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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
mwaskom/seaborn
|
a47b97e4b98c809db55cbd283de21acba89fe186
|
seaborn/_core/data.py
|
PlotData.__init__
|
(
self,
data: DataSource,
variables: dict[str, VariableSpec],
)
| 49 | 64 |
def __init__(
self,
data: DataSource,
variables: dict[str, VariableSpec],
):
frame, names, ids = self._assign_variables(data, variables)
self.frame = frame
self.names = names
self.ids = ids
self.frames = {} # TODO this is a hack, remove
self.source_data = data
self.source_vars = variables
|
https://github.com/mwaskom/seaborn/blob/a47b97e4b98c809db55cbd283de21acba89fe186/project26/seaborn/_core/data.py#L49-L64
| 26 |
[
0,
5,
6,
7,
8,
9,
10,
11,
12,
13,
14,
15
] | 75 |
[] | 0 | false | 98.913043 | 16 | 1 | 100 | 0 |
def __init__(
self,
data: DataSource,
variables: dict[str, VariableSpec],
):
frame, names, ids = self._assign_variables(data, variables)
self.frame = frame
self.names = names
self.ids = ids
self.frames = {} # TODO this is a hack, remove
self.source_data = data
self.source_vars = variables
| 19,295 |
|||
mwaskom/seaborn
|
a47b97e4b98c809db55cbd283de21acba89fe186
|
seaborn/_core/data.py
|
PlotData.__contains__
|
(self, key: str)
|
return key in self.frame
|
Boolean check on whether a variable is defined in this dataset.
|
Boolean check on whether a variable is defined in this dataset.
| 66 | 70 |
def __contains__(self, key: str) -> bool:
"""Boolean check on whether a variable is defined in this dataset."""
if self.frame is None:
return any(key in df for df in self.frames.values())
return key in self.frame
|
https://github.com/mwaskom/seaborn/blob/a47b97e4b98c809db55cbd283de21acba89fe186/project26/seaborn/_core/data.py#L66-L70
| 26 |
[
0,
1,
2,
4
] | 80 |
[
3
] | 20 | false | 98.913043 | 5 | 2 | 80 | 1 |
def __contains__(self, key: str) -> bool:
if self.frame is None:
return any(key in df for df in self.frames.values())
return key in self.frame
| 19,296 |
mwaskom/seaborn
|
a47b97e4b98c809db55cbd283de21acba89fe186
|
seaborn/_core/data.py
|
PlotData.join
|
(
self,
data: DataSource,
variables: dict[str, VariableSpec] | None,
)
|
return new
|
Add, replace, or drop variables and return as a new dataset.
|
Add, replace, or drop variables and return as a new dataset.
| 72 | 117 |
def join(
self,
data: DataSource,
variables: dict[str, VariableSpec] | None,
) -> PlotData:
"""Add, replace, or drop variables and return as a new dataset."""
# Inherit the original source of the upsteam data by default
if data is None:
data = self.source_data
# TODO allow `data` to be a function (that is called on the source data?)
if not variables:
variables = self.source_vars
# Passing var=None implies that we do not want that variable in this layer
disinherit = [k for k, v in variables.items() if v is None]
# Create a new dataset with just the info passed here
new = PlotData(data, variables)
# -- Update the inherited DataSource with this new information
drop_cols = [k for k in self.frame if k in new.frame or k in disinherit]
parts = [self.frame.drop(columns=drop_cols), new.frame]
# Because we are combining distinct columns, this is perhaps more
# naturally thought of as a "merge"/"join". But using concat because
# some simple testing suggests that it is marginally faster.
frame = pd.concat(parts, axis=1, sort=False, copy=False)
names = {k: v for k, v in self.names.items() if k not in disinherit}
names.update(new.names)
ids = {k: v for k, v in self.ids.items() if k not in disinherit}
ids.update(new.ids)
new.frame = frame
new.names = names
new.ids = ids
# Multiple chained operations should always inherit from the original object
new.source_data = self.source_data
new.source_vars = self.source_vars
return new
|
https://github.com/mwaskom/seaborn/blob/a47b97e4b98c809db55cbd283de21acba89fe186/project26/seaborn/_core/data.py#L72-L117
| 26 |
[
0,
6,
7,
8,
9,
10,
11,
12,
13,
14,
15,
16,
17,
18,
19,
20,
21,
22,
23,
24,
25,
26,
27,
28,
29,
30,
31,
32,
33,
34,
35,
36,
37,
38,
39,
40,
41,
42,
43,
44,
45
] | 89.130435 |
[] | 0 | false | 98.913043 | 46 | 6 | 100 | 1 |
def join(
self,
data: DataSource,
variables: dict[str, VariableSpec] | None,
) -> PlotData:
# Inherit the original source of the upsteam data by default
if data is None:
data = self.source_data
# TODO allow `data` to be a function (that is called on the source data?)
if not variables:
variables = self.source_vars
# Passing var=None implies that we do not want that variable in this layer
disinherit = [k for k, v in variables.items() if v is None]
# Create a new dataset with just the info passed here
new = PlotData(data, variables)
# -- Update the inherited DataSource with this new information
drop_cols = [k for k in self.frame if k in new.frame or k in disinherit]
parts = [self.frame.drop(columns=drop_cols), new.frame]
# Because we are combining distinct columns, this is perhaps more
# naturally thought of as a "merge"/"join". But using concat because
# some simple testing suggests that it is marginally faster.
frame = pd.concat(parts, axis=1, sort=False, copy=False)
names = {k: v for k, v in self.names.items() if k not in disinherit}
names.update(new.names)
ids = {k: v for k, v in self.ids.items() if k not in disinherit}
ids.update(new.ids)
new.frame = frame
new.names = names
new.ids = ids
# Multiple chained operations should always inherit from the original object
new.source_data = self.source_data
new.source_vars = self.source_vars
return new
| 19,297 |
mwaskom/seaborn
|
a47b97e4b98c809db55cbd283de21acba89fe186
|
seaborn/_core/data.py
|
PlotData._assign_variables
|
(
self,
data: DataSource,
variables: dict[str, VariableSpec],
)
|
return frame, names, ids
|
Assign values for plot variables given long-form data and/or vector inputs.
Parameters
----------
data
Input data where variable names map to vector values.
variables
Keys are names of plot variables (x, y, ...) each value is one of:
- name of a column (or index level, or dictionary entry) in `data`
- vector in any format that can construct a :class:`pandas.DataFrame`
Returns
-------
frame
Table mapping seaborn variables (x, y, color, ...) to data vectors.
names
Keys are defined seaborn variables; values are names inferred from
the inputs (or None when no name can be determined).
ids
Like the `names` dict, but `None` values are replaced by the `id()`
of the data object that defined the variable.
Raises
------
ValueError
When variables are strings that don't appear in `data`, or when they are
non-indexed vector datatypes that have a different length from `data`.
|
Assign values for plot variables given long-form data and/or vector inputs.
| 119 | 260 |
def _assign_variables(
self,
data: DataSource,
variables: dict[str, VariableSpec],
) -> tuple[DataFrame, dict[str, str | None], dict[str, str | int]]:
"""
Assign values for plot variables given long-form data and/or vector inputs.
Parameters
----------
data
Input data where variable names map to vector values.
variables
Keys are names of plot variables (x, y, ...) each value is one of:
- name of a column (or index level, or dictionary entry) in `data`
- vector in any format that can construct a :class:`pandas.DataFrame`
Returns
-------
frame
Table mapping seaborn variables (x, y, color, ...) to data vectors.
names
Keys are defined seaborn variables; values are names inferred from
the inputs (or None when no name can be determined).
ids
Like the `names` dict, but `None` values are replaced by the `id()`
of the data object that defined the variable.
Raises
------
ValueError
When variables are strings that don't appear in `data`, or when they are
non-indexed vector datatypes that have a different length from `data`.
"""
source_data: Mapping | DataFrame
frame: DataFrame
names: dict[str, str | None]
ids: dict[str, str | int]
plot_data = {}
names = {}
ids = {}
given_data = data is not None
if data is not None:
source_data = data
else:
# Data is optional; all variables can be defined as vectors
# But simplify downstream code by always having a usable source data object
source_data = {}
# TODO Generally interested in accepting a generic DataFrame interface
# Track https://data-apis.org/ for development
# Variables can also be extracted from the index of a DataFrame
if isinstance(source_data, pd.DataFrame):
index = source_data.index.to_frame().to_dict("series")
else:
index = {}
for key, val in variables.items():
# Simply ignore variables with no specification
if val is None:
continue
# Try to treat the argument as a key for the data collection.
# But be flexible about what can be used as a key.
# Usually it will be a string, but allow other hashables when
# taking from the main data object. Allow only strings to reference
# fields in the index, because otherwise there is too much ambiguity.
# TODO this will be rendered unnecessary by the following pandas fix:
# https://github.com/pandas-dev/pandas/pull/41283
try:
hash(val)
val_is_hashable = True
except TypeError:
val_is_hashable = False
val_as_data_key = (
# See https://github.com/pandas-dev/pandas/pull/41283
# (isinstance(val, abc.Hashable) and val in source_data)
(val_is_hashable and val in source_data)
or (isinstance(val, str) and val in index)
)
if val_as_data_key:
val = cast(ColumnName, val)
if val in source_data:
plot_data[key] = source_data[val]
elif val in index:
plot_data[key] = index[val]
names[key] = ids[key] = str(val)
elif isinstance(val, str):
# This looks like a column name but, lookup failed.
err = f"Could not interpret value `{val}` for `{key}`. "
if not given_data:
err += "Value is a string, but `data` was not passed."
else:
err += "An entry with this name does not appear in `data`."
raise ValueError(err)
else:
# Otherwise, assume the value somehow represents data
# Ignore empty data structures
if isinstance(val, Sized) and len(val) == 0:
continue
# If vector has no index, it must match length of data table
if isinstance(data, pd.DataFrame) and not isinstance(val, pd.Series):
if isinstance(val, Sized) and len(data) != len(val):
val_cls = val.__class__.__name__
err = (
f"Length of {val_cls} vectors must match length of `data`"
f" when both are used, but `data` has length {len(data)}"
f" and the vector passed to `{key}` has length {len(val)}."
)
raise ValueError(err)
plot_data[key] = val
# Try to infer the original name using pandas-like metadata
if hasattr(val, "name"):
names[key] = ids[key] = str(val.name) # type: ignore # mypy/1424
else:
names[key] = None
ids[key] = id(val)
# Construct a tidy plot DataFrame. This will convert a number of
# types automatically, aligning on index in case of pandas objects
# TODO Note: this fails when variable specs *only* have scalars!
frame = pd.DataFrame(plot_data)
return frame, names, ids
|
https://github.com/mwaskom/seaborn/blob/a47b97e4b98c809db55cbd283de21acba89fe186/project26/seaborn/_core/data.py#L119-L260
| 26 |
[
0,
40,
41,
42,
43,
44,
45,
46,
47,
50,
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54,
55,
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60,
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64,
65,
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69,
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78,
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95,
96,
97,
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112,
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126,
127,
128,
129,
130,
131,
133,
134,
135,
136,
137,
138,
139,
140,
141
] | 61.267606 |
[] | 0 | false | 98.913043 | 142 | 21 | 100 | 28 |
def _assign_variables(
self,
data: DataSource,
variables: dict[str, VariableSpec],
) -> tuple[DataFrame, dict[str, str | None], dict[str, str | int]]:
source_data: Mapping | DataFrame
frame: DataFrame
names: dict[str, str | None]
ids: dict[str, str | int]
plot_data = {}
names = {}
ids = {}
given_data = data is not None
if data is not None:
source_data = data
else:
# Data is optional; all variables can be defined as vectors
# But simplify downstream code by always having a usable source data object
source_data = {}
# TODO Generally interested in accepting a generic DataFrame interface
# Track https://data-apis.org/ for development
# Variables can also be extracted from the index of a DataFrame
if isinstance(source_data, pd.DataFrame):
index = source_data.index.to_frame().to_dict("series")
else:
index = {}
for key, val in variables.items():
# Simply ignore variables with no specification
if val is None:
continue
# Try to treat the argument as a key for the data collection.
# But be flexible about what can be used as a key.
# Usually it will be a string, but allow other hashables when
# taking from the main data object. Allow only strings to reference
# fields in the index, because otherwise there is too much ambiguity.
# TODO this will be rendered unnecessary by the following pandas fix:
# https://github.com/pandas-dev/pandas/pull/41283
try:
hash(val)
val_is_hashable = True
except TypeError:
val_is_hashable = False
val_as_data_key = (
# See https://github.com/pandas-dev/pandas/pull/41283
# (isinstance(val, abc.Hashable) and val in source_data)
(val_is_hashable and val in source_data)
or (isinstance(val, str) and val in index)
)
if val_as_data_key:
val = cast(ColumnName, val)
if val in source_data:
plot_data[key] = source_data[val]
elif val in index:
plot_data[key] = index[val]
names[key] = ids[key] = str(val)
elif isinstance(val, str):
# This looks like a column name but, lookup failed.
err = f"Could not interpret value `{val}` for `{key}`. "
if not given_data:
err += "Value is a string, but `data` was not passed."
else:
err += "An entry with this name does not appear in `data`."
raise ValueError(err)
else:
# Otherwise, assume the value somehow represents data
# Ignore empty data structures
if isinstance(val, Sized) and len(val) == 0:
continue
# If vector has no index, it must match length of data table
if isinstance(data, pd.DataFrame) and not isinstance(val, pd.Series):
if isinstance(val, Sized) and len(data) != len(val):
val_cls = val.__class__.__name__
err = (
f"Length of {val_cls} vectors must match length of `data`"
f" when both are used, but `data` has length {len(data)}"
f" and the vector passed to `{key}` has length {len(val)}."
