diagram of relationships within a matrix
Parameters
----------
data : pandas.DataFrame
Rectangular data
linkage : numpy.array, optional
Linkage matrix
axis : int, optional
Which axis to use to calculate linkage. 0 is rows, 1 is columns.
label : bool, optional
If True, label the dendrogram at leaves with column or row names
metric : str, optional
Distance metric. Anything valid for scipy.spatial.distance.pdist
method : str, optional
Linkage method to use. Anything valid for
scipy.cluster.hierarchy.linkage
rotate : bool, optional
When plotting the matrix, whether to rotate it 90 degrees
counter-clockwise, so the leaves face right
tree_kws : dict, optional
Keyword arguments for the ``matplotlib.collections.LineCollection``
that is used for plotting the lines of the dendrogram tree.
ax : matplotlib axis, optional
Axis to plot on, otherwise uses current axis
Returns
-------
dendrogramplotter : _DendrogramPlotter
A Dendrogram plotter object.
Notes
-----
Access the reordered dendrogram indices with
dendrogramplotter.reordered_ind
"""
| /usr/src/app/target_test_cases/failed_tests_matrix.dendrogram.txt | def dendrogram(
data, *,
linkage=None, axis=1, label=True, metric='euclidean',
method='average', rotate=False, tree_kws=None, ax=None
):
"""Draw a tree diagram of relationships within a matrix
Parameters
----------
data : pandas.DataFrame
Rectangular data
linkage : numpy.array, optional
Linkage matrix
axis : int, optional
Which axis to use to calculate linkage. 0 is rows, 1 is columns.
label : bool, optional
If True, label the dendrogram at leaves with column or row names
metric : str, optional
Distance metric. Anything valid for scipy.spatial.distance.pdist
method : str, optional
Linkage method to use. Anything valid for
scipy.cluster.hierarchy.linkage
rotate : bool, optional
When plotting the matrix, whether to rotate it 90 degrees
counter-clockwise, so the leaves face right
tree_kws : dict, optional
Keyword arguments for the ``matplotlib.collections.LineCollection``
that is used for plotting the lines of the dendrogram tree.
ax : matplotlib axis, optional
Axis to plot on, otherwise uses current axis
Returns
-------
dendrogramplotter : _DendrogramPlotter
A Dendrogram plotter object.
Notes
-----
Access the reordered dendrogram indices with
dendrogramplotter.reordered_ind
"""
if _no_scipy:
raise RuntimeError("dendrogram requires scipy to be installed")
plotter = _DendrogramPlotter(data, linkage=linkage, axis=axis,
metric=metric, method=method,
label=label, rotate=rotate)
if ax is None:
ax = plt.gca()
return plotter.plot(ax=ax, tree_kws=tree_kws)
| matrix.dendrogram |
seaborn | 41 | seaborn/matrix.py | def heatmap(
data, *,
vmin=None, vmax=None, cmap=None, center=None, robust=False,
annot=None, fmt=".2g", annot_kws=None,
linewidths=0, linecolor="white",
cbar=True, cbar_kws=None, cbar_ax=None,
square=False, xticklabels="auto", yticklabels="auto",
mask=None, ax=None,
**kwargs
):
"""Plot rectangular data as a color-encoded matrix.
This is an Axes-level function and will draw the heatmap into the
currently-active Axes if none is provided to the ``ax`` argument. Part of
this Axes space will be taken and used to plot a colormap, unless ``cbar``
is False or a separate Axes is provided to ``cbar_ax``.
Parameters
----------
data : rectangular dataset
2D dataset that can be coerced into an ndarray. If a Pandas DataFrame
is provided, the index/column information will be used to label the
columns and rows.
vmin, vmax : floats, optional
Values to anchor the colormap, otherwise they are inferred from the
data and other keyword arguments.
cmap : matplotlib colormap name or object, or list of colors, optional
The mapping from data values to color space. If not provided, the
default will depend on whether ``center`` is set.
center : float, optional
The value at which to center the colormap when plotting divergent data.
