id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
|---|---|---|
171,762 | import os
from typing import Optional
from ._password_hasher import (
DEFAULT_HASH_LENGTH,
DEFAULT_MEMORY_COST,
DEFAULT_PARALLELISM,
DEFAULT_RANDOM_SALT_LENGTH,
DEFAULT_TIME_COST,
)
from ._typing import Literal
from .low_level import Type, hash_secret, hash_secret_raw, verify_secret
class Type(Enum... | Legacy alias for :func:`verify_secret` with default parameters. .. deprecated:: 16.0.0 Use :class:`argon2.PasswordHasher` for passwords. |
171,763 | from enum import Enum
from typing import Any
from _argon2_cffi_bindings import ffi, lib
from ._typing import Literal
from .exceptions import HashingError, VerificationError, VerifyMismatchError
Any = object()
The provided code snippet includes necessary dependencies for implementing the `core` function. Write a Pytho... | Direct binding to the ``argon2_ctx`` function. .. warning:: This is a strictly advanced function working on raw C data structures. Both *Argon2*'s and *argon2-cffi*'s higher-level bindings do a lot of sanity checks and housekeeping work that *you* are now responsible for (e.g. clearing buffers). The structure of the *c... |
171,764 | from __future__ import annotations
from collections.abc import Iterable
import string
from types import MappingProxyType
from typing import Any, BinaryIO, NamedTuple
from ._re import (
RE_DATETIME,
RE_LOCALTIME,
RE_NUMBER,
match_to_datetime,
match_to_localtime,
match_to_number,
)
from ._types im... | Parse TOML from a binary file object. |
171,765 | from __future__ import annotations
import math
def two_factors(n: int) -> tuple[int, int]:
"""Split an integer into two integer factors.
The two factors will be as close as possible to the sqrt of n, and are returned in decreasing
order. Worst case returns (n, 1).
Args:
n (int): The integer to ... | Calculate chunk sizes. Args: chunk_size (int or tuple(int, int), optional): Chunk size in (y, x) directions, or the same size in both directions if only one is specified. chunk_count (int or tuple(int, int), optional): Chunk count in (y, x) directions, or the same count in both irections if only one is specified. total... |
171,766 | from __future__ import annotations
from contourpy._contourpy import FillType, LineType, ZInterp
The provided code snippet includes necessary dependencies for implementing the `as_fill_type` function. Write a Python function `def as_fill_type(fill_type: FillType | str) -> FillType` to solve the following problem:
Coerc... | Coerce a FillType or string value to a FillType. Args: fill_type (FillType or str): Value to convert. Return: FillType: Converted value. |
171,767 | from __future__ import annotations
from contourpy._contourpy import FillType, LineType, ZInterp
The provided code snippet includes necessary dependencies for implementing the `as_line_type` function. Write a Python function `def as_line_type(line_type: LineType | str) -> LineType` to solve the following problem:
Coerc... | Coerce a LineType or string value to a LineType. Args: line_type (LineType or str): Value to convert. Return: LineType: Converted value. |
171,768 | from __future__ import annotations
from contourpy._contourpy import FillType, LineType, ZInterp
The provided code snippet includes necessary dependencies for implementing the `as_z_interp` function. Write a Python function `def as_z_interp(z_interp: ZInterp | str) -> ZInterp` to solve the following problem:
Coerce a Z... | Coerce a ZInterp or string value to a ZInterp. Args: z_interp (ZInterp or str): Value to convert. Return: ZInterp: Converted value. |
171,769 | from __future__ import annotations
from typing import TYPE_CHECKING, Any
import numpy as np
Any = object()
try:
import numpy
except ImportError:
pass
The provided code snippet includes necessary dependencies for implementing the `simple` function. Write a Python function `def simple( shape: tuple[int, in... | Return simple test data consisting of the sum of two gaussians. Args: shape (tuple(int, int)): 2D shape of data to return. want_mask (bool, optional): Whether test data should be masked or not, default ``False``. Return: Tuple of 3 arrays: ``x``, ``y``, ``z`` test data, ``z`` will be masked if ``want_mask=True``. |
171,770 | from __future__ import annotations
from typing import TYPE_CHECKING, Any
import numpy as np
Any = object()
try:
import numpy
except ImportError:
pass
The provided code snippet includes necessary dependencies for implementing the `random` function. Write a Python function `def random( shape: tuple[int, in... | Return random test data.. Args: shape (tuple(int, int)): 2D shape of data to return. seed (int, optional): Seed for random number generator, default 2187. mask_fraction (float, optional): Fraction of elements to mask, default 0. Return: Tuple of 3 arrays: ``x``, ``y``, ``z`` test data, ``z`` will be masked if ``mask_fr... |
171,771 | from __future__ import annotations
from typing import TYPE_CHECKING, cast
import matplotlib.path as mpath
import numpy as np
from contourpy import FillType, LineType
def offsets_to_mpl_codes(offsets: OffsetArray) -> CodeArray:
codes = np.full(offsets[-1]-offsets[0], 2, dtype=np.uint8) # LINETO = 2
codes[offset... | null |
171,772 | from __future__ import annotations
from typing import TYPE_CHECKING, cast
import matplotlib.path as mpath