)
raise ValueError(err)
plot_data[key] = val
# Try to infer the original name using pandas-like metadata
if hasattr(val, "name"):
names[key] = ids[key] = str(val.name) # type: ignore # mypy/1424
else:
names[key] = None
ids[key] = id(val)
# Construct a tidy plot DataFrame. This will convert a number of
# types automatically, aligning on index in case of pandas objects
# TODO Note: this fails when variable specs *only* have scalars!
frame = pd.DataFrame(plot_data)
return frame, names, ids
| 19,298 |
mwaskom/seaborn
|
a47b97e4b98c809db55cbd283de21acba89fe186
|
seaborn/_core/exceptions.py
|
PlotSpecError._during
|
(cls, step: str, var: str = "")
|
return cls(" ".join(message))
|
Initialize the class to report the failure of a specific operation.
|
Initialize the class to report the failure of a specific operation.
| 22 | 32 |
def _during(cls, step: str, var: str = "") -> PlotSpecError:
"""
Initialize the class to report the failure of a specific operation.
"""
message = []
if var:
message.append(f"{step} failed for the `{var}` variable.")
else:
message.append(f"{step} failed.")
message.append("See the traceback above for more information.")
return cls(" ".join(message))
|
https://github.com/mwaskom/seaborn/blob/a47b97e4b98c809db55cbd283de21acba89fe186/project26/seaborn/_core/exceptions.py#L22-L32
| 26 |
[
0,
1,
2,
3,
4,
5,
6,
7,
9,
10
] | 90.909091 |
[
8
] | 9.090909 | false | 90 | 11 | 2 | 90.909091 | 1 |
def _during(cls, step: str, var: str = "") -> PlotSpecError:
message = []
if var:
message.append(f"{step} failed for the `{var}` variable.")
else:
message.append(f"{step} failed.")
message.append("See the traceback above for more information.")
return cls(" ".join(message))
| 19,299 |
mwaskom/seaborn
|
a47b97e4b98c809db55cbd283de21acba89fe186
|
seaborn/_core/scales.py
|
_make_identity_transforms
|
()
|
return identity, identity
| 1,001 | 1,006 |
def _make_identity_transforms() -> TransFuncs:
def identity(x):
return x
return identity, identity
|
https://github.com/mwaskom/seaborn/blob/a47b97e4b98c809db55cbd283de21acba89fe186/project26/seaborn/_core/scales.py#L1001-L1006
| 26 |
[
0,
1,
2,
3,
4,
5
] | 100 |
[] | 0 | true | 90.258449 | 6 | 2 | 100 | 0 |
def _make_identity_transforms() -> TransFuncs:
def identity(x):
return x
return identity, identity
| 19,300 |
||
mwaskom/seaborn
|
a47b97e4b98c809db55cbd283de21acba89fe186
|
seaborn/_core/scales.py
|
_make_logit_transforms
|
(base: float | None = None)
|
return logit, expit
| 1,009 | 1,021 |
def _make_logit_transforms(base: float | None = None) -> TransFuncs:
log, exp = _make_log_transforms(base)
def logit(x):
with np.errstate(invalid="ignore", divide="ignore"):
return log(x) - log(1 - x)
def expit(x):
with np.errstate(invalid="ignore", divide="ignore"):
return exp(x) / (1 + exp(x))
return logit, expit
|
https://github.com/mwaskom/seaborn/blob/a47b97e4b98c809db55cbd283de21acba89fe186/project26/seaborn/_core/scales.py#L1009-L1021
| 26 |
[
0,
1
] | 15.384615 |
[
2,
4,
5,
6,
8,
9,
10,
12
] | 61.538462 | false | 90.258449 | 13 | 5 | 38.461538 | 0 |
def _make_logit_transforms(base: float | None = None) -> TransFuncs:
log, exp = _make_log_transforms(base)
def logit(x):
with np.errstate(invalid="ignore", divide="ignore"):
return log(x) - log(1 - x)
def expit(x):
with np.errstate(invalid="ignore", divide="ignore"):
return exp(x) / (1 + exp(x))
return logit, expit
| 19,301 |
||
mwaskom/seaborn
|
a47b97e4b98c809db55cbd283de21acba89fe186
|
seaborn/_core/scales.py
|
_make_log_transforms
|
(base: float | None = None)
|
return log, exp
| 1,024 | 1,046 |
def _make_log_transforms(base: float | None = None) -> TransFuncs:
fs: TransFuncs
if base is None:
fs = np.log, np.exp
elif base == 2:
fs = np.log2, partial(np.power, 2)
elif base == 10:
fs = np.log10, partial(np.power, 10)
else:
def forward(x):
return np.log(x) / np.log(base)
fs = forward, partial(np.power, base)
def log(x: ArrayLike) -> ArrayLike:
with np.errstate(invalid="ignore", divide="ignore"):
return fs[0](x)
def exp(x: ArrayLike) -> ArrayLike:
with np.errstate(invalid="ignore", divide="ignore"):
return fs[1](x)
return log, exp
|
https://github.com/mwaskom/seaborn/blob/a47b97e4b98c809db55cbd283de21acba89fe186/project26/seaborn/_core/scales.py#L1024-L1046
| 26 |
[
0,
1,
3,
5,
7,
8,
13,
14,
15,
16,
17,
18,
19,
20,
21,
22
] | 69.565217 |
[
4,
6,
10,
11,
12
] | 21.73913 | false | 90.258449 | 23 | 9 | 78.26087 | 0 |
def _make_log_transforms(base: float | None = None) -> TransFuncs:
fs: TransFuncs
if base is None:
fs = np.log, np.exp
elif base == 2:
fs = np.log2, partial(np.power, 2)
elif base == 10:
fs = np.log10, partial(np.power, 10)
else:
def forward(x):
return np.log(x) / np.log(base)
fs = forward, partial(np.power, base)
def log(x: ArrayLike) -> ArrayLike:
with np.errstate(invalid="ignore", divide="ignore"):
return fs[0](x)
def exp(x: ArrayLike) -> ArrayLike:
with np.errstate(invalid="ignore", divide="ignore"):
return fs[1](x)
return log, exp
| 19,302 |
||
mwaskom/seaborn
|
a47b97e4b98c809db55cbd283de21acba89fe186
|
seaborn/_core/scales.py
|
_make_symlog_transforms
|
(c: float = 1, base: float = 10)
|
return symlog, symexp
| 1,049 | 1,066 |
def _make_symlog_transforms(c: float = 1, base: float = 10) -> TransFuncs:
# From https://iopscience.iop.org/article/10.1088/0957-0233/24/2/027001
# Note: currently not using base because we only get
# one parameter from the string, and are using c (this is consistent with d3)
log, exp = _make_log_transforms(base)
def symlog(x):
with np.errstate(invalid="ignore", divide="ignore"):
return np.sign(x) * log(1 + np.abs(np.divide(x, c)))
def symexp(x):
with np.errstate(invalid="ignore", divide="ignore"):
return np.sign(x) * c * (exp(np.abs(x)) - 1)
return symlog, symexp
|
https://github.com/mwaskom/seaborn/blob/a47b97e4b98c809db55cbd283de21acba89fe186/project26/seaborn/_core/scales.py#L1049-L1066
| 26 |
[
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
12,
13,
16,
17
] | 77.777778 |
[
10,
11,
14,
15
] | 22.222222 | false | 90.258449 | 18 | 5 | 77.777778 | 0 |
def _make_symlog_transforms(c: float = 1, base: float = 10) -> TransFuncs:
# From https://iopscience.iop.org/article/10.1088/0957-0233/24/2/027001
# Note: currently not using base because we only get
# one parameter from the string, and are using c (this is consistent with d3)
log, exp = _make_log_transforms(base)
def symlog(x):
with np.errstate(invalid="ignore", divide="ignore"):
return np.sign(x) * log(1 + np.abs(np.divide(x, c)))
def symexp(x):
with np.errstate(invalid="ignore", divide="ignore"):
return np.sign(x) * c * (exp(np.abs(x)) - 1)
return symlog, symexp
| 19,303 |
||
mwaskom/seaborn
|
a47b97e4b98c809db55cbd283de21acba89fe186
|
seaborn/_core/scales.py
|
_make_sqrt_transforms
|
()
|
return sqrt, square
| 1,069 | 1,077 |
def _make_sqrt_transforms() -> TransFuncs:
def sqrt(x):
return np.sign(x) * np.sqrt(np.abs(x))
def square(x):
return np.sign(x) * np.square(x)
return sqrt, square
|
https://github.com/mwaskom/seaborn/blob/a47b97e4b98c809db55cbd283de21acba89fe186/project26/seaborn/_core/scales.py#L1069-L1077
| 26 |
[
0,
1
] | 22.222222 |
[
2,
3,
5,
6,
8
] | 55.555556 | false | 90.258449 | 9 | 3 | 44.444444 | 0 |
def _make_sqrt_transforms() -> TransFuncs:
def sqrt(x):
return np.sign(x) * np.sqrt(np.abs(x))
def square(x):
return np.sign(x) * np.square(x)
return sqrt, square
| 19,304 |
||
mwaskom/seaborn
|
a47b97e4b98c809db55cbd283de21acba89fe186
|
seaborn/_core/scales.py
|
_make_power_transforms
|
(exp: float)
|
return forward, inverse
| 1,080 | 1,088 |
def _make_power_transforms(exp: float) -> TransFuncs:
def forward(x):
return np.sign(x) * np.power(np.abs(x), exp)
def inverse(x):
return np.sign(x) * np.power(np.abs(x), 1 / exp)
return forward, inverse
|
https://github.com/mwaskom/seaborn/blob/a47b97e4b98c809db55cbd283de21acba89fe186/project26/seaborn/_core/scales.py#L1080-L1088
| 26 |
[
0,
1,
2,
3,
4,
5,
7,
8
] | 88.888889 |
[
6
] | 11.111111 | false | 90.258449 | 9 | 3 | 88.888889 | 0 |
def _make_power_transforms(exp: float) -> TransFuncs:
def forward(x):
return np.sign(x) * np.power(np.abs(x), exp)
def inverse(x):
return np.sign(x) * np.power(np.abs(x), 1 / exp)
return forward, inverse
| 19,305 |
||
mwaskom/seaborn
|
a47b97e4b98c809db55cbd283de21acba89fe186
|
seaborn/_core/scales.py
|
_default_spacer
|
(x: Series)
|
return 1
| 1,091 | 1,092 |
def _default_spacer(x: Series) -> float:
return 1
|
https://github.com/mwaskom/seaborn/blob/a47b97e4b98c809db55cbd283de21acba89fe186/project26/seaborn/_core/scales.py#L1091-L1092
| 26 |
[
0,
1
] | 100 |
[] | 0 | true | 90.258449 | 2 | 1 | 100 | 0 |
def _default_spacer(x: Series) -> float:
return 1
| 19,306 |
||
mwaskom/seaborn
|
a47b97e4b98c809db55cbd283de21acba89fe186
|
seaborn/_core/scales.py
|
Scale.__post_init__
|
(self)
| 66 | 70 |
def __post_init__(self):
self._tick_params = None
self._label_params = None
self._legend = None
|
https://github.com/mwaskom/seaborn/blob/a47b97e4b98c809db55cbd283de21acba89fe186/project26/seaborn/_core/scales.py#L66-L70
| 26 |
[
0,
1,
2,
3,
4
] | 100 |
[] | 0 | true | 90.258449 | 5 | 1 | 100 | 0 |
def __post_init__(self):
self._tick_params = None
self._label_params = None
self._legend = None
| 19,307 |
|||
mwaskom/seaborn
|
a47b97e4b98c809db55cbd283de21acba89fe186
|
seaborn/_core/scales.py
|
Scale._get_scale
|
(self, name: str, forward: Callable, inverse: Callable)
|
return InternalScale(name, (forward, inverse))
| 84 | 96 |
def _get_scale(self, name: str, forward: Callable, inverse: Callable):
major_locator, minor_locator = self._get_locators(**self._tick_params)
major_formatter = self._get_formatter(major_locator, **self._label_params)
class InternalScale(mpl.scale.FuncScale):
def set_default_locators_and_formatters(self, axis):
axis.set_major_locator(major_locator)
if minor_locator is not None:
axis.set_minor_locator(minor_locator)
axis.set_major_formatter(major_formatter)
return InternalScale(name, (forward, inverse))
|
https://github.com/mwaskom/seaborn/blob/a47b97e4b98c809db55cbd283de21acba89fe186/project26/seaborn/_core/scales.py#L84-L96
| 26 |
[
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
12
] | 100 |
[] | 0 | true | 90.258449 | 13 | 3 | 100 | 0 |
def _get_scale(self, name: str, forward: Callable, inverse: Callable):
major_locator, minor_locator = self._get_locators(**self._tick_params)
major_formatter = self._get_formatter(major_locator, **self._label_params)
class InternalScale(mpl.scale.FuncScale):
def set_default_locators_and_formatters(self, axis):
axis.set_major_locator(major_locator)
if minor_locator is not None:
axis.set_minor_locator(minor_locator)
axis.set_major_formatter(major_formatter)
return InternalScale(name, (forward, inverse))
| 19,308 |
||
mwaskom/seaborn
|
a47b97e4b98c809db55cbd283de21acba89fe186
|
seaborn/_core/scales.py
|
Scale._spacing
|
(self, x: Series)
|
return space
| 98 | 104 |
def _spacing(self, x: Series) -> float:
space = self._spacer(x)
if np.isnan(space):
# This happens when there is no variance in the orient coordinate data
# Not exactly clear what the right default is, but 1 seems reasonable?
return 1
return space
|
https://github.com/mwaskom/seaborn/blob/a47b97e4b98c809db55cbd283de21acba89fe186/project26/seaborn/_core/scales.py#L98-L104
| 26 |
[
0,
1,
2,
3,
4,
5,
6
] | 100 |
[] | 0 | true | 90.258449 | 7 | 2 | 100 | 0 |
def _spacing(self, x: Series) -> float:
space = self._spacer(x)
if np.isnan(space):
# This happens when there is no variance in the orient coordinate data
# Not exactly clear what the right default is, but 1 seems reasonable?
return 1
return space
| 19,309 |
||
mwaskom/seaborn
|
a47b97e4b98c809db55cbd283de21acba89fe186
|
seaborn/_core/scales.py
|
Scale._finalize
|
(self, p: Plot, axis: Axis)
|
Perform scale-specific axis tweaks after adding artists.