Using this parameter will change the default ``cmap`` if none is
specified.
robust : bool, optional
If True and ``vmin`` or ``vmax`` are absent, the colormap range is
computed with robust quantiles instead of the extreme values.
annot : bool or rectangular dataset, optional
If True, write the data value in each cell. If an array-like with the
same shape as ``data``, then use this to annotate the heatmap instead
of the data. Note that DataFrames will match on position, not index.
fmt : str, optional
String formatting code to use when adding annotations.
annot_kws : dict of key, value mappings, optional
Keyword arguments for :meth:`matplotlib.axes.Axes.text` when ``annot``
is True.
linewidths : float, optional
Width of the lines that will divide each cell.
linecolor : color, optional
Color of the lines that will divide each cell.
cbar : bool, optional
Whether to draw a colorbar.
cbar_kws : dict of key, value mappings, optional
Keyword arguments for :meth:`matplotlib.figure.Figure.colorbar`.
cbar_ax : matplotlib Axes, optional
Axes in which to draw the colorbar, otherwise take space from the
main Axes.
square : bool, optional
If True, set the Axes aspect to "equal" so each cell will be
square-shaped.
xticklabels, yticklabels : "auto", bool, list-like, or int, optional
If True, plot the column names of the dataframe. If False, don't plot
the column names. If list-like, plot these alternate labels as the
xticklabels. If an integer, use the column names but plot only every
n label. If "auto", try to densely plot non-overlapping labels.
mask : bool array or DataFrame, optional
If passed, data will not be shown in cells where ``mask`` is True.
Cells with missing values are automatically masked.
ax : matplotlib Axes, optional
Axes in which to draw the plot, otherwise use the currently-active
Axes.
kwargs : other keyword arguments
All other keyword arguments are passed to
:meth:`matplotlib.axes.Axes.pcolormesh`.
Returns
-------
ax : matplotlib Axes
Axes object with the heatmap.
See Also
--------
clustermap : Plot a matrix using hierarchical clustering to arrange the
rows and columns.
Examples
--------
.. include:: ../docstrings/heatmap.rst
"""
| /usr/src/app/target_test_cases/failed_tests_matrix.heatmap.txt | def heatmap(
data, *,
vmin=None, vmax=None, cmap=None, center=None, robust=False,
annot=None, fmt=".2g", annot_kws=None,
linewidths=0, linecolor="white",
cbar=True, cbar_kws=None, cbar_ax=None,
square=False, xticklabels="auto", yticklabels="auto",
mask=None, ax=None,
**kwargs
):
"""Plot rectangular data as a color-encoded matrix.
This is an Axes-level function and will draw the heatmap into the
currently-active Axes if none is provided to the ``ax`` argument. Part of
this Axes space will be taken and used to plot a colormap, unless ``cbar``
is False or a separate Axes is provided to ``cbar_ax``.
Parameters
----------
data : rectangular dataset
2D dataset that can be coerced into an ndarray. If a Pandas DataFrame
is provided, the index/column information will be used to label the
columns and rows.
vmin, vmax : floats, optional
Values to anchor the colormap, otherwise they are inferred from the
data and other keyword arguments.
cmap : matplotlib colormap name or object, or list of colors, optional
The mapping from data values to color space. If not provided, the
default will depend on whether ``center`` is set.
center : float, optional
The value at which to center the colormap when plotting divergent data.
Using this parameter will change the default ``cmap`` if none is
specified.
robust : bool, optional
If True and ``vmin`` or ``vmax`` are absent, the colormap range is
computed with robust quantiles instead of the extreme values.
annot : bool or rectangular dataset, optional
If True, write the data value in each cell. If an array-like with the
same shape as ``data``, then use this to annotate the heatmap instead
of the data. Note that DataFrames will match on position, not index.
fmt : str, optional
String formatting code to use when adding annotations.
annot_kws : dict of key, value mappings, optional
Keyword arguments for :meth:`matplotlib.axes.Axes.text` when ``annot``
is True.
linewidths : float, optional
Width of the lines that will divide each cell.
linecolor : color, optional
Color of the lines that will divide each cell.
cbar : bool, optional
Whether to draw a colorbar.
cbar_kws : dict of key, value mappings, optional
Keyword arguments for :meth:`matplotlib.figure.Figure.colorbar`.
cbar_ax : matplotlib Axes, optional
Axes in which to draw the colorbar, otherwise take space from the
main Axes.
square : bool, optional
If True, set the Axes aspect to "equal" so each cell will be
square-shaped.
xticklabels, yticklabels : "auto", bool, list-like, or int, optional
If True, plot the column names of the dataframe. If False, don't plot
the column names. If list-like, plot these alternate labels as the
xticklabels. If an integer, use the column names but plot only every
n label. If "auto", try to densely plot non-overlapping labels.
mask : bool array or DataFrame, optional
If passed, data will not be shown in cells where ``mask`` is True.