import numpy as np
from contourpy import FillType, LineType
if TYPE_CHECKING:
from contourpy._contourpy import (
CodeArray, FillReturn, LineReturn, LineReturn_Separate, OffsetArray,
)
TYPE_CHECKING... | null |
171,773 | from __future__ import annotations
from typing import TYPE_CHECKING, cast
from contourpy import FillType, LineType
from contourpy.util.mpl_util import mpl_codes_to_offsets
def mpl_codes_to_offsets(codes: CodeArray) -> OffsetArray:
offsets = np.nonzero(codes == 1)[0].astype(np.uint32)
offsets = np.append(offset... | null |
171,774 | from __future__ import annotations
from typing import TYPE_CHECKING, cast
from contourpy import FillType, LineType
from contourpy.util.mpl_util import mpl_codes_to_offsets
if TYPE_CHECKING:
from contourpy._contourpy import (
CoordinateArray, FillReturn, LineReturn, LineReturn_Separate, LineReturn_SeparateCo... | null |
171,775 | from numbers import Number
from functools import partial
import math
import textwrap
import warnings
import numpy as np
import pandas as pd
import matplotlib as mpl
import matplotlib.pyplot as plt
import matplotlib.transforms as tx
from matplotlib.colors import to_rgba
from matplotlib.collections import LineCollection
... | null |
171,776 | from numbers import Number
from functools import partial
import math
import textwrap
import warnings
import numpy as np
import pandas as pd
import matplotlib as mpl
import matplotlib.pyplot as plt
import matplotlib.transforms as tx
from matplotlib.colors import to_rgba
from matplotlib.collections import LineCollection
... | DEPRECATED This function has been deprecated and will be removed in seaborn v0.14.0. It has been replaced by :func:`histplot` and :func:`displot`, two functions with a modern API and many more capabilities. For a guide to updating, please see this notebook: https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe57... |
171,777 | from textwrap import dedent
from numbers import Number
import warnings
from colorsys import rgb_to_hls
from functools import partial
import numpy as np
import pandas as pd
import matplotlib as mpl
from matplotlib.collections import PatchCollection
import matplotlib.patches as Patches
import matplotlib.pyplot as plt
fro... | null |
171,778 | from textwrap import dedent
from numbers import Number
import warnings
from colorsys import rgb_to_hls
from functools import partial
import numpy as np
import pandas as pd
import matplotlib as mpl
from matplotlib.collections import PatchCollection
import matplotlib.patches as Patches
import matplotlib.pyplot as plt
fro... | null |
171,779 | from textwrap import dedent
from numbers import Number
import warnings
from colorsys import rgb_to_hls
from functools import partial
import numpy as np
import pandas as pd
import matplotlib as mpl
from matplotlib.collections import PatchCollection
import matplotlib.patches as Patches
import matplotlib.pyplot as plt
fro... | null |
171,780 | from textwrap import dedent
from numbers import Number
import warnings
from colorsys import rgb_to_hls
from functools import partial
import numpy as np
import pandas as pd
import matplotlib as mpl
from matplotlib.collections import PatchCollection
import matplotlib.patches as Patches
import matplotlib.pyplot as plt
fro... | null |
171,781 | from textwrap import dedent
from numbers import Number
import warnings
from colorsys import rgb_to_hls
from functools import partial
import numpy as np
import pandas as pd
import matplotlib as mpl
from matplotlib.collections import PatchCollection
import matplotlib.patches as Patches
import matplotlib.pyplot as plt
fro... | null |
171,782 | from textwrap import dedent
from numbers import Number
import warnings
from colorsys import rgb_to_hls
from functools import partial
import numpy as np
import pandas as pd
import matplotlib as mpl
from matplotlib.collections import PatchCollection
import matplotlib.patches as Patches
import matplotlib.pyplot as plt
fro... | null |
171,783 | from textwrap import dedent
from numbers import Number
import warnings
from colorsys import rgb_to_hls
from functools import partial
import numpy as np
import pandas as pd
import matplotlib as mpl
from matplotlib.collections import PatchCollection
import matplotlib.patches as Patches
import matplotlib.pyplot as plt
fro... | null |
171,784 | from textwrap import dedent
from numbers import Number
import warnings
from colorsys import rgb_to_hls
from functools import partial
import numpy as np
import pandas as pd
import matplotlib as mpl
from matplotlib.collections import PatchCollection
import matplotlib.patches as Patches
import matplotlib.pyplot as plt
fro... | null |
171,785 | from textwrap import dedent
from numbers import Number
import warnings
from colorsys import rgb_to_hls
from functools import partial
import numpy as np
import pandas as pd
import matplotlib as mpl
from matplotlib.collections import PatchCollection
import matplotlib.patches as Patches
import matplotlib.pyplot as plt
fro... | null |
171,786 | from __future__ import annotations
import re
from copy import copy
from collections.abc import Sequence
from dataclasses import dataclass
from functools import partial
from typing import Any, Callable, Tuple, Optional, ClassVar
import numpy as np
import matplotlib as mpl
from matplotlib.ticker import (
Locator,
... | null |