|
Perform scale-specific axis tweaks after adding artists.
| 111 | 113 |
def _finalize(self, p: Plot, axis: Axis) -> None:
"""Perform scale-specific axis tweaks after adding artists."""
pass
|
https://github.com/mwaskom/seaborn/blob/a47b97e4b98c809db55cbd283de21acba89fe186/project26/seaborn/_core/scales.py#L111-L113
| 26 |
[
0,
1,
2
] | 100 |
[] | 0 | true | 90.258449 | 3 | 1 | 100 | 1 |
def _finalize(self, p: Plot, axis: Axis) -> None:
pass
| 19,310 |
|
mwaskom/seaborn
|
a47b97e4b98c809db55cbd283de21acba89fe186
|
seaborn/_core/scales.py
|
Scale.__call__
|
(self, data: Series)
| 115 | 134 |
def __call__(self, data: Series) -> ArrayLike:
trans_data: Series | NDArray | list
# TODO sometimes we need to handle scalars (e.g. for Line)
# but what is the best way to do that?
scalar_data = np.isscalar(data)
if scalar_data:
trans_data = np.array([data])
else:
trans_data = data
for func in self._pipeline:
if func is not None:
trans_data = func(trans_data)
if scalar_data:
return trans_data[0]
else:
return trans_data
|
https://github.com/mwaskom/seaborn/blob/a47b97e4b98c809db55cbd283de21acba89fe186/project26/seaborn/_core/scales.py#L115-L134
| 26 |
[
0,
1,
5,
6,
7,
8,
10,
11,
12,
13,
14,
15,
16,
17,
19
] | 75 |
[] | 0 | false | 90.258449 | 20 | 5 | 100 | 0 |
def __call__(self, data: Series) -> ArrayLike:
trans_data: Series | NDArray | list
# TODO sometimes we need to handle scalars (e.g. for Line)
# but what is the best way to do that?
scalar_data = np.isscalar(data)
if scalar_data:
trans_data = np.array([data])
else:
trans_data = data
for func in self._pipeline:
if func is not None:
trans_data = func(trans_data)
if scalar_data:
return trans_data[0]
else:
return trans_data
| 19,311 |
|||
mwaskom/seaborn
|
a47b97e4b98c809db55cbd283de21acba89fe186
|
seaborn/_core/scales.py
|
Scale._identity
|
()
|
return Identity()
| 137 | 145 |
def _identity():
class Identity(Scale):
_pipeline = []
_spacer = None
_legend = None
_matplotlib_scale = None
return Identity()
|
https://github.com/mwaskom/seaborn/blob/a47b97e4b98c809db55cbd283de21acba89fe186/project26/seaborn/_core/scales.py#L137-L145
| 26 |
[
0,
1,
2,
3,
4,
5,
6,
7,
8
] | 100 |
[] | 0 | true | 90.258449 | 9 | 1 | 100 | 0 |
def _identity():
class Identity(Scale):
_pipeline = []
_spacer = None
_legend = None
_matplotlib_scale = None
return Identity()
| 19,312 |
||
mwaskom/seaborn
|
a47b97e4b98c809db55cbd283de21acba89fe186
|
seaborn/_core/scales.py
|
PseudoAxis.__init__
|
(self, scale)
| 903 | 915 |
def __init__(self, scale):
self.converter = None
self.units = None
self.scale = scale
self.major = mpl.axis.Ticker()
self.minor = mpl.axis.Ticker()
# It appears that this needs to be initialized this way on matplotlib 3.1,
# but not later versions. It is unclear whether there are any issues with it.
self._data_interval = None, None
scale.set_default_locators_and_formatters(self)
|
https://github.com/mwaskom/seaborn/blob/a47b97e4b98c809db55cbd283de21acba89fe186/project26/seaborn/_core/scales.py#L903-L915
| 26 |
[
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
12
] | 100 |
[] | 0 | true | 90.258449 | 13 | 1 | 100 | 0 |
def __init__(self, scale):
self.converter = None
self.units = None
self.scale = scale
self.major = mpl.axis.Ticker()
self.minor = mpl.axis.Ticker()
# It appears that this needs to be initialized this way on matplotlib 3.1,
# but not later versions. It is unclear whether there are any issues with it.
self._data_interval = None, None
scale.set_default_locators_and_formatters(self)
| 19,313 |
|||
mwaskom/seaborn
|
a47b97e4b98c809db55cbd283de21acba89fe186
|
seaborn/_core/scales.py
|
PseudoAxis.set_view_interval
|
(self, vmin, vmax)
| 918 | 919 |
def set_view_interval(self, vmin, vmax):
self._view_interval = vmin, vmax
|
https://github.com/mwaskom/seaborn/blob/a47b97e4b98c809db55cbd283de21acba89fe186/project26/seaborn/_core/scales.py#L918-L919
| 26 |
[
0,
1
] | 100 |
[] | 0 | true | 90.258449 | 2 | 1 | 100 | 0 |
def set_view_interval(self, vmin, vmax):
self._view_interval = vmin, vmax
| 19,314 |
|||
mwaskom/seaborn
|
a47b97e4b98c809db55cbd283de21acba89fe186
|
seaborn/_core/scales.py
|
PseudoAxis.get_view_interval
|
(self)
|
return self._view_interval
| 921 | 922 |
def get_view_interval(self):
return self._view_interval
|
https://github.com/mwaskom/seaborn/blob/a47b97e4b98c809db55cbd283de21acba89fe186/project26/seaborn/_core/scales.py#L921-L922
| 26 |
[
0,
1
] | 100 |
[] | 0 | true | 90.258449 | 2 | 1 | 100 | 0 |
def get_view_interval(self):
return self._view_interval
| 19,315 |
||
mwaskom/seaborn
|
a47b97e4b98c809db55cbd283de21acba89fe186
|
seaborn/_core/scales.py
|
PseudoAxis.set_data_interval
|
(self, vmin, vmax)
| 929 | 930 |
def set_data_interval(self, vmin, vmax):
self._data_interval = vmin, vmax
|
https://github.com/mwaskom/seaborn/blob/a47b97e4b98c809db55cbd283de21acba89fe186/project26/seaborn/_core/scales.py#L929-L930
| 26 |
[
0
] | 50 |
[
1
] | 50 | false | 90.258449 | 2 | 1 | 50 | 0 |
def set_data_interval(self, vmin, vmax):
self._data_interval = vmin, vmax
| 19,316 |
|||
mwaskom/seaborn
|
a47b97e4b98c809db55cbd283de21acba89fe186
|
seaborn/_core/scales.py
|
PseudoAxis.get_data_interval
|
(self)
|
return self._data_interval
| 932 | 933 |
def get_data_interval(self):
return self._data_interval
|
https://github.com/mwaskom/seaborn/blob/a47b97e4b98c809db55cbd283de21acba89fe186/project26/seaborn/_core/scales.py#L932-L933
| 26 |
[
0
] | 50 |
[
1
] | 50 | false | 90.258449 | 2 | 1 | 50 | 0 |
def get_data_interval(self):
return self._data_interval
| 19,317 |
||
mwaskom/seaborn
|
a47b97e4b98c809db55cbd283de21acba89fe186
|
seaborn/_core/scales.py
|
PseudoAxis.get_tick_space
|
(self)
|
return 5
| 935 | 938 |
def get_tick_space(self):
# TODO how to do this in a configurable / auto way?
# Would be cool to have legend density adapt to figure size, etc.
return 5
|
https://github.com/mwaskom/seaborn/blob/a47b97e4b98c809db55cbd283de21acba89fe186/project26/seaborn/_core/scales.py#L935-L938
| 26 |
[
0,
1,
2,
3
] | 100 |
[] | 0 | true | 90.258449 | 4 | 1 | 100 | 0 |
def get_tick_space(self):
# TODO how to do this in a configurable / auto way?
# Would be cool to have legend density adapt to figure size, etc.
return 5
| 19,318 |
||
mwaskom/seaborn
|
a47b97e4b98c809db55cbd283de21acba89fe186
|
seaborn/_core/scales.py
|
PseudoAxis.set_major_locator
|
(self, locator)
| 940 | 942 |
def set_major_locator(self, locator):
self.major.locator = locator
locator.set_axis(self)
|
https://github.com/mwaskom/seaborn/blob/a47b97e4b98c809db55cbd283de21acba89fe186/project26/seaborn/_core/scales.py#L940-L942
| 26 |
[
0,
1,
2
] | 100 |
[] | 0 | true | 90.258449 | 3 | 1 | 100 | 0 |
def set_major_locator(self, locator):
self.major.locator = locator
locator.set_axis(self)
| 19,319 |
|||
mwaskom/seaborn
|
a47b97e4b98c809db55cbd283de21acba89fe186
|
seaborn/_core/scales.py
|
PseudoAxis.set_major_formatter
|
(self, formatter)
| 944 | 946 |
def set_major_formatter(self, formatter):
self.major.formatter = formatter
formatter.set_axis(self)
|
https://github.com/mwaskom/seaborn/blob/a47b97e4b98c809db55cbd283de21acba89fe186/project26/seaborn/_core/scales.py#L944-L946
| 26 |
[
0,
1,
2
] | 100 |
[] | 0 | true | 90.258449 | 3 | 1 | 100 | 0 |
def set_major_formatter(self, formatter):
self.major.formatter = formatter
formatter.set_axis(self)
| 19,320 |
|||
mwaskom/seaborn
|
a47b97e4b98c809db55cbd283de21acba89fe186
|
seaborn/_core/scales.py
|
PseudoAxis.set_minor_locator
|
(self, locator)
| 948 | 950 |
def set_minor_locator(self, locator):
self.minor.locator = locator
locator.set_axis(self)
|
https://github.com/mwaskom/seaborn/blob/a47b97e4b98c809db55cbd283de21acba89fe186/project26/seaborn/_core/scales.py#L948-L950
| 26 |
[
0,
1,
2
] | 100 |
[] | 0 | true | 90.258449 | 3 | 1 | 100 | 0 |
def set_minor_locator(self, locator):
self.minor.locator = locator
locator.set_axis(self)
| 19,321 |
|||
mwaskom/seaborn
|
a47b97e4b98c809db55cbd283de21acba89fe186
|
seaborn/_core/scales.py
|
PseudoAxis.set_minor_formatter
|
(self, formatter)
| 952 | 954 |
def set_minor_formatter(self, formatter):
self.minor.formatter = formatter
formatter.set_axis(self)
|
https://github.com/mwaskom/seaborn/blob/a47b97e4b98c809db55cbd283de21acba89fe186/project26/seaborn/_core/scales.py#L952-L954
| 26 |
[
0
] | 33.333333 |
[
1,
2
] | 66.666667 | false | 90.258449 | 3 | 1 | 33.333333 | 0 |
def set_minor_formatter(self, formatter):
self.minor.formatter = formatter
formatter.set_axis(self)
| 19,322 |
|||
mwaskom/seaborn
|
a47b97e4b98c809db55cbd283de21acba89fe186
|
seaborn/_core/scales.py
|
PseudoAxis.set_units
|
(self, units)
| 956 | 957 |
def set_units(self, units):
self.units = units
|
https://github.com/mwaskom/seaborn/blob/a47b97e4b98c809db55cbd283de21acba89fe186/project26/seaborn/_core/scales.py#L956-L957
| 26 |
[
0,
1
] | 100 |
[] | 0 | true | 90.258449 | 2 | 1 | 100 | 0 |
def set_units(self, units):
self.units = units
| 19,323 |
|||
mwaskom/seaborn
|
a47b97e4b98c809db55cbd283de21acba89fe186
|
seaborn/_core/scales.py
|
PseudoAxis.update_units
|
(self, x)
|
Pass units to the internal converter, potentially updating its mapping.
|
Pass units to the internal converter, potentially updating its mapping.
| 959 | 972 |
def update_units(self, x):
"""Pass units to the internal converter, potentially updating its mapping."""
self.converter = mpl.units.registry.get_converter(x)
if self.converter is not None:
self.converter.default_units(x, self)
info = self.converter.axisinfo(self.units, self)
if info is None:
return
if info.majloc is not None:
self.set_major_locator(info.majloc)
if info.majfmt is not None:
self.set_major_formatter(info.majfmt)
|
https://github.com/mwaskom/seaborn/blob/a47b97e4b98c809db55cbd283de21acba89fe186/project26/seaborn/_core/scales.py#L959-L972
| 26 |
[
0,
1,
2,
3,
4,
5,
6,
7,
8,
10,
11,
12,
13
] | 92.857143 |
[
9
] | 7.142857 | false | 90.258449 | 14 | 5 | 92.857143 | 1 |
def update_units(self, x):
self.converter = mpl.units.registry.get_converter(x)
if self.converter is not None:
self.converter.default_units(x, self)
info = self.converter.axisinfo(self.units, self)
if info is None:
return
if info.majloc is not None:
self.set_major_locator(info.majloc)
if info.majfmt is not None:
self.set_major_formatter(info.majfmt)
| 19,324 |
|
mwaskom/seaborn
|
a47b97e4b98c809db55cbd283de21acba89fe186
|
seaborn/_core/scales.py
|
PseudoAxis.convert_units
|
(self, x)
|
return self.converter.convert(x, self.units, self)
|
Return a numeric representation of the input data.
|
Return a numeric representation of the input data.
| 977 | 983 |
def convert_units(self, x):
"""Return a numeric representation of the input data."""