Cells with missing values are automatically masked.
ax : matplotlib Axes, optional
Axes in which to draw the plot, otherwise use the currently-active
Axes.
kwargs : other keyword arguments
All other keyword arguments are passed to
:meth:`matplotlib.axes.Axes.pcolormesh`.
Returns
-------
ax : matplotlib Axes
Axes object with the heatmap.
See Also
--------
clustermap : Plot a matrix using hierarchical clustering to arrange the
rows and columns.
Examples
--------
.. include:: ../docstrings/heatmap.rst
"""
# Initialize the plotter object
plotter = _HeatMapper(data, vmin, vmax, cmap, center, robust, annot, fmt,
annot_kws, cbar, cbar_kws, xticklabels,
yticklabels, mask)
# Add the pcolormesh kwargs here
kwargs["linewidths"] = linewidths
kwargs["edgecolor"] = linecolor
# Draw the plot and return the Axes
if ax is None:
ax = plt.gca()
if square:
ax.set_aspect("equal")
plotter.plot(ax, cbar_ax, kwargs)
return ax
| matrix.heatmap |
seaborn | 42 | seaborn/palettes.py | def blend_palette(colors, n_colors=6, as_cmap=False, input="rgb"):
"""Make a palette that blends between a list of colors.
Parameters
----------
colors : sequence of colors in various formats interpreted by `input`
hex code, html color name, or tuple in `input` space.
n_colors : int, optional
Number of colors in the palette.
as_cmap : bool, optional
If True, return a :class:`matplotlib.colors.ListedColormap`.
Returns
-------
palette
list of RGB tuples or :class:`matplotlib.colors.ListedColormap`
Examples
--------
.. include: ../docstrings/blend_palette.rst
"""
| /usr/src/app/target_test_cases/failed_tests_palettes.blend_palette.txt | def blend_palette(colors, n_colors=6, as_cmap=False, input="rgb"):
"""Make a palette that blends between a list of colors.
Parameters
----------
colors : sequence of colors in various formats interpreted by `input`
hex code, html color name, or tuple in `input` space.
n_colors : int, optional
Number of colors in the palette.
as_cmap : bool, optional
If True, return a :class:`matplotlib.colors.ListedColormap`.
Returns
-------
palette
list of RGB tuples or :class:`matplotlib.colors.ListedColormap`
Examples
--------
.. include: ../docstrings/blend_palette.rst
"""
colors = [_color_to_rgb(color, input) for color in colors]
name = "blend"
pal = mpl.colors.LinearSegmentedColormap.from_list(name, colors)
if not as_cmap:
rgb_array = pal(np.linspace(0, 1, int(n_colors)))[:, :3] # no alpha
pal = _ColorPalette(map(tuple, rgb_array))
return pal
| palettes.blend_palette |
seaborn | 43 | seaborn/palettes.py | def crayon_palette(colors):
"""Make a palette with color names from Crayola crayons.
Colors are taken from here:
https://en.wikipedia.org/wiki/List_of_Crayola_crayon_colors
This is just a simple wrapper around the `seaborn.crayons` dictionary.
Parameters
----------
colors : list of strings
List of keys in the `seaborn.crayons` dictionary.
Returns
-------
palette
A list of colors as RGB tuples.
See Also
--------
xkcd_palette : Make a palette with named colors from the XKCD color survey.
"""
| /usr/src/app/target_test_cases/failed_tests_palettes.crayon_palette.txt | def crayon_palette(colors):
"""Make a palette with color names from Crayola crayons.
Colors are taken from here:
https://en.wikipedia.org/wiki/List_of_Crayola_crayon_colors
This is just a simple wrapper around the `seaborn.crayons` dictionary.
Parameters
----------
colors : list of strings
List of keys in the `seaborn.crayons` dictionary.
Returns
-------
palette
A list of colors as RGB tuples.
See Also
--------
xkcd_palette : Make a palette with named colors from the XKCD color survey.