171,787 | from __future__ import annotations
import re
from copy import copy
from collections.abc import Sequence
from dataclasses import dataclass
from functools import partial
from typing import Any, Callable, Tuple, Optional, ClassVar
import numpy as np
import matplotlib as mpl
from matplotlib.ticker import (
Locator,
... | null |
171,788 | from __future__ import annotations
import re
from copy import copy
from collections.abc import Sequence
from dataclasses import dataclass
from functools import partial
from typing import Any, Callable, Tuple, Optional, ClassVar
import numpy as np
import matplotlib as mpl
from matplotlib.ticker import (
Locator,
... | null |
171,789 | from __future__ import annotations
import re
from copy import copy
from collections.abc import Sequence
from dataclasses import dataclass
from functools import partial
from typing import Any, Callable, Tuple, Optional, ClassVar
import numpy as np
import matplotlib as mpl
from matplotlib.ticker import (
Locator,
... | null |
171,790 | from __future__ import annotations
import re
from copy import copy
from collections.abc import Sequence
from dataclasses import dataclass
from functools import partial
from typing import Any, Callable, Tuple, Optional, ClassVar
import numpy as np
import matplotlib as mpl
from matplotlib.ticker import (
Locator,
... | null |
171,791 | from __future__ import annotations
import re
from copy import copy
from collections.abc import Sequence
from dataclasses import dataclass
from functools import partial
from typing import Any, Callable, Tuple, Optional, ClassVar
import numpy as np
import matplotlib as mpl
from matplotlib.ticker import (
Locator,
... | null |
171,792 | from __future__ import annotations
import warnings
from collections import UserString
from numbers import Number
from datetime import datetime
import numpy as np
import pandas as pd
from seaborn.external.version import Version
from typing import TYPE_CHECKING
def variable_type(
vector: Series,
boolean_type: Lit... | 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 ... |
171,793 | from __future__ import annotations
import io
import os
import re
import sys
import inspect
import itertools
import textwrap
from contextlib import contextmanager
from collections import abc
from collections.abc import Callable, Generator
from typing import Any, List, Optional, cast
from cycler import cycler
import pand... | Temporarily modify specifc matplotlib rcParams. |
171,794 | from __future__ import annotations
import io
import os
import re
import sys
import inspect
import itertools
import textwrap
from contextlib import contextmanager
from collections import abc
from collections.abc import Callable, Generator
from typing import Any, List, Optional, cast
from cycler import cycler
import pand... | Decorator function for giving Plot a useful signature. Currently this mostly saves us some duplicated typing, but we would like eventually to have a way of registering new semantic properties, at which point dynamic signature generation would become more important. |
171,795 | import os
import re
import inspect
import warnings
import colorsys
from contextlib import contextmanager
from urllib.request import urlopen, urlretrieve
import numpy as np
import pandas as pd
import matplotlib as mpl
from matplotlib.colors import to_rgb
import matplotlib.pyplot as plt
from matplotlib.cbook import norma... | Convert intervals to error arguments relative to plot heights. Parameters ---------- cis : 2 x n sequence sequence of confidence interval limits heights : n sequence sequence of plot heights Returns ------- errsize : 2 x n array sequence of error size relative to height values in correct format as argument for plt.bar |
171,796 | import os
import re
import inspect
import warnings
import colorsys
from contextlib import contextmanager
from urllib.request import urlopen, urlretrieve
import numpy as np
import pandas as pd
import matplotlib as mpl
from matplotlib.colors import to_rgb
import matplotlib.pyplot as plt
from matplotlib.cbook import norma... | Compute the quantile function of the standard normal distribution. This wrapper exists because we are dropping scipy as a mandatory dependency but statistics.NormalDist was added to the standard library in 3.8. |
171,797 | import os
import re
import inspect
import warnings
import colorsys
from contextlib import contextmanager
from urllib.request import urlopen, urlretrieve
import numpy as np
import pandas as pd
import matplotlib as mpl
from matplotlib.colors import to_rgb
import matplotlib.pyplot as plt
from matplotlib.cbook import norma... | Force draw of a matplotlib figure, accounting for back-compat. |
171,798 | import os
import re
import inspect
import warnings
import colorsys
from contextlib import contextmanager
from urllib.request import urlopen, urlretrieve
import numpy as np
import pandas as pd
import matplotlib as mpl
from matplotlib.colors import to_rgb
import matplotlib.pyplot as plt
from matplotlib.cbook import norma... | Return a fully saturated color with the same hue. Parameters ---------- color : matplotlib color hex, rgb-tuple, or html color name Returns ------- new_color : rgb tuple saturated color code in RGB tuple representation |
171,799 | import os
import re
import inspect
import warnings
import colorsys