if np.issubdtype(np.asarray(x).dtype, np.number):
return x
elif self.converter is None:
return x
return self.converter.convert(x, self.units, self)
|
https://github.com/mwaskom/seaborn/blob/a47b97e4b98c809db55cbd283de21acba89fe186/project26/seaborn/_core/scales.py#L977-L983
| 26 |
[
0,
1,
2,
3,
4,
5,
6
] | 100 |
[] | 0 | true | 90.258449 | 7 | 3 | 100 | 1 |
def convert_units(self, x):
if np.issubdtype(np.asarray(x).dtype, np.number):
return x
elif self.converter is None:
return x
return self.converter.convert(x, self.units, self)
| 19,325 |
mwaskom/seaborn
|
a47b97e4b98c809db55cbd283de21acba89fe186
|
seaborn/_core/scales.py
|
PseudoAxis.get_scale
|
(self)
|
return self.scale
| 985 | 991 |
def get_scale(self):
# Note that matplotlib actually returns a string here!
# (e.g., with a log scale, axis.get_scale() returns "log")
# Currently we just hit it with minor ticks where it checks for
# scale == "log". I'm not sure how you'd actually use log-scale
# minor "ticks" in a legend context, so this is fine....
return self.scale
|
https://github.com/mwaskom/seaborn/blob/a47b97e4b98c809db55cbd283de21acba89fe186/project26/seaborn/_core/scales.py#L985-L991
| 26 |
[
0,
1,
2,
3,
4,
5,
6
] | 100 |
[] | 0 | true | 90.258449 | 7 | 1 | 100 | 0 |
def get_scale(self):
# Note that matplotlib actually returns a string here!
# (e.g., with a log scale, axis.get_scale() returns "log")
# Currently we just hit it with minor ticks where it checks for
# scale == "log". I'm not sure how you'd actually use log-scale
# minor "ticks" in a legend context, so this is fine....
return self.scale
| 19,326 |
||
mwaskom/seaborn
|
a47b97e4b98c809db55cbd283de21acba89fe186
|
seaborn/_core/scales.py
|
PseudoAxis.get_majorticklocs
|
(self)
|
return self.major.locator()
| 993 | 994 |
def get_majorticklocs(self):
return self.major.locator()
|
https://github.com/mwaskom/seaborn/blob/a47b97e4b98c809db55cbd283de21acba89fe186/project26/seaborn/_core/scales.py#L993-L994
| 26 |
[
0,
1
] | 100 |
[] | 0 | true | 90.258449 | 2 | 1 | 100 | 0 |
def get_majorticklocs(self):
return self.major.locator()
| 19,327 |
||
mwaskom/seaborn
|
a47b97e4b98c809db55cbd283de21acba89fe186
|
seaborn/_core/rules.py
|
variable_type
|
(
vector: Series,
boolean_type: Literal["numeric", "categorical", "boolean"] = "numeric",
strict_boolean: bool = False,
)
|
return VarType("categorical")
|
Determine whether a vector contains numeric, categorical, or datetime data.
This function differs from the pandas typing API in a few 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`.
- There is some flexibility about how to treat binary / boolean data.
Parameters
----------
vector : :func:`pandas.Series`, :func:`numpy.ndarray`, or Python sequence
Input data to test.
boolean_type : 'numeric', 'categorical', or 'boolean'
Type to use for vectors containing only 0s and 1s (and NAs).
strict_boolean : bool
If True, only consider data to be boolean when the dtype is bool or Boolean.
Returns
-------
var_type : 'numeric', 'categorical', or 'datetime'
Name identifying the type of data in the vector.
|
Determine whether a vector contains numeric, categorical, or datetime data.
| 38 | 134 |
def variable_type(
vector: Series,
boolean_type: Literal["numeric", "categorical", "boolean"] = "numeric",
strict_boolean: bool = False,
) -> VarType:
"""
Determine whether a vector contains numeric, categorical, or datetime data.
This function differs from the pandas typing API in a few 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`.
- There is some flexibility about how to treat binary / boolean data.
Parameters
----------
vector : :func:`pandas.Series`, :func:`numpy.ndarray`, or Python sequence
Input data to test.
boolean_type : 'numeric', 'categorical', or 'boolean'
Type to use for vectors containing only 0s and 1s (and NAs).
strict_boolean : bool
If True, only consider data to be boolean when the dtype is bool or Boolean.
Returns
-------
var_type : 'numeric', 'categorical', or 'datetime'
Name identifying the type of data in the vector.
"""
# If a categorical dtype is set, infer categorical
if pd.api.types.is_categorical_dtype(vector):
return VarType("categorical")
# Special-case all-na data, which is always "numeric"
if pd.isna(vector).all():
return VarType("numeric")
# 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) # type: ignore # mypy bug?
)
if strict_boolean:
if isinstance(vector.dtype, pd.core.dtypes.base.ExtensionDtype):
boolean_dtypes = ["bool", "boolean"]
else:
boolean_dtypes = ["bool"]
boolean_vector = vector.dtype in boolean_dtypes
else:
boolean_vector = bool(np.isin(vector, [0, 1, np.nan]).all())
if boolean_vector:
return VarType(boolean_type)
# Defer to positive pandas tests
if pd.api.types.is_numeric_dtype(vector):
return VarType("numeric")
if pd.api.types.is_datetime64_dtype(vector):
return VarType("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 VarType("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 VarType("datetime")
# Otherwise, our final fallback is to consider things categorical
return VarType("categorical")
|
https://github.com/mwaskom/seaborn/blob/a47b97e4b98c809db55cbd283de21acba89fe186/project26/seaborn/_core/rules.py#L38-L134
| 26 |
[
0,
31,
32,
33,
34,
35,
36,
37,
38,
39,
40,
41,
42,
43,
44,
45,
46,
47,
48,
52,
53,
56,
57,
59,
60,
61,
62,
63,
64,
65,
66,
67,
68,
69,
70,
71,
72,
73,
74,
75,
76,
77,
78,
79,
80,
81,
82,
83,
84,
85,
86,
87,
88,
89,
90,
91,
92,
93,
94,
95,
96
] | 62.886598 |
[
54
] | 1.030928 | false | 98.305085 | 97 | 17 | 98.969072 | 23 |
def variable_type(
vector: Series,
boolean_type: Literal["numeric", "categorical", "boolean"] = "numeric",
strict_boolean: bool = False,
) -> VarType:
# If a categorical dtype is set, infer categorical
if pd.api.types.is_categorical_dtype(vector):
return VarType("categorical")
# Special-case all-na data, which is always "numeric"
if pd.isna(vector).all():
return VarType("numeric")
# 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) # type: ignore # mypy bug?
)
if strict_boolean:
if isinstance(vector.dtype, pd.core.dtypes.base.ExtensionDtype):
boolean_dtypes = ["bool", "boolean"]
else:
boolean_dtypes = ["bool"]
boolean_vector = vector.dtype in boolean_dtypes
else:
boolean_vector = bool(np.isin(vector, [0, 1, np.nan]).all())
if boolean_vector:
return VarType(boolean_type)
# Defer to positive pandas tests
if pd.api.types.is_numeric_dtype(vector):
return VarType("numeric")
if pd.api.types.is_datetime64_dtype(vector):
return VarType("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 VarType("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 VarType("datetime")
# Otherwise, our final fallback is to consider things categorical
return VarType("categorical")
| 19,328 |
mwaskom/seaborn
|
a47b97e4b98c809db55cbd283de21acba89fe186
|
seaborn/_core/rules.py
|
categorical_order
|
(vector: Series, order: list | None = None)
|
return order
|
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.
|
Return a list of unique data values using seaborn's ordering rules.
| 137 | 165 |
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
|
https://github.com/mwaskom/seaborn/blob/a47b97e4b98c809db55cbd283de21acba89fe186/project26/seaborn/_core/rules.py#L137-L165
| 26 |
[
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
12,
13,
14,
15,
16,
17,
18,
19,
20,
21,
22,
23,
24,
25,
26,
27,
28
] | 100 |
[] | 0 | true | 98.305085 | 29 | 4 | 100 | 14 |
def categorical_order(vector: Series, order: list | None = None) -> list:
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
| 19,329 |
mwaskom/seaborn
|
a47b97e4b98c809db55cbd283de21acba89fe186
|
seaborn/_core/rules.py
|
VarType.__init__
|
(self, data)
| 29 | 31 |
def __init__(self, data):
assert data in self.allowed, data
super().__init__(data)
|
https://github.com/mwaskom/seaborn/blob/a47b97e4b98c809db55cbd283de21acba89fe186/project26/seaborn/_core/rules.py#L29-L31
| 26 |
[
0,
1,
2
] | 100 |
[] | 0 | true | 98.305085 | 3 | 2 | 100 | 0 |
def __init__(self, data):
assert data in self.allowed, data
super().__init__(data)
| 19,330 |
|||
mwaskom/seaborn
|
a47b97e4b98c809db55cbd283de21acba89fe186
|
seaborn/_core/rules.py
|
VarType.__eq__
|
(self, other)
|
return self.data == other
| 33 | 35 |
def __eq__(self, other):
assert other in self.allowed, other
return self.data == other
|
https://github.com/mwaskom/seaborn/blob/a47b97e4b98c809db55cbd283de21acba89fe186/project26/seaborn/_core/rules.py#L33-L35
| 26 |
[
0,
1,
2
] | 100 |
[] | 0 | true | 98.305085 | 3 | 2 | 100 | 0 |
def __eq__(self, other):
assert other in self.allowed, other
return self.data == other
| 19,331 |
||
mwaskom/seaborn
|
a47b97e4b98c809db55cbd283de21acba89fe186
|
seaborn/_core/properties.py
|
Property.__init__
|
(self, variable: str | None = None)
|
Initialize the property with the name of the corresponding plot variable.
|
Initialize the property with the name of the corresponding plot variable.
| 58 | 62 |
def __init__(self, variable: str | None = None):
"""Initialize the property with the name of the corresponding plot variable."""
if not variable:
variable = self.__class__.__name__.lower()
self.variable = variable
|
https://github.com/mwaskom/seaborn/blob/a47b97e4b98c809db55cbd283de21acba89fe186/project26/seaborn/_core/properties.py#L58-L62
| 26 |
[
0,
1,
2,
3,
4
] | 100 |
[] | 0 | true | 99.058824 | 5 | 2 | 100 | 1 |
def __init__(self, variable: str | None = None):
if not variable:
variable = self.__class__.__name__.lower()
self.variable = variable
| 19,332 |
|
mwaskom/seaborn
|
a47b97e4b98c809db55cbd283de21acba89fe186
|
seaborn/_core/properties.py
|
Property.default_scale
|
(self, data: Series)
|
Given data, initialize appropriate scale class.
|
Given data, initialize appropriate scale class.
| 64 | 75 |
def default_scale(self, data: Series) -> Scale:
"""Given data, initialize appropriate scale class."""
var_type = variable_type(data, boolean_type="boolean", strict_boolean=True)
if var_type == "numeric":
return Continuous()
elif var_type == "datetime":
return Temporal()
elif var_type == "boolean":
return Boolean()
else:
return Nominal()
|
https://github.com/mwaskom/seaborn/blob/a47b97e4b98c809db55cbd283de21acba89fe186/project26/seaborn/_core/properties.py#L64-L75
| 26 |
[
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11
] | 100 |
[] | 0 | true | 99.058824 | 12 | 4 | 100 | 1 |
def default_scale(self, data: Series) -> Scale:
var_type = variable_type(data, boolean_type="boolean", strict_boolean=True)
if var_type == "numeric":
return Continuous()
elif var_type == "datetime":
return Temporal()
elif var_type == "boolean":
return Boolean()
else:
return Nominal()
| 19,333 |
|
mwaskom/seaborn
|
a47b97e4b98c809db55cbd283de21acba89fe186
|
seaborn/_core/properties.py
|
Property.infer_scale
|
(self, arg: Any, data: Series)
|
Given data and a scaling argument, initialize appropriate scale class.
|
Given data and a scaling argument, initialize appropriate scale class.
| 77 | 95 |
def infer_scale(self, arg: Any, data: Series) -> Scale:
"""Given data and a scaling argument, initialize appropriate scale class."""