"""
palette = [crayons[name] for name in colors]
return color_palette(palette, len(palette))
| palettes.crayon_palette |
seaborn | 44 | seaborn/palettes.py | def cubehelix_palette(n_colors=6, start=0, rot=.4, gamma=1.0, hue=0.8,
light=.85, dark=.15, reverse=False, as_cmap=False):
"""Make a sequential palette from the cubehelix system.
This produces a colormap with linearly-decreasing (or increasing)
brightness. That means that information will be preserved if printed to
black and white or viewed by someone who is colorblind. "cubehelix" is
also available as a matplotlib-based palette, but this function gives the
user more control over the look of the palette and has a different set of
defaults.
In addition to using this function, it is also possible to generate a
cubehelix palette generally in seaborn using a string starting with
`ch:` and containing other parameters (e.g. `"ch:s=.25,r=-.5"`).
Parameters
----------
n_colors : int
Number of colors in the palette.
start : float, 0 <= start <= 3
The hue value at the start of the helix.
rot : float
Rotations around the hue wheel over the range of the palette.
gamma : float 0 <= gamma
Nonlinearity to emphasize dark (gamma < 1) or light (gamma > 1) colors.
hue : float, 0 <= hue <= 1
Saturation of the colors.
dark : float 0 <= dark <= 1
Intensity of the darkest color in the palette.
light : float 0 <= light <= 1
Intensity of the lightest color in the palette.
reverse : bool
If True, the palette will go from dark to light.
as_cmap : bool
If True, return a :class:`matplotlib.colors.ListedColormap`.
Returns
-------
palette
list of RGB tuples or :class:`matplotlib.colors.ListedColormap`
See Also
--------
choose_cubehelix_palette : Launch an interactive widget to select cubehelix
palette parameters.
dark_palette : Create a sequential palette with dark low values.
light_palette : Create a sequential palette with bright low values.
References
----------
Green, D. A. (2011). "A colour scheme for the display of astronomical
intensity images". Bulletin of the Astromical Society of India, Vol. 39,
p. 289-295.
Examples
--------
.. include:: ../docstrings/cubehelix_palette.rst
"""
| /usr/src/app/target_test_cases/failed_tests_palettes.cubehelix_palette.txt | def cubehelix_palette(n_colors=6, start=0, rot=.4, gamma=1.0, hue=0.8,
light=.85, dark=.15, reverse=False, as_cmap=False):
"""Make a sequential palette from the cubehelix system.
This produces a colormap with linearly-decreasing (or increasing)
brightness. That means that information will be preserved if printed to
black and white or viewed by someone who is colorblind. "cubehelix" is
also available as a matplotlib-based palette, but this function gives the
user more control over the look of the palette and has a different set of
defaults.
In addition to using this function, it is also possible to generate a
cubehelix palette generally in seaborn using a string starting with
`ch:` and containing other parameters (e.g. `"ch:s=.25,r=-.5"`).
Parameters
----------
n_colors : int
Number of colors in the palette.
start : float, 0 <= start <= 3
The hue value at the start of the helix.
rot : float
Rotations around the hue wheel over the range of the palette.
gamma : float 0 <= gamma
Nonlinearity to emphasize dark (gamma < 1) or light (gamma > 1) colors.
hue : float, 0 <= hue <= 1
Saturation of the colors.
dark : float 0 <= dark <= 1
Intensity of the darkest color in the palette.
light : float 0 <= light <= 1
Intensity of the lightest color in the palette.
reverse : bool
If True, the palette will go from dark to light.
as_cmap : bool
If True, return a :class:`matplotlib.colors.ListedColormap`.
Returns
-------
palette
list of RGB tuples or :class:`matplotlib.colors.ListedColormap`
See Also
--------
choose_cubehelix_palette : Launch an interactive widget to select cubehelix
palette parameters.
dark_palette : Create a sequential palette with dark low values.
light_palette : Create a sequential palette with bright low values.
References
----------
Green, D. A. (2011). "A colour scheme for the display of astronomical
intensity images". Bulletin of the Astromical Society of India, Vol. 39,
p. 289-295.