from contextlib import contextmanager
from urllib.request import urlopen, urlretrieve
import numpy as np
import pandas as pd
import matplotlib as mpl
from matplotlib.colors import to_rgb
import matplotlib.pyplot as plt
from matplotlib.cbook import norma... | Grab current axis and label it. DEPRECATED: will be removed in a future version. |
171,800 | import os
import re
import inspect
import warnings
import colorsys
from contextlib import contextmanager
from urllib.request import urlopen, urlretrieve
import numpy as np
import pandas as pd
import matplotlib as mpl
from matplotlib.colors import to_rgb
import matplotlib.pyplot as plt
from matplotlib.cbook import norma... | Remove the top and right spines from plot(s). fig : matplotlib figure, optional Figure to despine all axes of, defaults to the current figure. ax : matplotlib axes, optional Specific axes object to despine. Ignored if fig is provided. top, right, left, bottom : boolean, optional If True, remove that spine. offset : int... |
171,801 | import os
import re
import inspect
import warnings
import colorsys
from contextlib import contextmanager
from urllib.request import urlopen, urlretrieve
import numpy as np
import pandas as pd
import matplotlib as mpl
from matplotlib.colors import to_rgb
import matplotlib.pyplot as plt
from matplotlib.cbook import norma... | Recreate a plot's legend at a new location. The name is a slight misnomer. Matplotlib legends do not expose public control over their position parameters. So this function creates a new legend, copying over the data from the original object, which is then removed. Parameters ---------- obj : the object with the plot Th... |
171,802 | import os
import re
import inspect
import warnings
import colorsys
from contextlib import contextmanager
from urllib.request import urlopen, urlretrieve
import numpy as np
import pandas as pd
import matplotlib as mpl
from matplotlib.colors import to_rgb
import matplotlib.pyplot as plt
from matplotlib.cbook import norma... | 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... |
171,803 | import os
import re
import inspect
import warnings
import colorsys
from contextlib import contextmanager
from urllib.request import urlopen, urlretrieve
import numpy as np
import pandas as pd
import matplotlib as mpl
from matplotlib.colors import to_rgb
import matplotlib.pyplot as plt
from matplotlib.cbook import norma... | Return booleans for whether the x and y ticklabels on an Axes overlap. Parameters ---------- ax : matplotlib Axes Returns ------- x_overlap, y_overlap : booleans True when the labels on that axis overlap. |
171,804 | import os
import re
import inspect
import warnings
import colorsys
from contextlib import contextmanager
from urllib.request import urlopen, urlretrieve
import numpy as np
import pandas as pd
import matplotlib as mpl
from matplotlib.colors import to_rgb
import matplotlib.pyplot as plt
from matplotlib.cbook import norma... | Return levels and formatted levels for brief numeric legends. |
171,805 | import os
import re
import inspect
import warnings
import colorsys
from contextlib import contextmanager
from urllib.request import urlopen, urlretrieve
import numpy as np
import pandas as pd
import matplotlib as mpl
from matplotlib.colors import to_rgb
import matplotlib.pyplot as plt
from matplotlib.cbook import norma... | Calculate the relative luminance of a color according to W3C standards Parameters ---------- color : matplotlib color or sequence of matplotlib colors Hex code, rgb-tuple, or html color name. Returns ------- luminance : float(s) between 0 and 1 |
171,806 | import os
import re
import inspect
import warnings
import colorsys
from contextlib import contextmanager
from urllib.request import urlopen, urlretrieve
import numpy as np
import pandas as pd
import matplotlib as mpl
from matplotlib.colors import to_rgb
import matplotlib.pyplot as plt
from matplotlib.cbook import norma... | Make invisible-handle "subtitles" entries look more like titles. Note: This function is not part of the public API and may be changed or removed. |
171,807 | import os
import re
import inspect
import warnings
import colorsys
from contextlib import contextmanager
from urllib.request import urlopen, urlretrieve
import numpy as np
import pandas as pd
import matplotlib as mpl
from matplotlib.colors import to_rgb
import matplotlib.pyplot as plt
from matplotlib.cbook import norma... | Context manager for preventing rc-controlled auto-layout behavior. |
171,808 | import copy
from textwrap import dedent
import warnings
import numpy as np
import pandas as pd
import matplotlib as mpl
import matplotlib.pyplot as plt
from . import utils
from . import algorithms as algo
from .axisgrid import FacetGrid, _facet_docs
def regplot(
data=None, *, x=None, y=None,
x_estimator=None, x... | null |
171,809 | import numbers
import numpy as np
import warnings
def _structured_bootstrap(args, n_boot, units, func, func_kwargs, integers):
"""Resample units instead of datapoints."""
unique_units = np.unique(units)
n_units = len(unique_units)
args = [[a[units == unit] for unit in unique_units] for a in args]
bo... | 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 ar... |
171,810 | from inspect import signature
def signature(obj: Callable[..., Any], *, follow_wrapped: bool = ...) -> Signature: ...