# TODO put these somewhere external for validation
# TODO putting this here won't pick it up if subclasses define infer_scale
# (e.g. color). How best to handle that? One option is to call super after
# handling property-specific possibilities (e.g. for color check that the
# arg is not a valid palette name) but that could get tricky.
trans_args = ["log", "symlog", "logit", "pow", "sqrt"]
if isinstance(arg, str):
if any(arg.startswith(k) for k in trans_args):
# TODO validate numeric type? That should happen centrally somewhere
return Continuous(trans=arg)
else:
msg = f"Unknown magic arg for {self.variable} scale: '{arg}'."
raise ValueError(msg)
else:
arg_type = type(arg).__name__
msg = f"Magic arg for {self.variable} scale must be str, not {arg_type}."
raise TypeError(msg)
|
https://github.com/mwaskom/seaborn/blob/a47b97e4b98c809db55cbd283de21acba89fe186/project26/seaborn/_core/properties.py#L77-L95
| 26 |
[
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
12,
13,
14,
15,
16,
17,
18
] | 100 |
[] | 0 | true | 99.058824 | 19 | 3 | 100 | 1 |
def infer_scale(self, arg: Any, data: Series) -> Scale:
# TODO put these somewhere external for validation
# TODO putting this here won't pick it up if subclasses define infer_scale
# (e.g. color). How best to handle that? One option is to call super after
# handling property-specific possibilities (e.g. for color check that the
# arg is not a valid palette name) but that could get tricky.
trans_args = ["log", "symlog", "logit", "pow", "sqrt"]
if isinstance(arg, str):
if any(arg.startswith(k) for k in trans_args):
# TODO validate numeric type? That should happen centrally somewhere
return Continuous(trans=arg)
else:
msg = f"Unknown magic arg for {self.variable} scale: '{arg}'."
raise ValueError(msg)
else:
arg_type = type(arg).__name__
msg = f"Magic arg for {self.variable} scale must be str, not {arg_type}."
raise TypeError(msg)
| 19,334 |
|
mwaskom/seaborn
|
a47b97e4b98c809db55cbd283de21acba89fe186
|
seaborn/_core/properties.py
|
Property.get_mapping
|
(self, scale: Scale, data: Series)
|
return identity
|
Return a function that maps from data domain to property range.
|
Return a function that maps from data domain to property range.
| 97 | 101 |
def get_mapping(self, scale: Scale, data: Series) -> Mapping:
"""Return a function that maps from data domain to property range."""
def identity(x):
return x
return identity
|
https://github.com/mwaskom/seaborn/blob/a47b97e4b98c809db55cbd283de21acba89fe186/project26/seaborn/_core/properties.py#L97-L101
| 26 |
[
0,
1,
2,
3,
4
] | 100 |
[] | 0 | true | 99.058824 | 5 | 2 | 100 | 1 |
def get_mapping(self, scale: Scale, data: Series) -> Mapping:
def identity(x):
return x
return identity
| 19,335 |
mwaskom/seaborn
|
a47b97e4b98c809db55cbd283de21acba89fe186
|
seaborn/_core/properties.py
|
Property.standardize
|
(self, val: Any)
|
return val
|
Coerce flexible property value to standardized representation.
|
Coerce flexible property value to standardized representation.
| 103 | 105 |
def standardize(self, val: Any) -> Any:
"""Coerce flexible property value to standardized representation."""
return val
|
https://github.com/mwaskom/seaborn/blob/a47b97e4b98c809db55cbd283de21acba89fe186/project26/seaborn/_core/properties.py#L103-L105
| 26 |
[
0,
1,
2
] | 100 |
[] | 0 | true | 99.058824 | 3 | 1 | 100 | 1 |
def standardize(self, val: Any) -> Any:
return val
| 19,336 |
mwaskom/seaborn
|
a47b97e4b98c809db55cbd283de21acba89fe186
|
seaborn/_core/properties.py
|
Property._check_dict_entries
|
(self, levels: list, values: dict)
|
Input check when values are provided as a dictionary.
|
Input check when values are provided as a dictionary.
| 107 | 113 |
def _check_dict_entries(self, levels: list, values: dict) -> None:
"""Input check when values are provided as a dictionary."""
missing = set(levels) - set(values)
if missing:
formatted = ", ".join(map(repr, sorted(missing, key=str)))
err = f"No entry in {self.variable} dictionary for {formatted}"
raise ValueError(err)
|
https://github.com/mwaskom/seaborn/blob/a47b97e4b98c809db55cbd283de21acba89fe186/project26/seaborn/_core/properties.py#L107-L113
| 26 |
[
0,
1,
2,
3,
4,
5,
6
] | 100 |
[] | 0 | true | 99.058824 | 7 | 2 | 100 | 1 |
def _check_dict_entries(self, levels: list, values: dict) -> None:
missing = set(levels) - set(values)
if missing:
formatted = ", ".join(map(repr, sorted(missing, key=str)))
err = f"No entry in {self.variable} dictionary for {formatted}"
raise ValueError(err)
| 19,337 |
|
mwaskom/seaborn
|
a47b97e4b98c809db55cbd283de21acba89fe186
|
seaborn/_core/properties.py
|
Property._check_list_length
|
(self, levels: list, values: list)
|
return values
|
Input check when values are provided as a list.
|
Input check when values are provided as a list.
| 115 | 137 |
def _check_list_length(self, levels: list, values: list) -> list:
"""Input check when values are provided as a list."""
message = ""
if len(levels) > len(values):
message = " ".join([
f"\nThe {self.variable} list has fewer values ({len(values)})",
f"than needed ({len(levels)}) and will cycle, which may",
"produce an uninterpretable plot."
])
values = [x for _, x in zip(levels, itertools.cycle(values))]
elif len(values) > len(levels):
message = " ".join([
f"The {self.variable} list has more values ({len(values)})",
f"than needed ({len(levels)}), which may not be intended.",
])
values = values[:len(levels)]
# TODO look into custom PlotSpecWarning with better formatting
if message:
warnings.warn(message, UserWarning)
return values
|
https://github.com/mwaskom/seaborn/blob/a47b97e4b98c809db55cbd283de21acba89fe186/project26/seaborn/_core/properties.py#L115-L137
| 26 |
[
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
12,
13,
14,
15,
16,
17,
18,
19,
20,
21,
22
] | 100 |
[] | 0 | true | 99.058824 | 23 | 5 | 100 | 1 |
def _check_list_length(self, levels: list, values: list) -> list:
message = ""
if len(levels) > len(values):
message = " ".join([
f"\nThe {self.variable} list has fewer values ({len(values)})",
f"than needed ({len(levels)}) and will cycle, which may",
"produce an uninterpretable plot."
])
values = [x for _, x in zip(levels, itertools.cycle(values))]
elif len(values) > len(levels):
message = " ".join([
f"The {self.variable} list has more values ({len(values)})",
f"than needed ({len(levels)}), which may not be intended.",
])
values = values[:len(levels)]
# TODO look into custom PlotSpecWarning with better formatting
if message:
warnings.warn(message, UserWarning)
return values
| 19,338 |
mwaskom/seaborn
|
a47b97e4b98c809db55cbd283de21acba89fe186
|
seaborn/_core/properties.py
|
IntervalProperty.default_range
|
(self)
|
return self._default_range
|
Min and max values used by default for semantic mapping.
|
Min and max values used by default for semantic mapping.
| 164 | 166 |
def default_range(self) -> tuple[float, float]:
"""Min and max values used by default for semantic mapping."""
return self._default_range
|
https://github.com/mwaskom/seaborn/blob/a47b97e4b98c809db55cbd283de21acba89fe186/project26/seaborn/_core/properties.py#L164-L166
| 26 |
[
0,
1,
2
] | 100 |
[] | 0 | true | 99.058824 | 3 | 1 | 100 | 1 |
def default_range(self) -> tuple[float, float]:
return self._default_range
| 19,339 |
mwaskom/seaborn
|
a47b97e4b98c809db55cbd283de21acba89fe186
|
seaborn/_core/properties.py
|
IntervalProperty._forward
|
(self, values: ArrayLike)
|
return values
|
Transform applied to native values before linear mapping into interval.
|
Transform applied to native values before linear mapping into interval.
| 168 | 170 |
def _forward(self, values: ArrayLike) -> ArrayLike:
"""Transform applied to native values before linear mapping into interval."""
return values
|
https://github.com/mwaskom/seaborn/blob/a47b97e4b98c809db55cbd283de21acba89fe186/project26/seaborn/_core/properties.py#L168-L170
| 26 |
[
0,
1,
2
] | 100 |
[] | 0 | true | 99.058824 | 3 | 1 | 100 | 1 |
def _forward(self, values: ArrayLike) -> ArrayLike:
return values
| 19,340 |
mwaskom/seaborn
|
a47b97e4b98c809db55cbd283de21acba89fe186
|
seaborn/_core/properties.py
|
IntervalProperty._inverse
|
(self, values: ArrayLike)
|
return values
|
Transform applied to results of mapping that returns to native values.
|
Transform applied to results of mapping that returns to native values.
| 172 | 174 |
def _inverse(self, values: ArrayLike) -> ArrayLike:
"""Transform applied to results of mapping that returns to native values."""
return values
|
https://github.com/mwaskom/seaborn/blob/a47b97e4b98c809db55cbd283de21acba89fe186/project26/seaborn/_core/properties.py#L172-L174
| 26 |
[
0,
1,
2
] | 100 |
[] | 0 | true | 99.058824 | 3 | 1 | 100 | 1 |
def _inverse(self, values: ArrayLike) -> ArrayLike:
return values
| 19,341 |
mwaskom/seaborn
|
a47b97e4b98c809db55cbd283de21acba89fe186
|
seaborn/_core/properties.py
|
IntervalProperty.infer_scale
|
(self, arg: Any, data: Series)
|
Given data and a scaling argument, initialize appropriate scale class.
|
Given data and a scaling argument, initialize appropriate scale class.
| 176 | 193 |
def infer_scale(self, arg: Any, data: Series) -> Scale:
"""Given data and a scaling argument, initialize appropriate scale class."""
# TODO infer continuous based on log/sqrt etc?
var_type = variable_type(data, boolean_type="boolean", strict_boolean=True)
if var_type == "boolean":
return Boolean(arg)
elif isinstance(arg, (list, dict)):
return Nominal(arg)
elif var_type == "categorical":
return Nominal(arg)
elif var_type == "datetime":
return Temporal(arg)
# TODO other variable types
else:
return Continuous(arg)
|
https://github.com/mwaskom/seaborn/blob/a47b97e4b98c809db55cbd283de21acba89fe186/project26/seaborn/_core/properties.py#L176-L193
| 26 |
[
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
12,
13,
16,
17
] | 88.888889 |
[
14
] | 5.555556 | false | 99.058824 | 18 | 5 | 94.444444 | 1 |
def infer_scale(self, arg: Any, data: Series) -> Scale:
# TODO infer continuous based on log/sqrt etc?
var_type = variable_type(data, boolean_type="boolean", strict_boolean=True)
if var_type == "boolean":
return Boolean(arg)
elif isinstance(arg, (list, dict)):
return Nominal(arg)
elif var_type == "categorical":
return Nominal(arg)
elif var_type == "datetime":
return Temporal(arg)
# TODO other variable types
else:
return Continuous(arg)
| 19,342 |
|
mwaskom/seaborn
|
a47b97e4b98c809db55cbd283de21acba89fe186
|
seaborn/_core/properties.py
|
IntervalProperty.get_mapping
|
(self, scale: Scale, data: Series)
|
return mapping
|
Return a function that maps from data domain to property range.
|
Return a function that maps from data domain to property range.
| 195 | 221 |
def get_mapping(self, scale: Scale, data: Series) -> Mapping:
"""Return a function that maps from data domain to property range."""
if isinstance(scale, Nominal):
return self._get_nominal_mapping(scale, data)
elif isinstance(scale, Boolean):
return self._get_boolean_mapping(scale, data)
if scale.values is None:
vmin, vmax = self._forward(self.default_range)
elif isinstance(scale.values, tuple) and len(scale.values) == 2:
vmin, vmax = self._forward(scale.values)
else:
if isinstance(scale.values, tuple):
actual = f"{len(scale.values)}-tuple"
else:
actual = str(type(scale.values))
scale_class = scale.__class__.__name__
err = " ".join([
f"Values for {self.variable} variables with {scale_class} scale",
f"must be 2-tuple; not {actual}.",
])
raise TypeError(err)
def mapping(x):
return self._inverse(np.multiply(x, vmax - vmin) + vmin)
return mapping
|
https://github.com/mwaskom/seaborn/blob/a47b97e4b98c809db55cbd283de21acba89fe186/project26/seaborn/_core/properties.py#L195-L221
| 26 |
[
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
12,
13,
14,
15,
16,
17,
18,
19,
20,
21,
22,
23,
24,
25,
26
] | 100 |
[] | 0 | true | 99.058824 | 27 | 8 | 100 | 1 |
def get_mapping(self, scale: Scale, data: Series) -> Mapping:
if isinstance(scale, Nominal):
return self._get_nominal_mapping(scale, data)
elif isinstance(scale, Boolean):
return self._get_boolean_mapping(scale, data)
if scale.values is None:
vmin, vmax = self._forward(self.default_range)
elif isinstance(scale.values, tuple) and len(scale.values) == 2:
vmin, vmax = self._forward(scale.values)
else:
if isinstance(scale.values, tuple):
actual = f"{len(scale.values)}-tuple"
else:
actual = str(type(scale.values))
scale_class = scale.__class__.__name__
err = " ".join([
f"Values for {self.variable} variables with {scale_class} scale",
f"must be 2-tuple; not {actual}.",
])
raise TypeError(err)
def mapping(x):
return self._inverse(np.multiply(x, vmax - vmin) + vmin)
return mapping
| 19,343 |
mwaskom/seaborn
|
a47b97e4b98c809db55cbd283de21acba89fe186
|
seaborn/_core/properties.py
|
IntervalProperty._get_nominal_mapping
|
(self, scale: Nominal, data: Series)
|
return mapping
|
Identify evenly-spaced values using interval or explicit mapping.
|
Identify evenly-spaced values using interval or explicit mapping.
| 223 | 235 |
def _get_nominal_mapping(self, scale: Nominal, data: Series) -> Mapping:
"""Identify evenly-spaced values using interval or explicit mapping."""
levels = categorical_order(data, scale.order)
values = self._get_values(scale, levels)
def mapping(x):
ixs = np.asarray(x, np.intp)
out = np.full(len(x), np.nan)
use = np.isfinite(x)
out[use] = np.take(values, ixs[use])
return out
return mapping
|
https://github.com/mwaskom/seaborn/blob/a47b97e4b98c809db55cbd283de21acba89fe186/project26/seaborn/_core/properties.py#L223-L235
| 26 |
[
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
12
] | 100 |
[] | 0 | true | 99.058824 | 13 | 2 | 100 | 1 |
def _get_nominal_mapping(self, scale: Nominal, data: Series) -> Mapping:
levels = categorical_order(data, scale.order)
values = self._get_values(scale, levels)
def mapping(x):
ixs = np.asarray(x, np.intp)
out = np.full(len(x), np.nan)
use = np.isfinite(x)
out[use] = np.take(values, ixs[use])
return out
return mapping
| 19,344 |
mwaskom/seaborn
|
a47b97e4b98c809db55cbd283de21acba89fe186
|
seaborn/_core/properties.py
|
IntervalProperty._get_boolean_mapping
|
(self, scale: Boolean, data: Series)
|
return mapping
|
Identify evenly-spaced values using interval or explicit mapping.
|
Identify evenly-spaced values using interval or explicit mapping.