Examples
--------
.. include:: ../docstrings/cubehelix_palette.rst
"""
def get_color_function(p0, p1):
# Copied from matplotlib because it lives in private module
def color(x):
# Apply gamma factor to emphasise low or high intensity values
xg = x ** gamma
# Calculate amplitude and angle of deviation from the black
# to white diagonal in the plane of constant
# perceived intensity.
a = hue * xg * (1 - xg) / 2
phi = 2 * np.pi * (start / 3 + rot * x)
return xg + a * (p0 * np.cos(phi) + p1 * np.sin(phi))
return color
cdict = {
"red": get_color_function(-0.14861, 1.78277),
"green": get_color_function(-0.29227, -0.90649),
"blue": get_color_function(1.97294, 0.0),
}
cmap = mpl.colors.LinearSegmentedColormap("cubehelix", cdict)
x = np.linspace(light, dark, int(n_colors))
pal = cmap(x)[:, :3].tolist()
if reverse:
pal = pal[::-1]
if as_cmap:
x_256 = np.linspace(light, dark, 256)
if reverse:
x_256 = x_256[::-1]
pal_256 = cmap(x_256)
cmap = mpl.colors.ListedColormap(pal_256, "seaborn_cubehelix")
return cmap
else:
return _ColorPalette(pal)
| palettes.cubehelix_palette |
seaborn | 45 | seaborn/palettes.py | def dark_palette(color, n_colors=6, reverse=False, as_cmap=False, input="rgb"):
"""Make a sequential palette that blends from dark to ``color``.
This kind of palette is good for data that range between relatively
uninteresting low values and interesting high values.
The ``color`` parameter can be specified in a number of ways, including
all options for defining a color in matplotlib and several additional
color spaces that are handled by seaborn. You can also use the database
of named colors from the XKCD color survey.
If you are using the IPython notebook, you can also choose this palette
interactively with the :func:`choose_dark_palette` function.
Parameters
----------
color : base color for high values
hex, rgb-tuple, or html color name
n_colors : int, optional
number of colors in the palette
reverse : bool, optional
if True, reverse the direction of the blend
as_cmap : bool, optional
If True, return a :class:`matplotlib.colors.ListedColormap`.
input : {'rgb', 'hls', 'husl', xkcd'}
Color space to interpret the input color. The first three options
apply to tuple inputs and the latter applies to string inputs.
Returns
-------
palette
list of RGB tuples or :class:`matplotlib.colors.ListedColormap`
See Also
--------
light_palette : Create a sequential palette with bright low values.
diverging_palette : Create a diverging palette with two colors.
Examples
--------
.. include:: ../docstrings/dark_palette.rst
"""
| /usr/src/app/target_test_cases/failed_tests_palettes.dark_palette.txt | def dark_palette(color, n_colors=6, reverse=False, as_cmap=False, input="rgb"):
"""Make a sequential palette that blends from dark to ``color``.
This kind of palette is good for data that range between relatively
uninteresting low values and interesting high values.
The ``color`` parameter can be specified in a number of ways, including
all options for defining a color in matplotlib and several additional
color spaces that are handled by seaborn. You can also use the database
of named colors from the XKCD color survey.
If you are using the IPython notebook, you can also choose this palette
interactively with the :func:`choose_dark_palette` function.
Parameters
----------
color : base color for high values
hex, rgb-tuple, or html color name
n_colors : int, optional
number of colors in the palette
reverse : bool, optional
if True, reverse the direction of the blend
as_cmap : bool, optional
If True, return a :class:`matplotlib.colors.ListedColormap`.
input : {'rgb', 'hls', 'husl', xkcd'}
Color space to interpret the input color. The first three options
apply to tuple inputs and the latter applies to string inputs.
Returns
-------
palette
list of RGB tuples or :class:`matplotlib.colors.ListedColormap`
See Also
--------
light_palette : Create a sequential palette with bright low values.
diverging_palette : Create a diverging palette with two colors.
Examples
--------
.. include:: ../docstrings/dark_palette.rst
"""
rgb = _color_to_rgb(color, input)
hue, sat, _ = husl.rgb_to_husl(*rgb)
gray_s, gray_l = .15 * sat, 15
gray = _color_to_rgb((hue, gray_s, gray_l), input="husl")
colors = [rgb, gray] if reverse else [gray, rgb]
return blend_palette(colors, n_colors, as_cmap)
| palettes.dark_palette |
seaborn | 46 | seaborn/palettes.py | def diverging_palette(h_neg, h_pos, s=75, l=50, sep=1, n=6, # noqa
center="light", as_cmap=False):
"""Make a diverging palette between two HUSL colors.