The provided code snippet includes necessary dependencies for implementing the `share_init_params_with_map` function. Write a Python function `def share_init_params_with_map(cls)` to solve the followi... | Make cls.map a classmethod with same signature as cls.__init__. |
171,811 | import warnings
import itertools
from copy import copy
from functools import partial
from collections import UserString
from collections.abc import Iterable, Sequence, Mapping
from numbers import Number
from datetime import datetime
import numpy as np
import pandas as pd
import matplotlib as mpl
from ._decorators impor... | 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 :... |
171,812 | import warnings
import itertools
from copy import copy
from functools import partial
from collections import UserString
from collections.abc import Iterable, Sequence, Mapping
from numbers import Number
from datetime import datetime
import numpy as np
import pandas as pd
import matplotlib as mpl
from ._decorators impor... | 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 remaind... |
171,813 | import warnings
import itertools
from copy import copy
from functools import partial
from collections import UserString
from collections.abc import Iterable, Sequence, Mapping
from numbers import Number
from datetime import datetime
import numpy as np
import pandas as pd
import matplotlib as mpl
from ._decorators impor... | 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. |
171,814 | import warnings
import itertools
from copy import copy
from functools import partial
from collections import UserString
from collections.abc import Iterable, Sequence, Mapping
from numbers import Number
from datetime import datetime
import numpy as np
import pandas as pd
import matplotlib as mpl
from ._decorators impor... | 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 ----... |
171,815 | import functools
import matplotlib as mpl
from cycler import cycler
from . import palettes
The provided code snippet includes necessary dependencies for implementing the `reset_defaults` function. Write a Python function `def reset_defaults()` to solve the following problem:
Restore all RC params to default settings.
... | Restore all RC params to default settings. |
171,816 | import functools
import matplotlib as mpl
from cycler import cycler
from . import palettes
The provided code snippet includes necessary dependencies for implementing the `reset_orig` function. Write a Python function `def reset_orig()` to solve the following problem:
Restore all RC params to original settings (respect... | Restore all RC params to original settings (respects custom rc). |
171,817 | import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import LinearSegmentedColormap
try:
from ipywidgets import interact, FloatSlider, IntSlider
except ImportError:
def interact(f):
msg = "Interactive palettes require `ipywidgets`, which is not installed."
raise ImportError(... | Select a palette from the ColorBrewer set. These palettes are built into matplotlib and can be used by name in many seaborn functions, or by passing the object returned by this function. Parameters ---------- data_type : {'sequential', 'diverging', 'qualitative'} This describes the kind of data you want to visualize. S... |
171,818 | import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import LinearSegmentedColormap
try:
from ipywidgets import interact, FloatSlider, IntSlider
except ImportError:
def interact(f):
msg = "Interactive palettes require `ipywidgets`, which is not installed."
raise ImportError(... | Launch an interactive widget to create a dark sequential palette. This corresponds with the :func:`dark_palette` function. This kind of palette is good for data that range between relatively uninteresting low values and interesting high values. Requires IPython 2+ and must be used in the notebook. Parameters ----------... |
171,819 | import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import LinearSegmentedColormap
try:
from ipywidgets import interact, FloatSlider, IntSlider
except ImportError:
def interact(f):
msg = "Interactive palettes require `ipywidgets`, which is not installed."
raise ImportError(... | Launch an interactive widget to create a light sequential palette. This corresponds with the :func:`light_palette` function. This kind of palette is good for data that range between relatively uninteresting low values and interesting high values. Requires IPython 2+ and must be used in the notebook. Parameters --------... |
171,820 | import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import LinearSegmentedColormap
try:
from ipywidgets import interact, FloatSlider, IntSlider
except ImportError:
def interact(f):
msg = "Interactive palettes require `ipywidgets`, which is not installed."
raise ImportError(... | Launch an interactive widget to choose a diverging color palette. This corresponds with the :func:`diverging_palette` function. This kind of palette is good for data that range between interesting low values and interesting high values with a meaningful midpoint. (For example, change scores relative to some baseline va... |
171,821 | import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import LinearSegmentedColormap
try:
from ipywidgets import interact, FloatSlider, IntSlider
except ImportError:
def interact(f):
msg = "Interactive palettes require `ipywidgets`, which is not installed."