| 237 | 247 |
def _get_boolean_mapping(self, scale: Boolean, data: Series) -> Mapping:
"""Identify evenly-spaced values using interval or explicit mapping."""
values = self._get_values(scale, [True, False])
def mapping(x):
out = np.full(len(x), np.nan)
use = np.isfinite(x)
out[use] = np.where(x[use], *values)
return out
return mapping
|
https://github.com/mwaskom/seaborn/blob/a47b97e4b98c809db55cbd283de21acba89fe186/project26/seaborn/_core/properties.py#L237-L247
| 26 |
[
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10
] | 100 |
[] | 0 | true | 99.058824 | 11 | 2 | 100 | 1 |
def _get_boolean_mapping(self, scale: Boolean, data: Series) -> Mapping:
values = self._get_values(scale, [True, False])
def mapping(x):
out = np.full(len(x), np.nan)
use = np.isfinite(x)
out[use] = np.where(x[use], *values)
return out
return mapping
| 19,345 |
mwaskom/seaborn
|
a47b97e4b98c809db55cbd283de21acba89fe186
|
seaborn/_core/properties.py
|
IntervalProperty._get_values
|
(self, scale: Scale, levels: list)
|
return values
|
Validate scale.values and identify a value for each level.
|
Validate scale.values and identify a value for each level.
| 249 | 272 |
def _get_values(self, scale: Scale, levels: list) -> list:
"""Validate scale.values and identify a value for each level."""
if isinstance(scale.values, dict):
self._check_dict_entries(levels, scale.values)
values = [scale.values[x] for x in levels]
elif isinstance(scale.values, list):
values = self._check_list_length(levels, scale.values)
else:
if scale.values is None:
vmin, vmax = self.default_range
elif isinstance(scale.values, tuple):
vmin, vmax = scale.values
else:
scale_class = scale.__class__.__name__
err = " ".join([
f"Values for {self.variable} variables with {scale_class} scale",
f"must be a dict, list or tuple; not {type(scale.values)}",
])
raise TypeError(err)
vmin, vmax = self._forward([vmin, vmax])
values = list(self._inverse(np.linspace(vmax, vmin, len(levels))))
return values
|
https://github.com/mwaskom/seaborn/blob/a47b97e4b98c809db55cbd283de21acba89fe186/project26/seaborn/_core/properties.py#L249-L272
| 26 |
[
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
12,
13,
14,
15,
16,
17,
18,
19,
20,
21,
22,
23
] | 100 |
[] | 0 | true | 99.058824 | 24 | 6 | 100 | 1 |
def _get_values(self, scale: Scale, levels: list) -> list:
if isinstance(scale.values, dict):
self._check_dict_entries(levels, scale.values)
values = [scale.values[x] for x in levels]
elif isinstance(scale.values, list):
values = self._check_list_length(levels, scale.values)
else:
if scale.values is None:
vmin, vmax = self.default_range
elif isinstance(scale.values, tuple):
vmin, vmax = scale.values
else:
scale_class = scale.__class__.__name__
err = " ".join([
f"Values for {self.variable} variables with {scale_class} scale",
f"must be a dict, list or tuple; not {type(scale.values)}",
])
raise TypeError(err)
vmin, vmax = self._forward([vmin, vmax])
values = list(self._inverse(np.linspace(vmax, vmin, len(levels))))
return values
| 19,346 |
mwaskom/seaborn
|
a47b97e4b98c809db55cbd283de21acba89fe186
|
seaborn/_core/properties.py
|
PointSize._forward
|
(self, values)
|
return np.square(values)
|
Square native values to implement linear scaling of point area.
|
Square native values to implement linear scaling of point area.
| 279 | 281 |
def _forward(self, values):
"""Square native values to implement linear scaling of point area."""
return np.square(values)
|
https://github.com/mwaskom/seaborn/blob/a47b97e4b98c809db55cbd283de21acba89fe186/project26/seaborn/_core/properties.py#L279-L281
| 26 |
[
0,
1,
2
] | 100 |
[] | 0 | true | 99.058824 | 3 | 1 | 100 | 1 |
def _forward(self, values):
return np.square(values)
| 19,347 |
mwaskom/seaborn
|
a47b97e4b98c809db55cbd283de21acba89fe186
|
seaborn/_core/properties.py
|
PointSize._inverse
|
(self, values)
|
return np.sqrt(values)
|
Invert areal values back to point diameter.
|
Invert areal values back to point diameter.
| 283 | 285 |
def _inverse(self, values):
"""Invert areal values back to point diameter."""
return np.sqrt(values)
|
https://github.com/mwaskom/seaborn/blob/a47b97e4b98c809db55cbd283de21acba89fe186/project26/seaborn/_core/properties.py#L283-L285
| 26 |
[
0,
1,
2
] | 100 |
[] | 0 | true | 99.058824 | 3 | 1 | 100 | 1 |
def _inverse(self, values):
return np.sqrt(values)
| 19,348 |
mwaskom/seaborn
|
a47b97e4b98c809db55cbd283de21acba89fe186
|
seaborn/_core/properties.py
|
LineWidth.default_range
|
(self)
|
return base * .5, base * 2
|
Min and max values used by default for semantic mapping.
|
Min and max values used by default for semantic mapping.
| 291 | 294 |
def default_range(self) -> tuple[float, float]:
"""Min and max values used by default for semantic mapping."""
base = mpl.rcParams["lines.linewidth"]
return base * .5, base * 2
|
https://github.com/mwaskom/seaborn/blob/a47b97e4b98c809db55cbd283de21acba89fe186/project26/seaborn/_core/properties.py#L291-L294
| 26 |
[
0,
1,
2,
3
] | 100 |
[] | 0 | true | 99.058824 | 4 | 1 | 100 | 1 |
def default_range(self) -> tuple[float, float]:
base = mpl.rcParams["lines.linewidth"]
return base * .5, base * 2
| 19,349 |
mwaskom/seaborn
|
a47b97e4b98c809db55cbd283de21acba89fe186
|
seaborn/_core/properties.py
|
EdgeWidth.default_range
|
(self)
|
return base * .5, base * 2
|
Min and max values used by default for semantic mapping.
|
Min and max values used by default for semantic mapping.
| 300 | 303 |
def default_range(self) -> tuple[float, float]:
"""Min and max values used by default for semantic mapping."""
base = mpl.rcParams["patch.linewidth"]
return base * .5, base * 2
|
https://github.com/mwaskom/seaborn/blob/a47b97e4b98c809db55cbd283de21acba89fe186/project26/seaborn/_core/properties.py#L300-L303
| 26 |
[
0,
1,
2,
3
] | 100 |
[] | 0 | true | 99.058824 | 4 | 1 | 100 | 1 |
def default_range(self) -> tuple[float, float]:
base = mpl.rcParams["patch.linewidth"]
return base * .5, base * 2
| 19,350 |
mwaskom/seaborn
|
a47b97e4b98c809db55cbd283de21acba89fe186
|
seaborn/_core/properties.py
|
FontSize.default_range
|
(self)
|
return base * .5, base * 2
|
Min and max values used by default for semantic mapping.
|
Min and max values used by default for semantic mapping.
| 328 | 331 |
def default_range(self) -> tuple[float, float]:
"""Min and max values used by default for semantic mapping."""
base = mpl.rcParams["font.size"]
return base * .5, base * 2
|
https://github.com/mwaskom/seaborn/blob/a47b97e4b98c809db55cbd283de21acba89fe186/project26/seaborn/_core/properties.py#L328-L331
| 26 |
[
0,
1,
2,
3
] | 100 |
[] | 0 | true | 99.058824 | 4 | 1 | 100 | 1 |
def default_range(self) -> tuple[float, float]:
base = mpl.rcParams["font.size"]
return base * .5, base * 2
| 19,351 |
mwaskom/seaborn
|
a47b97e4b98c809db55cbd283de21acba89fe186
|
seaborn/_core/properties.py
|
ObjectProperty.default_scale
|
(self, data: Series)
|
return Boolean() if var_type == "boolean" else Nominal()
| 351 | 353 |
def default_scale(self, data: Series) -> Scale:
var_type = variable_type(data, boolean_type="boolean", strict_boolean=True)
return Boolean() if var_type == "boolean" else Nominal()
|
https://github.com/mwaskom/seaborn/blob/a47b97e4b98c809db55cbd283de21acba89fe186/project26/seaborn/_core/properties.py#L351-L353
| 26 |
[
0,
1,
2
] | 100 |
[] | 0 | true | 99.058824 | 3 | 1 | 100 | 0 |
def default_scale(self, data: Series) -> Scale:
var_type = variable_type(data, boolean_type="boolean", strict_boolean=True)
return Boolean() if var_type == "boolean" else Nominal()
| 19,352 |
||
mwaskom/seaborn
|
a47b97e4b98c809db55cbd283de21acba89fe186
|
seaborn/_core/properties.py
|
ObjectProperty.infer_scale
|
(self, arg: Any, data: Series)
|
return Boolean(arg) if var_type == "boolean" else Nominal(arg)
| 355 | 357 |
def infer_scale(self, arg: Any, data: Series) -> Scale:
var_type = variable_type(data, boolean_type="boolean", strict_boolean=True)
return Boolean(arg) if var_type == "boolean" else Nominal(arg)
|
https://github.com/mwaskom/seaborn/blob/a47b97e4b98c809db55cbd283de21acba89fe186/project26/seaborn/_core/properties.py#L355-L357
| 26 |
[
0,
1,
2
] | 100 |
[] | 0 | true | 99.058824 | 3 | 1 | 100 | 0 |
def infer_scale(self, arg: Any, data: Series) -> Scale:
var_type = variable_type(data, boolean_type="boolean", strict_boolean=True)
return Boolean(arg) if var_type == "boolean" else Nominal(arg)
| 19,353 |
||
mwaskom/seaborn
|
a47b97e4b98c809db55cbd283de21acba89fe186
|
seaborn/_core/properties.py
|
ObjectProperty.get_mapping
|
(self, scale: Scale, data: Series)
|
return mapping
|
Define mapping as lookup into list of object values.
|
Define mapping as lookup into list of object values.
| 359 | 376 |
def get_mapping(self, scale: Scale, data: Series) -> Mapping:
"""Define mapping as lookup into list of object values."""
boolean_scale = isinstance(scale, Boolean)
order = getattr(scale, "order", [True, False] if boolean_scale else None)
levels = categorical_order(data, order)
values = self._get_values(scale, levels)
if boolean_scale:
values = values[::-1]
def mapping(x):
ixs = np.asarray(x, np.intp)
return [
values[ix] if np.isfinite(x_i) else self.null_value
for x_i, ix in zip(x, ixs)
]
return mapping
|
https://github.com/mwaskom/seaborn/blob/a47b97e4b98c809db55cbd283de21acba89fe186/project26/seaborn/_core/properties.py#L359-L376
| 26 |
[
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
12,
13,
14,
15,
16,
17
] | 100 |
[] | 0 | true | 99.058824 | 18 | 4 | 100 | 1 |
def get_mapping(self, scale: Scale, data: Series) -> Mapping:
boolean_scale = isinstance(scale, Boolean)
order = getattr(scale, "order", [True, False] if boolean_scale else None)
levels = categorical_order(data, order)
values = self._get_values(scale, levels)
if boolean_scale:
values = values[::-1]
def mapping(x):
ixs = np.asarray(x, np.intp)
return [
values[ix] if np.isfinite(x_i) else self.null_value
for x_i, ix in zip(x, ixs)
]
return mapping
| 19,354 |
mwaskom/seaborn
|
a47b97e4b98c809db55cbd283de21acba89fe186
|
seaborn/_core/properties.py
|
ObjectProperty._get_values
|
(self, scale: Scale, levels: list)
|
return values
|
Validate scale.values and identify a value for each level.
|
Validate scale.values and identify a value for each level.
| 378 | 396 |
def _get_values(self, scale: Scale, levels: list) -> list:
"""Validate scale.values and identify a value for each level."""
n = len(levels)
if isinstance(scale.values, dict):
self._check_dict_entries(levels, scale.values)
values = [scale.values[x] for x in levels]
elif isinstance(scale.values, list):
values = self._check_list_length(levels, scale.values)
elif scale.values is None:
values = self._default_values(n)
else:
msg = " ".join([
f"Scale values for a {self.variable} variable must be provided",
f"in a dict or list; not {type(scale.values)}."
])
raise TypeError(msg)
values = [self.standardize(x) for x in values]
return values
|
https://github.com/mwaskom/seaborn/blob/a47b97e4b98c809db55cbd283de21acba89fe186/project26/seaborn/_core/properties.py#L378-L396
| 26 |
[
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
12,
13,
14,
15,
16,
17,
18
] | 100 |
[] | 0 | true | 99.058824 | 19 | 6 | 100 | 1 |
def _get_values(self, scale: Scale, levels: list) -> list:
n = len(levels)
if isinstance(scale.values, dict):
self._check_dict_entries(levels, scale.values)
values = [scale.values[x] for x in levels]
elif isinstance(scale.values, list):
values = self._check_list_length(levels, scale.values)
elif scale.values is None:
values = self._default_values(n)
else:
msg = " ".join([
f"Scale values for a {self.variable} variable must be provided",
f"in a dict or list; not {type(scale.values)}."
])
raise TypeError(msg)
values = [self.standardize(x) for x in values]
return values
| 19,355 |
mwaskom/seaborn
|
a47b97e4b98c809db55cbd283de21acba89fe186
|
seaborn/_core/properties.py
|
Marker.standardize
|
(self, val: MarkerPattern)
|
return MarkerStyle(val)
| 408 | 409 |
def standardize(self, val: MarkerPattern) -> MarkerStyle:
return MarkerStyle(val)
|
https://github.com/mwaskom/seaborn/blob/a47b97e4b98c809db55cbd283de21acba89fe186/project26/seaborn/_core/properties.py#L408-L409
| 26 |
[
0,
1
] | 100 |
[] | 0 | true | 99.058824 | 2 | 1 | 100 | 0 |
def standardize(self, val: MarkerPattern) -> MarkerStyle:
return MarkerStyle(val)
| 19,356 |
||
mwaskom/seaborn
|
a47b97e4b98c809db55cbd283de21acba89fe186
|
seaborn/_core/properties.py
|
Marker._default_values
|
(self, n: int)
|
return markers
|
Build an arbitrarily long list of unique marker styles.