If you are using the IPython notebook, you can also choose this palette
interactively with the :func:`choose_diverging_palette` function.
Parameters
----------
h_neg, h_pos : float in [0, 359]
Anchor hues for negative and positive extents of the map.
s : float in [0, 100], optional
Anchor saturation for both extents of the map.
l : float in [0, 100], optional
Anchor lightness for both extents of the map.
sep : int, optional
Size of the intermediate region.
n : int, optional
Number of colors in the palette (if not returning a cmap)
center : {"light", "dark"}, optional
Whether the center of the palette is light or dark
as_cmap : bool, optional
If True, return a :class:`matplotlib.colors.ListedColormap`.
Returns
-------
palette
list of RGB tuples or :class:`matplotlib.colors.ListedColormap`
See Also
--------
dark_palette : Create a sequential palette with dark values.
light_palette : Create a sequential palette with light values.
Examples
--------
.. include: ../docstrings/diverging_palette.rst
"""
| /usr/src/app/target_test_cases/failed_tests_palettes.diverging_palette.txt | def diverging_palette(h_neg, h_pos, s=75, l=50, sep=1, n=6, # noqa
center="light", as_cmap=False):
"""Make a diverging palette between two HUSL colors.
If you are using the IPython notebook, you can also choose this palette
interactively with the :func:`choose_diverging_palette` function.
Parameters
----------
h_neg, h_pos : float in [0, 359]
Anchor hues for negative and positive extents of the map.
s : float in [0, 100], optional
Anchor saturation for both extents of the map.
l : float in [0, 100], optional
Anchor lightness for both extents of the map.
sep : int, optional
Size of the intermediate region.
n : int, optional
Number of colors in the palette (if not returning a cmap)
center : {"light", "dark"}, optional
Whether the center of the palette is light or dark
as_cmap : bool, optional
If True, return a :class:`matplotlib.colors.ListedColormap`.
Returns
-------
palette
list of RGB tuples or :class:`matplotlib.colors.ListedColormap`
See Also
--------
dark_palette : Create a sequential palette with dark values.
light_palette : Create a sequential palette with light values.
Examples
--------
.. include: ../docstrings/diverging_palette.rst
"""
palfunc = dict(dark=dark_palette, light=light_palette)[center]
n_half = int(128 - (sep // 2))
neg = palfunc((h_neg, s, l), n_half, reverse=True, input="husl")
pos = palfunc((h_pos, s, l), n_half, input="husl")
midpoint = dict(light=[(.95, .95, .95)], dark=[(.133, .133, .133)])[center]
mid = midpoint * sep
pal = blend_palette(np.concatenate([neg, mid, pos]), n, as_cmap=as_cmap)
return pal
| palettes.diverging_palette |
seaborn | 47 | seaborn/palettes.py | def hls_palette(n_colors=6, h=.01, l=.6, s=.65, as_cmap=False): # noqa
"""
Return hues with constant lightness and saturation in the HLS system.
The hues are evenly sampled along a circular path. The resulting palette will be
appropriate for categorical or cyclical data.
The `h`, `l`, and `s` values should be between 0 and 1.
.. note::
While the separation of the resulting colors will be mathematically
constant, the HLS system does not construct a perceptually-uniform space,
so their apparent intensity will vary.
Parameters
----------
n_colors : int
Number of colors in the palette.
h : float
The value of the first hue.
l : float
The lightness value.
s : float
The saturation intensity.
as_cmap : bool
If True, return a matplotlib colormap object.
Returns
-------
palette
list of RGB tuples or :class:`matplotlib.colors.ListedColormap`
See Also
--------
husl_palette : Make a palette using evenly spaced hues in the HUSL system.
Examples
--------
.. include:: ../docstrings/hls_palette.rst
"""
| /usr/src/app/target_test_cases/failed_tests_palettes.hls_palette.txt | def hls_palette(n_colors=6, h=.01, l=.6, s=.65, as_cmap=False): # noqa
"""
Return hues with constant lightness and saturation in the HLS system.
The hues are evenly sampled along a circular path. The resulting palette will be
appropriate for categorical or cyclical data.
The `h`, `l`, and `s` values should be between 0 and 1.