raise ImportError(... | Launch an interactive widget to create a sequential cubehelix palette. This corresponds with the :func:`cubehelix_palette` function. This kind of palette is good for data that range between relatively uninteresting low values and interesting high values. The cubehelix system allows the palette to have more hue variance... |
171,822 | import numpy as np
import matplotlib as mpl
from seaborn.external.version import Version
The provided code snippet includes necessary dependencies for implementing the `MarkerStyle` function. Write a Python function `def MarkerStyle(marker=None, fillstyle=None)` to solve the following problem:
Allow MarkerStyle to acc... | Allow MarkerStyle to accept a MarkerStyle object as parameter. Supports matplotlib < 3.3.0 https://github.com/matplotlib/matplotlib/pull/16692 |
171,823 | import numpy as np
import matplotlib as mpl
from seaborn.external.version import Version
The provided code snippet includes necessary dependencies for implementing the `norm_from_scale` function. Write a Python function `def norm_from_scale(scale, norm)` to solve the following problem:
Produce a Normalize object given... | Produce a Normalize object given a Scale and min/max domain limits. |
171,824 | import numpy as np
import matplotlib as mpl
from seaborn.external.version import Version
class Version(_BaseVersion):
_regex = re.compile(r"^\s*" + VERSION_PATTERN + r"\s*$", re.VERBOSE | re.IGNORECASE)
def __init__(self, version: str) -> None:
# Validate the version and parse it into pieces
... | Backwards compatability for creation of independent scales. Matplotlib scales require an Axis object for instantiation on < 3.4. But the axis is not used, aside from extraction of the axis_name in LogScale. |
171,825 | import numpy as np
import matplotlib as mpl
from seaborn.external.version import Version
class Version(_BaseVersion):
_regex = re.compile(r"^\s*" + VERSION_PATTERN + r"\s*$", re.VERBOSE | re.IGNORECASE)
def __init__(self, version: str) -> None:
# Validate the version and parse it into pieces
... | Handle backwards compatability with setting matplotlib scale. |
171,826 | import numpy as np
import matplotlib as mpl
from seaborn.external.version import Version
The provided code snippet includes necessary dependencies for implementing the `register_colormap` function. Write a Python function `def register_colormap(name, cmap)` to solve the following problem:
Handle changes to matplotlib ... | Handle changes to matplotlib colormap interface in 3.6. |
171,827 | import numpy as np
import matplotlib as mpl
from seaborn.external.version import Version
The provided code snippet includes necessary dependencies for implementing the `set_layout_engine` function. Write a Python function `def set_layout_engine(fig, engine)` to solve the following problem:
Handle changes to auto layou... | Handle changes to auto layout engine interface in 3.6 |
171,828 | import numpy as np
import matplotlib as mpl
from seaborn.external.version import Version
class Version(_BaseVersion):
_regex = re.compile(r"^\s*" + VERSION_PATTERN + r"\s*$", re.VERBOSE | re.IGNORECASE)
def __init__(self, version: str) -> None:
# Validate the version and parse it into pieces
... | Handle changes to post-hoc axis sharing. |
171,829 | from __future__ import annotations
from dataclasses import dataclass, fields, field
import textwrap
from typing import Any, Callable, Union
from collections.abc import Generator
import numpy as np
import pandas as pd
import matplotlib as mpl
from numpy import ndarray
from pandas import DataFrame
from matplotlib.artist ... | null |
171,830 | from __future__ import annotations
from dataclasses import dataclass, fields, field
import textwrap
from typing import Any, Callable, Union
from collections.abc import Generator
import numpy as np
import pandas as pd
import matplotlib as mpl
from numpy import ndarray
from pandas import DataFrame
from matplotlib.artist ... | 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 ma... |
171,831 | from __future__ import annotations
from dataclasses import dataclass, fields, field
import textwrap
from typing import Any, Callable, Union
from collections.abc import Generator
import numpy as np
import pandas as pd
import matplotlib as mpl
from numpy import ndarray
from pandas import DataFrame
from matplotlib.artist ... | null |
171,832 | import warnings
import matplotlib as mpl
from matplotlib.collections import LineCollection
import matplotlib.pyplot as plt
from matplotlib import gridspec
import numpy as np
import pandas as pd
from . import cm
from .axisgrid import Grid
from ._compat import get_colormap
from .utils import (
despine,
axis_tickl... | Convert a pandas index or multiindex to an axis label. |
171,833 | import warnings
import matplotlib as mpl
from matplotlib.collections import LineCollection
import matplotlib.pyplot as plt
from matplotlib import gridspec
import numpy as np
import pandas as pd
from . import cm
from .axisgrid import Grid
from ._compat import get_colormap
from .utils import (
despine,
axis_tickl... | Convert a pandas index or multiindex into ticklabels. |
171,834 | import warnings
import matplotlib as mpl