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.
|
Build an arbitrarily long list of unique marker styles.
| 411 | 440 |
def _default_values(self, n: int) -> list[MarkerStyle]:
"""Build an arbitrarily long list of unique marker styles.
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
markers = [MarkerStyle(m) for m in markers[:n]]
return markers
|
https://github.com/mwaskom/seaborn/blob/a47b97e4b98c809db55cbd283de21acba89fe186/project26/seaborn/_core/properties.py#L411-L440
| 26 |
[
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
12,
13,
14,
15,
16,
17,
18,
19,
20,
21,
22,
23,
24,
25,
26,
27,
28,
29
] | 100 |
[] | 0 | true | 99.058824 | 30 | 3 | 100 | 12 |
def _default_values(self, n: int) -> list[MarkerStyle]:
# 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
markers = [MarkerStyle(m) for m in markers[:n]]
return markers
| 19,357 |
mwaskom/seaborn
|
a47b97e4b98c809db55cbd283de21acba89fe186
|
seaborn/_core/properties.py
|
LineStyle.standardize
|
(self, val: str | DashPattern)
|
return self._get_dash_pattern(val)
| 447 | 448 |
def standardize(self, val: str | DashPattern) -> DashPatternWithOffset:
return self._get_dash_pattern(val)
|
https://github.com/mwaskom/seaborn/blob/a47b97e4b98c809db55cbd283de21acba89fe186/project26/seaborn/_core/properties.py#L447-L448
| 26 |
[
0,
1
] | 100 |
[] | 0 | true | 99.058824 | 2 | 1 | 100 | 0 |
def standardize(self, val: str | DashPattern) -> DashPatternWithOffset:
return self._get_dash_pattern(val)
| 19,358 |
||
mwaskom/seaborn
|
a47b97e4b98c809db55cbd283de21acba89fe186
|
seaborn/_core/properties.py
|
LineStyle._default_values
|
(self, n: int)
|
return [self._get_dash_pattern(x) for x in dashes]
|
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.
|
Build an arbitrarily long list of unique dash styles for lines.
| 450 | 491 |
def _default_values(self, n: int) -> list[DashPatternWithOffset]:
"""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: list[str | DashPattern] = [
"-", (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 [self._get_dash_pattern(x) for x in dashes]
|
https://github.com/mwaskom/seaborn/blob/a47b97e4b98c809db55cbd283de21acba89fe186/project26/seaborn/_core/properties.py#L450-L491
| 26 |
[
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
12,
13,
14,
15,
16,
17,
18,
19,
20,
21,
22,
23,
24,
25,
26,
27,
28,
29,
30,
31,
32,
33,
34,
35,
36,
37,
38,
39,
40,
41
] | 100 |
[] | 0 | true | 99.058824 | 42 | 4 | 100 | 14 |
def _default_values(self, n: int) -> list[DashPatternWithOffset]:
# Start with dash specs that are well distinguishable
dashes: list[str | DashPattern] = [
"-", (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 [self._get_dash_pattern(x) for x in dashes]
| 19,359 |
mwaskom/seaborn
|
a47b97e4b98c809db55cbd283de21acba89fe186
|
seaborn/_core/properties.py
|
LineStyle._get_dash_pattern
|
(style: str | DashPattern)
|
return offset, dashes
|
Convert linestyle arguments to dash pattern with offset.
|
Convert linestyle arguments to dash pattern with offset.
| 494 | 537 |
def _get_dash_pattern(style: str | DashPattern) -> DashPatternWithOffset:
"""Convert linestyle arguments to dash pattern with offset."""
# Copied and modified from Matplotlib 3.4
# go from short hand -> full strings
ls_mapper = {"-": "solid", "--": "dashed", "-.": "dashdot", ":": "dotted"}
if isinstance(style, str):
style = ls_mapper.get(style, style)
# un-dashed styles
if style in ["solid", "none", "None"]:
offset = 0
dashes = None
# dashed styles
elif style in ["dashed", "dashdot", "dotted"]:
offset = 0
dashes = tuple(mpl.rcParams[f"lines.{style}_pattern"])
else:
options = [*ls_mapper.values(), *ls_mapper.keys()]
msg = f"Linestyle string must be one of {options}, not {repr(style)}."
raise ValueError(msg)
elif isinstance(style, tuple):
if len(style) > 1 and isinstance(style[1], tuple):
offset, dashes = style
elif len(style) > 1 and style[1] is None:
offset, dashes = style
else:
offset = 0
dashes = style
else:
val_type = type(style).__name__
msg = f"Linestyle must be str or tuple, not {val_type}."
raise TypeError(msg)
# Normalize offset to be positive and shorter than the dash cycle
if dashes is not None:
try:
dsum = sum(dashes)
except TypeError as err:
msg = f"Invalid dash pattern: {dashes}"
raise TypeError(msg) from err
if dsum:
offset %= dsum
return offset, dashes
|
https://github.com/mwaskom/seaborn/blob/a47b97e4b98c809db55cbd283de21acba89fe186/project26/seaborn/_core/properties.py#L494-L537
| 26 |
[
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
12,
13,
14,
15,
16,
17,
18,
19,
20,
21,
22,
23,
24,
25,
26,
27,
28,
29,
30,
31,
32,
33,
34,
35,
36,
37,
38,
39,
40,
41,
42,
43
] | 100 |
[] | 0 | true | 99.058824 | 44 | 12 | 100 | 1 |
def _get_dash_pattern(style: str | DashPattern) -> DashPatternWithOffset:
# Copied and modified from Matplotlib 3.4
# go from short hand -> full strings
ls_mapper = {"-": "solid", "--": "dashed", "-.": "dashdot", ":": "dotted"}
if isinstance(style, str):
style = ls_mapper.get(style, style)
# un-dashed styles
if style in ["solid", "none", "None"]:
offset = 0
dashes = None
# dashed styles
elif style in ["dashed", "dashdot", "dotted"]:
offset = 0
dashes = tuple(mpl.rcParams[f"lines.{style}_pattern"])
else:
options = [*ls_mapper.values(), *ls_mapper.keys()]
msg = f"Linestyle string must be one of {options}, not {repr(style)}."
raise ValueError(msg)
elif isinstance(style, tuple):
if len(style) > 1 and isinstance(style[1], tuple):
offset, dashes = style
elif len(style) > 1 and style[1] is None:
offset, dashes = style
else:
offset = 0
dashes = style
else:
val_type = type(style).__name__
msg = f"Linestyle must be str or tuple, not {val_type}."
raise TypeError(msg)
# Normalize offset to be positive and shorter than the dash cycle
if dashes is not None:
try:
dsum = sum(dashes)
except TypeError as err:
msg = f"Invalid dash pattern: {dashes}"
raise TypeError(msg) from err
if dsum:
offset %= dsum
return offset, dashes
| 19,360 |
mwaskom/seaborn
|
a47b97e4b98c809db55cbd283de21acba89fe186
|
seaborn/_core/properties.py
|
HorizontalAlignment._default_values
|
(self, n: int)
|
return [next(vals) for _ in range(n)]
| 546 | 548 |
def _default_values(self, n: int) -> list:
vals = itertools.cycle(["left", "right"])
return [next(vals) for _ in range(n)]
|
https://github.com/mwaskom/seaborn/blob/a47b97e4b98c809db55cbd283de21acba89fe186/project26/seaborn/_core/properties.py#L546-L548
| 26 |
[
0,
1,
2
] | 100 |
[] | 0 | true | 99.058824 | 3 | 2 | 100 | 0 |
def _default_values(self, n: int) -> list:
vals = itertools.cycle(["left", "right"])
return [next(vals) for _ in range(n)]
| 19,361 |
||
mwaskom/seaborn
|
a47b97e4b98c809db55cbd283de21acba89fe186
|
seaborn/_core/properties.py
|
VerticalAlignment._default_values
|
(self, n: int)
|
return [next(vals) for _ in range(n)]
| 553 | 555 |
def _default_values(self, n: int) -> list:
vals = itertools.cycle(["top", "bottom"])
return [next(vals) for _ in range(n)]
|
https://github.com/mwaskom/seaborn/blob/a47b97e4b98c809db55cbd283de21acba89fe186/project26/seaborn/_core/properties.py#L553-L555
| 26 |
[
0,
1,
2
] | 100 |
[] | 0 | true | 99.058824 | 3 | 2 | 100 | 0 |
def _default_values(self, n: int) -> list:
vals = itertools.cycle(["top", "bottom"])
return [next(vals) for _ in range(n)]
| 19,362 |
||
mwaskom/seaborn
|
a47b97e4b98c809db55cbd283de21acba89fe186
|
seaborn/_core/properties.py
|
Color.standardize
|
(self, val: ColorSpec)
| 568 | 574 |
def standardize(self, val: ColorSpec) -> RGBTuple | RGBATuple:
# Return color with alpha channel only if the input spec has it
# This is so that RGBA colors can override the Alpha property
if to_rgba(val) != to_rgba(val, 1):
return to_rgba(val)
else:
return to_rgb(val)
|
https://github.com/mwaskom/seaborn/blob/a47b97e4b98c809db55cbd283de21acba89fe186/project26/seaborn/_core/properties.py#L568-L574
| 26 |
[
0,
1,
2,
3,
4,
6
] | 85.714286 |
[] | 0 | false | 99.058824 | 7 | 2 | 100 | 0 |
def standardize(self, val: ColorSpec) -> RGBTuple | RGBATuple:
# Return color with alpha channel only if the input spec has it
# This is so that RGBA colors can override the Alpha property
if to_rgba(val) != to_rgba(val, 1):
return to_rgba(val)
else:
return to_rgb(val)
| 19,363 |
|||
mwaskom/seaborn
|
a47b97e4b98c809db55cbd283de21acba89fe186
|
seaborn/_core/properties.py
|
Color._standardize_color_sequence
|
(self, colors: ArrayLike)
|
Convert color sequence to RGB(A) array, preserving but not adding alpha.
|
Convert color sequence to RGB(A) array, preserving but not adding alpha.
| 576 | 589 |
def _standardize_color_sequence(self, colors: ArrayLike) -> ArrayLike:
"""Convert color sequence to RGB(A) array, preserving but not adding alpha."""
def has_alpha(x):
return to_rgba(x) != to_rgba(x, 1)
if isinstance(colors, np.ndarray):
needs_alpha = colors.shape[1] == 4
else:
needs_alpha = any(has_alpha(x) for x in colors)
if needs_alpha:
return to_rgba_array(colors)
else:
return to_rgba_array(colors)[:, :3]
|
https://github.com/mwaskom/seaborn/blob/a47b97e4b98c809db55cbd283de21acba89fe186/project26/seaborn/_core/properties.py#L576-L589
| 26 |
[
0,
1,
2,
3,
4,
5,
7,
8,
9,
10,
11,
12,
13
] | 92.857143 |
[
6
] | 7.142857 | false | 99.058824 | 14 | 4 | 92.857143 | 1 |
def _standardize_color_sequence(self, colors: ArrayLike) -> ArrayLike:
def has_alpha(x):
return to_rgba(x) != to_rgba(x, 1)
if isinstance(colors, np.ndarray):
needs_alpha = colors.shape[1] == 4
else:
needs_alpha = any(has_alpha(x) for x in colors)
if needs_alpha:
return to_rgba_array(colors)
else:
return to_rgba_array(colors)[:, :3]
| 19,364 |
|
mwaskom/seaborn
|
a47b97e4b98c809db55cbd283de21acba89fe186
|
seaborn/_core/properties.py
|
Color.infer_scale
|
(self, arg: Any, data: Series)
| 591 | 630 |
def infer_scale(self, arg: Any, data: Series) -> Scale:
# TODO when inferring Continuous without data, verify type
# TODO need to rethink the variable type system
# (e.g. boolean, ordered categories as Ordinal, etc)..
var_type = variable_type(data, boolean_type="boolean", strict_boolean=True)
if var_type == "boolean":
return Boolean(arg)
if isinstance(arg, (dict, list)):
return Nominal(arg)
if isinstance(arg, tuple):
if var_type == "categorical":
# TODO It seems reasonable to allow a gradient mapping for nominal
# scale but it also feels "technically" wrong. Should this infer
# Ordinal with categorical data and, if so, verify orderedness?
return Nominal(arg)
return Continuous(arg)
if callable(arg):
return Continuous(arg)
# TODO Do we accept str like "log", "pow", etc. for semantics?
if not isinstance(arg, str):
msg = " ".join([
f"A single scale argument for {self.variable} variables must be",
f"a string, dict, tuple, list, or callable, not {type(arg)}."
])
raise TypeError(msg)
if arg in QUAL_PALETTES:
return Nominal(arg)
elif var_type == "numeric":
return Continuous(arg)
# TODO implement scales for date variables and any others.
else:
return Nominal(arg)
|
https://github.com/mwaskom/seaborn/blob/a47b97e4b98c809db55cbd283de21acba89fe186/project26/seaborn/_core/properties.py#L591-L630
| 26 |
[
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
12,
13,
14,
15,
16,
17,
18,
19,
20,
21,
22,
23,
24,
25,
26,
27,
31,
32,
33,
34,
35,
36,
37,
39
] | 90 |
[] | 0 | false | 99.058824 | 40 | 9 | 100 | 0 |
def infer_scale(self, arg: Any, data: Series) -> Scale:
# TODO when inferring Continuous without data, verify type
# TODO need to rethink the variable type system
# (e.g. boolean, ordered categories as Ordinal, etc)..
var_type = variable_type(data, boolean_type="boolean", strict_boolean=True)
if var_type == "boolean":
return Boolean(arg)
if isinstance(arg, (dict, list)):
return Nominal(arg)
if isinstance(arg, tuple):
if var_type == "categorical":
# TODO It seems reasonable to allow a gradient mapping for nominal
# scale but it also feels "technically" wrong. Should this infer
# Ordinal with categorical data and, if so, verify orderedness?
return Nominal(arg)
return Continuous(arg)
if callable(arg):
return Continuous(arg)
# TODO Do we accept str like "log", "pow", etc. for semantics?
if not isinstance(arg, str):
msg = " ".join([
f"A single scale argument for {self.variable} variables must be",
f"a string, dict, tuple, list, or callable, not {type(arg)}."