.. note::
While the separation of the resulting colors will be mathematically
constant, the HLS system does not construct a perceptually-uniform space,
so their apparent intensity will vary.
Parameters
----------
n_colors : int
Number of colors in the palette.
h : float
The value of the first hue.
l : float
The lightness value.
s : float
The saturation intensity.
as_cmap : bool
If True, return a matplotlib colormap object.
Returns
-------
palette
list of RGB tuples or :class:`matplotlib.colors.ListedColormap`
See Also
--------
husl_palette : Make a palette using evenly spaced hues in the HUSL system.
Examples
--------
.. include:: ../docstrings/hls_palette.rst
"""
if as_cmap:
n_colors = 256
hues = np.linspace(0, 1, int(n_colors) + 1)[:-1]
hues += h
hues %= 1
hues -= hues.astype(int)
palette = [colorsys.hls_to_rgb(h_i, l, s) for h_i in hues]
if as_cmap:
return mpl.colors.ListedColormap(palette, "hls")
else:
return _ColorPalette(palette)
| palettes.hls_palette |
Can Language Models Replace Programmers? REPOCOD Says 'Not Yet'
Large language models (LLMs) have achieved high accuracy, i.e., more than 90 pass@1, in solving Python coding problems in HumanEval and MBPP. Thus, a natural question is, whether LLMs achieve comparable code completion performance compared to human developers? Unfortunately, one cannot answer this question using existing manual crafted or simple (e.g., single-line) code generation benchmarks, since such tasks fail to represent real-world software development tasks. In addition, existing benchmarks often use poor code correctness metrics, providing misleading conclusions.
To address these challenges, we create REPOCOD, a code generation benchmark with 980 problems collected from 11 popular real-world projects, with more than 58% of them requiring file-level or repository-level context information. In addition, REPOCOD has the longest average canonical solution length (331.6 tokens) and the highest average cyclomatic complexity (9.00) compared to existing benchmarks. Each task in REPOCOD includes 313.5 developer-written test cases on average for better correctness evaluation. In our evaluations on ten LLMs, none of the models achieves more than 30 pass@1 on REPOCOD, disclosing the necessity of building stronger LLMs that can help developers in real-world software development.
For more details on data collection and evaluation results, please refer to our arxiv preprint.
Examples code for downloading repositories, preparing repository snapshot, and running test cases for evaluation are propived at code
Check our Leaderboard for preliminary results using GPT-4o with BM25 and dense retrieval.
Usage
from datasets import load_dataset
data = load_dataset('lt-asset/REPOCOD')
print(data)
DatasetDict({
train: Dataset({
features: ['repository', 'repo_id', 'target_module_path', 'prompt', 'relavent_test_path', 'full_function', 'function_name'],
num_rows: 980
})
})
Data Fields
- repository: the source repository of the current sample
- repo_id: the unique index of the sample in the corresponding source repository
- target_module_path: the file path containing the current sample relative to the root of the source repository
- prompt: the developer provided function signature and docstring
- relavent_test_path: the path to the relevant test cases
- full_function: the canonical solution of the current sample
- function_name: the name of the target function (current sample)
Example
"repository": "seaborn", # collected from seaborn
"repo_id": "0", # first sample from seaborn
"target_module_path": "seaborn/_core/scales.py", # the target function is in this path
"prompt": " def label(
self,
formatter: Formatter | None = None, *,
like: str | Callable | None = None,
base: int | None | Default = default,
unit: str | None = None,
) -> Continuous: ....", # the function signature and docstring for the target function
"relevant_test_path": "/usr/src/app/target_test_cases/failed_tests_Continuous.label.txt", # Path to relevant tests for the function
"full_function": " def label(
self,
formatter: Formatter | None = None, *,
like: str | Callable | None = None,
base: int | None | Default = default,
unit: str | None = None,
) -> Continuous: ....", # the full snippet of the target function, including the function signature and docstring for the target function
"function_name": "Continuous.label" # The name of the target function
Citation
@misc{liang2024repocod,
title={Can Language Models Replace Programmers? REPOCOD Says 'Not Yet'},
author={Shanchao Liang and Yiran Hu and Nan Jiang and Lin Tan},
year={2024},
eprint={2410.21647},
archivePrefix={arXiv},
primaryClass={cs.SE},
url={https://arxiv.org/abs/2410.21647},
}
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