from matplotlib.collections import LineCollection
import matplotlib.pyplot as plt
from matplotlib import gridspec
import numpy as np
import pandas as pd
from . import cm
from .axisgrid import Grid
from ._compat import get_colormap
from .utils import (
despine,
axis_tickl... | Convert either a list of colors or nested lists of colors to RGB. |
171,835 | import warnings
import matplotlib as mpl
from matplotlib.collections import LineCollection
import matplotlib.pyplot as plt
from matplotlib import gridspec
import numpy as np
import pandas as pd
from . import cm
from .axisgrid import Grid
from ._compat import get_colormap
from .utils import (
despine,
axis_tickl... | Ensure that data and mask are compatible and add missing values. Values will be plotted for cells where ``mask`` is ``False``. ``data`` is expected to be a DataFrame; ``mask`` can be an array or a DataFrame. |
171,836 | import warnings
import matplotlib as mpl
from matplotlib.collections import LineCollection
import matplotlib.pyplot as plt
from matplotlib import gridspec
import numpy as np
import pandas as pd
from . import cm
from .axisgrid import Grid
from ._compat import get_colormap
from .utils import (
despine,
axis_tickl... | 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``. ... |
171,837 | import warnings
import matplotlib as mpl
from matplotlib.collections import LineCollection
import matplotlib.pyplot as plt
from matplotlib import gridspec
import numpy as np
import pandas as pd
from . import cm
from .axisgrid import Grid
from ._compat import get_colormap
from .utils import (
despine,
axis_tickl... | 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 col... |
171,838 | import warnings
import matplotlib as mpl
from matplotlib.collections import LineCollection
import matplotlib.pyplot as plt
from matplotlib import gridspec
import numpy as np
import pandas as pd
from . import cm
from .axisgrid import Grid
from ._compat import get_colormap
from .utils import (
despine,
axis_tickl... | 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 rectang... |
171,839 | import colorsys
from itertools import cycle
import numpy as np
import matplotlib as mpl
from .external import husl
from .utils import desaturate, get_color_cycle
from .colors import xkcd_rgb, crayons
from ._compat import get_colormap
class Image:
"""
This class represents an image object. To create
:py:cl... | Simplify the rich display of matplotlib color maps in a notebook. |
171,840 | import colorsys
from itertools import cycle
import numpy as np
import matplotlib as mpl
from .external import husl
from .utils import desaturate, get_color_cycle
from .colors import xkcd_rgb, crayons
from ._compat import get_colormap
def color_palette(palette=None, n_colors=None, desat=None, as_cmap=False):
"""Retu... | Make a palette with color names from the xkcd color survey. See xkcd for the full list of colors: https://xkcd.com/color/rgb/ This is just a simple wrapper around the `seaborn.xkcd_rgb` dictionary. Parameters ---------- colors : list of strings List of keys in the `seaborn.xkcd_rgb` dictionary. Returns ------- palette ... |
171,841 | import colorsys
from itertools import cycle
import numpy as np
import matplotlib as mpl
from .external import husl
from .utils import desaturate, get_color_cycle
from .colors import xkcd_rgb, crayons
from ._compat import get_colormap
def color_palette(palette=None, n_colors=None, desat=None, as_cmap=False):
"""Retu... | 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 --... |
171,842 | import collections
import itertools
import re
from typing import Callable, Optional, SupportsInt, Tuple, Union
Union: _SpecialForm = ...
Optional: _SpecialForm = ...
class SupportsInt(Protocol, metaclass=ABCMeta):
def __int__(self) -> int: ...
class Tuple(BaseTypingInstance):
def _is_homo... | null |
171,843 | import collections
import itertools
import re
from typing import Callable, Optional, SupportsInt, Tuple, Union
LocalType = Union[
NegativeInfinityType,
Tuple[
Union[
SubLocalType,
Tuple[SubLocalType, str],
Tuple[NegativeInfinityType, SubLocalType],
],
... | Takes a string like abc.1.twelve and turns it into ("abc", 1, "twelve"). |
171,844 | import collections
import itertools
import re
from typing import Callable, Optional, SupportsInt, Tuple, Union
Infinity = InfinityType()
NegativeInfinity = NegativeInfinityType()
PrePostDevType = Union[InfiniteTypes, Tuple[str, int]]
SubLocalType = Union[InfiniteTypes, int, str]
LocalType = Union[
NegativeInfinityT... | null |
171,845 | import operator
import math
def husl_to_rgb(h, s, l):
return lch_to_rgb(*husl_to_lch([h, s, l]))
def rgb_to_hex(triple):
[r, g, b] = triple
return '#%02x%02x%02x' % tuple(rgb_prepare([r, g, b]))
def husl_to_hex(h, s, l):
return rgb_to_hex(husl_to_rgb(h, s, l)) | null |
171,846 | import operator
import math
def rgb_to_husl(r, g, b):
return lch_to_husl(rgb_to_lch(r, g, b))
def hex_to_rgb(hex):
if hex.startswith('#'):
hex = hex[1:]
r = int(hex[0:2], 16) / 255.0
g = int(hex[2:4], 16) / 255.0
b = int(hex[4:6], 16) / 255.0
return [r, g, b]
def hex_to_husl(hex):
r... | null |
171,847 | import operator
import math
def huslp_to_rgb(h, s, l):
return lch_to_rgb(*huslp_to_lch([h, s, l]))
def rgb_to_hex(triple):
[r, g, b] = triple
return '#%02x%02x%02x' % tuple(rgb_prepare([r, g, b]))
def huslp_to_hex(h, s, l):
return rgb_to_hex(huslp_to_rgb(h, s, l)) | null |
171,848 | import operator
import math
def rgb_to_huslp(r, g, b):
return lch_to_huslp(rgb_to_lch(r, g, b))
def hex_to_rgb(hex):
if hex.startswith('#'):
hex = hex[1:]
r = int(hex[0:2], 16) / 255.0
g = int(hex[2:4], 16) / 255.0
b = int(hex[4:6], 16) / 255.0
return [r, g, b]
def hex_to_huslp(hex):
... | null |
171,849 | import inspect
import textwrap
import re
import pydoc
from warnings import warn
from collections import namedtuple
from collections.abc import Callable, Mapping
import copy
import sys
The provided code snippet includes necessary dependencies for implementing the `strip_blank_lines` function. Write a Python function `d... | Remove leading and trailing blank lines from a list of lines |
171,850 | import inspect
import textwrap
import re
import pydoc
from warnings import warn
from collections import namedtuple
from collections.abc import Callable, Mapping
import copy
import sys
def indent(str, indent=4):
indent_str = ' '*indent
if str is None:
return indent_str
lines = str.split('\n')
re... | null |
171,851 | import inspect
import textwrap
import re
import pydoc
from warnings import warn
from collections import namedtuple
from collections.abc import Callable, Mapping
import copy
import sys
The provided code snippet includes necessary dependencies for implementing the `dedent_lines` function. Write a Python function `def de... | Deindent a list of lines maximally |
171,852 | import inspect
import textwrap
import re
import pydoc
from warnings import warn
from collections import namedtuple
from collections.abc import Callable, Mapping
import copy
import sys
def header(text, style='-'):
return text + '\n' + style*len(text) + '\n' | null |
171,853 | import sys
import os
The provided code snippet includes necessary dependencies for implementing the `_get_win_folder_from_registry` function. Write a Python function `def _get_win_folder_from_registry(csidl_name)` to solve the following problem:
This is a fallback technique at best. I'm not sure if using the registry ... | This is a fallback technique at best. I'm not sure if using the registry for this guarantees us the correct answer for all CSIDL_* names. |
171,854 | import sys
import os
unicode = str
def _get_win_folder_with_pywin32(csidl_name):
from win32com.shell import shellcon, shell
dir = shell.SHGetFolderPath(0, getattr(shellcon, csidl_name), 0, 0)
# Try to make this a unicode path because SHGetFolderPath does
# not return unicode strings when there is unico... | null |
171,857 | import warnings
import numpy as np
import pandas as pd
import matplotlib as mpl
import matplotlib.pyplot as plt
from ._oldcore import (
VectorPlotter,
)
from .utils import (
locator_to_legend_entries,
adjust_legend_subtitles,
_default_color,
_deprecate_ci,
)
from ._statistics import EstimateAggregat... | null |
171,858 | import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
def urlopen(
url: Union[Request, _string],
data: Optional[_string] = ...,
timeout: Optional[float] = ...,
cafile: Optional[_string] = ...,
capath: Optional[_string] = ...,
cadefault: b... | Who's a good boy? |
171,859 | from numbers import Number
import numpy as np
import pandas as pd
from .algorithms import bootstrap
from .utils import _check_argument
The provided code snippet includes necessary dependencies for implementing the `_percentile_interval` function. Write a Python function `def _percentile_interval(data, width)` to solve... | Return a percentile interval from data of a given width. |
171,860 | from numbers import Number
import numpy as np
import pandas as pd
from .algorithms import bootstrap
from .utils import _check_argument
class Number(metaclass=ABCMeta):
def __hash__(self) -> int: ...
def _check_argument(param, options, value):
"""Raise if value for param is not in options."""
if value not ... | Check type and value of errorbar argument and assign default level. |
171,861 | from __future__ import annotations
from itertools import product
from inspect import signature
import warnings
from textwrap import dedent
import numpy as np
import pandas as pd
import matplotlib as mpl
import matplotlib.pyplot as plt
from ._oldcore import VectorPlotter, variable_type, categorical_order
from ._compat i... | 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 sh... |
171,862 | from __future__ import annotations
from itertools import product
from inspect import signature
import warnings
from textwrap import dedent
import numpy as np
import pandas as pd
import matplotlib as mpl
import matplotlib.pyplot as plt
from ._oldcore import VectorPlotter, variable_type, categorical_order
from ._compat i... | null |
171,863 | OPT_HIDE = "hide"
OPT_STOP_EXCEPTIONS = "stopatexceptions"
import win32api
import win32ui
def DoGetOption(optsDict, optName, default):
optsDict[optName] = win32ui.GetProfileVal("Debugger Options", optName, default)
def LoadDebuggerOptions():
opts = {}
DoGetOption(opts, OPT_HIDE, 0)
DoGetOption(opts, OP... | null |
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