])
raise TypeError(msg)
if arg in QUAL_PALETTES:
return Nominal(arg)
elif var_type == "numeric":
return Continuous(arg)
# TODO implement scales for date variables and any others.
else:
return Nominal(arg)
| 19,365 |
|||
mwaskom/seaborn
|
a47b97e4b98c809db55cbd283de21acba89fe186
|
seaborn/_core/properties.py
|
Color.get_mapping
|
(self, scale: Scale, data: Series)
|
return _mapping
|
Return a function that maps from data domain to color values.
|
Return a function that maps from data domain to color values.
| 632 | 671 |
def get_mapping(self, scale: Scale, data: Series) -> Mapping:
"""Return a function that maps from data domain to color values."""
# TODO what is best way to do this conditional?
# Should it be class-based or should classes have behavioral attributes?
if isinstance(scale, Nominal):
return self._get_nominal_mapping(scale, data)
elif isinstance(scale, Boolean):
return self._get_boolean_mapping(scale, data)
if scale.values is None:
# TODO Rethink best default continuous color gradient
mapping = color_palette("ch:", as_cmap=True)
elif isinstance(scale.values, tuple):
# TODO blend_palette will strip alpha, but we should support
# interpolation on all four channels
mapping = blend_palette(scale.values, as_cmap=True)
elif isinstance(scale.values, str):
# TODO for matplotlib colormaps this will clip extremes, which is
# different from what using the named colormap directly would do
# This may or may not be desireable.
mapping = color_palette(scale.values, as_cmap=True)
elif callable(scale.values):
mapping = scale.values
else:
scale_class = scale.__class__.__name__
msg = " ".join([
f"Scale values for {self.variable} with a {scale_class} mapping",
f"must be string, tuple, or callable; not {type(scale.values)}."
])
raise TypeError(msg)
def _mapping(x):
# Remove alpha channel so it does not override alpha property downstream
# TODO this will need to be more flexible to support RGBA tuples (see above)
invalid = ~np.isfinite(x)
out = mapping(x)[:, :3]
out[invalid] = np.nan
return out
return _mapping
|
https://github.com/mwaskom/seaborn/blob/a47b97e4b98c809db55cbd283de21acba89fe186/project26/seaborn/_core/properties.py#L632-L671
| 26 |
[
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
12,
13,
14,
15,
16,
17,
18,
19,
20,
21,
22,
23,
24,
25,
26,
27,
28,
29,
30,
31,
32,
33,
34,
35,
36,
37,
38,
39
] | 100 |
[] | 0 | true | 99.058824 | 40 | 8 | 100 | 1 |
def get_mapping(self, scale: Scale, data: Series) -> Mapping:
# TODO what is best way to do this conditional?
# Should it be class-based or should classes have behavioral attributes?
if isinstance(scale, Nominal):
return self._get_nominal_mapping(scale, data)
elif isinstance(scale, Boolean):
return self._get_boolean_mapping(scale, data)
if scale.values is None:
# TODO Rethink best default continuous color gradient
mapping = color_palette("ch:", as_cmap=True)
elif isinstance(scale.values, tuple):
# TODO blend_palette will strip alpha, but we should support
# interpolation on all four channels
mapping = blend_palette(scale.values, as_cmap=True)
elif isinstance(scale.values, str):
# TODO for matplotlib colormaps this will clip extremes, which is
# different from what using the named colormap directly would do
# This may or may not be desireable.
mapping = color_palette(scale.values, as_cmap=True)
elif callable(scale.values):
mapping = scale.values
else:
scale_class = scale.__class__.__name__
msg = " ".join([
f"Scale values for {self.variable} with a {scale_class} mapping",
f"must be string, tuple, or callable; not {type(scale.values)}."
])
raise TypeError(msg)
def _mapping(x):
# Remove alpha channel so it does not override alpha property downstream
# TODO this will need to be more flexible to support RGBA tuples (see above)
invalid = ~np.isfinite(x)
out = mapping(x)[:, :3]
out[invalid] = np.nan
return out
return _mapping
| 19,366 |
mwaskom/seaborn
|
a47b97e4b98c809db55cbd283de21acba89fe186
|
seaborn/_core/properties.py
|
Color._get_nominal_mapping
|
(self, scale: Nominal, data: Series)
|
return mapping
| 673 | 685 |
def _get_nominal_mapping(self, scale: Nominal, data: Series) -> Mapping:
levels = categorical_order(data, scale.order)
colors = self._get_values(scale, levels)
def mapping(x):
ixs = np.asarray(x, np.intp)
use = np.isfinite(x)
out = np.full((len(ixs), colors.shape[1]), np.nan)
out[use] = np.take(colors, ixs[use], axis=0)
return out
return mapping
|
https://github.com/mwaskom/seaborn/blob/a47b97e4b98c809db55cbd283de21acba89fe186/project26/seaborn/_core/properties.py#L673-L685
| 26 |
[
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
12
] | 100 |
[] | 0 | true | 99.058824 | 13 | 2 | 100 | 0 |
def _get_nominal_mapping(self, scale: Nominal, data: Series) -> Mapping:
levels = categorical_order(data, scale.order)
colors = self._get_values(scale, levels)
def mapping(x):
ixs = np.asarray(x, np.intp)
use = np.isfinite(x)
out = np.full((len(ixs), colors.shape[1]), np.nan)
out[use] = np.take(colors, ixs[use], axis=0)
return out
return mapping
| 19,367 |
||
mwaskom/seaborn
|
a47b97e4b98c809db55cbd283de21acba89fe186
|
seaborn/_core/properties.py
|
Color._get_boolean_mapping
|
(self, scale: Boolean, data: Series)
|
return mapping
| 687 | 700 |
def _get_boolean_mapping(self, scale: Boolean, data: Series) -> Mapping:
colors = self._get_values(scale, [True, False])
def mapping(x):
use = np.isfinite(x)
x = np.asarray(x).astype(bool)
out = np.full((len(x), colors.shape[1]), np.nan)
out[x & use] = colors[0]
out[~x & use] = colors[1]
return out
return mapping
|
https://github.com/mwaskom/seaborn/blob/a47b97e4b98c809db55cbd283de21acba89fe186/project26/seaborn/_core/properties.py#L687-L700
| 26 |
[
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
12,
13
] | 100 |
[] | 0 | true | 99.058824 | 14 | 2 | 100 | 0 |
def _get_boolean_mapping(self, scale: Boolean, data: Series) -> Mapping:
colors = self._get_values(scale, [True, False])
def mapping(x):
use = np.isfinite(x)
x = np.asarray(x).astype(bool)
out = np.full((len(x), colors.shape[1]), np.nan)
out[x & use] = colors[0]
out[~x & use] = colors[1]
return out
return mapping
| 19,368 |
||
mwaskom/seaborn
|
a47b97e4b98c809db55cbd283de21acba89fe186
|
seaborn/_core/properties.py
|
Color._get_values
|
(self, scale: Scale, levels: list)
|
return self._standardize_color_sequence(colors)
|
Validate scale.values and identify a value for each level.
|
Validate scale.values and identify a value for each level.
| 702 | 729 |
def _get_values(self, scale: Scale, levels: list) -> ArrayLike:
"""Validate scale.values and identify a value for each level."""
n = len(levels)
values = scale.values
if isinstance(values, dict):
self._check_dict_entries(levels, values)
colors = [values[x] for x in levels]
elif isinstance(values, list):
colors = self._check_list_length(levels, values)
elif isinstance(values, tuple):
colors = blend_palette(values, n)
elif isinstance(values, str):
colors = color_palette(values, n)
elif values is None:
if n <= len(get_color_cycle()):
# Use current (global) default palette
colors = color_palette(n_colors=n)
else:
colors = color_palette("husl", n)
else:
scale_class = scale.__class__.__name__
msg = " ".join([
f"Scale values for {self.variable} with a {scale_class} mapping",
f"must be string, list, tuple, or dict; not {type(scale.values)}."
])
raise TypeError(msg)
return self._standardize_color_sequence(colors)
|
https://github.com/mwaskom/seaborn/blob/a47b97e4b98c809db55cbd283de21acba89fe186/project26/seaborn/_core/properties.py#L702-L729
| 26 |
[
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
12,
13,
14,
15,
16,
17,
18,
19,
20,
21,
22,
23,
24,
25,
26,
27
] | 100 |
[] | 0 | true | 99.058824 | 28 | 8 | 100 | 1 |
def _get_values(self, scale: Scale, levels: list) -> ArrayLike:
n = len(levels)
values = scale.values
if isinstance(values, dict):
self._check_dict_entries(levels, values)
colors = [values[x] for x in levels]
elif isinstance(values, list):
colors = self._check_list_length(levels, values)
elif isinstance(values, tuple):
colors = blend_palette(values, n)
elif isinstance(values, str):
colors = color_palette(values, n)
elif values is None:
if n <= len(get_color_cycle()):
# Use current (global) default palette
colors = color_palette(n_colors=n)
else:
colors = color_palette("husl", n)
else:
scale_class = scale.__class__.__name__
msg = " ".join([
f"Scale values for {self.variable} with a {scale_class} mapping",
f"must be string, list, tuple, or dict; not {type(scale.values)}."
])
raise TypeError(msg)
return self._standardize_color_sequence(colors)
| 19,369 |
mwaskom/seaborn
|
a47b97e4b98c809db55cbd283de21acba89fe186
|
seaborn/_core/properties.py
|
Fill.default_scale
|
(self, data: Series)
|
return Boolean() if var_type == "boolean" else Nominal()
| 742 | 744 |
def default_scale(self, data: Series) -> Scale:
var_type = variable_type(data, boolean_type="boolean", strict_boolean=True)
return Boolean() if var_type == "boolean" else Nominal()
|
https://github.com/mwaskom/seaborn/blob/a47b97e4b98c809db55cbd283de21acba89fe186/project26/seaborn/_core/properties.py#L742-L744
| 26 |
[
0,
1,
2
] | 100 |
[] | 0 | true | 99.058824 | 3 | 1 | 100 | 0 |
def default_scale(self, data: Series) -> Scale:
var_type = variable_type(data, boolean_type="boolean", strict_boolean=True)
return Boolean() if var_type == "boolean" else Nominal()
| 19,370 |
||
mwaskom/seaborn
|
a47b97e4b98c809db55cbd283de21acba89fe186
|
seaborn/_core/properties.py
|
Fill.infer_scale
|
(self, arg: Any, data: Series)
|
return Boolean(arg) if var_type == "boolean" else Nominal(arg)
| 746 | 748 |
def infer_scale(self, arg: Any, data: Series) -> Scale:
var_type = variable_type(data, boolean_type="boolean", strict_boolean=True)
return Boolean(arg) if var_type == "boolean" else Nominal(arg)
|
https://github.com/mwaskom/seaborn/blob/a47b97e4b98c809db55cbd283de21acba89fe186/project26/seaborn/_core/properties.py#L746-L748
| 26 |
[
0,
1,
2
] | 100 |
[] | 0 | true | 99.058824 | 3 | 1 | 100 | 0 |
def infer_scale(self, arg: Any, data: Series) -> Scale:
var_type = variable_type(data, boolean_type="boolean", strict_boolean=True)
return Boolean(arg) if var_type == "boolean" else Nominal(arg)
| 19,371 |
||
mwaskom/seaborn
|
a47b97e4b98c809db55cbd283de21acba89fe186
|
seaborn/_core/properties.py
|
Fill.standardize
|
(self, val: Any)
|
return bool(val)
| 750 | 751 |
def standardize(self, val: Any) -> bool:
return bool(val)
|
https://github.com/mwaskom/seaborn/blob/a47b97e4b98c809db55cbd283de21acba89fe186/project26/seaborn/_core/properties.py#L750-L751
| 26 |
[
0,
1
] | 100 |
[] | 0 | true | 99.058824 | 2 | 1 | 100 | 0 |
def standardize(self, val: Any) -> bool:
return bool(val)
| 19,372 |
||
mwaskom/seaborn
|
a47b97e4b98c809db55cbd283de21acba89fe186
|
seaborn/_core/properties.py
|
Fill._default_values
|
(self, n: int)
|
return [x for x, _ in zip(itertools.cycle([True, False]), range(n))]
|
Return a list of n values, alternating True and False.
|
Return a list of n values, alternating True and False.
| 753 | 762 |
def _default_values(self, n: int) -> list:
"""Return a list of n values, alternating True and False."""
if n > 2:
msg = " ".join([
f"The variable assigned to {self.variable} has more than two levels,",
f"so {self.variable} values will cycle and may be uninterpretable",
])
# TODO fire in a "nice" way (see above)
warnings.warn(msg, UserWarning)
return [x for x, _ in zip(itertools.cycle([True, False]), range(n))]
|
https://github.com/mwaskom/seaborn/blob/a47b97e4b98c809db55cbd283de21acba89fe186/project26/seaborn/_core/properties.py#L753-L762
| 26 |
[
0,
1,
2,
3,
4,
5,
6,
7,
8,
9
] | 100 |
[] | 0 | true | 99.058824 | 10 | 3 | 100 | 1 |
def _default_values(self, n: int) -> list:
if n > 2:
msg = " ".join([
f"The variable assigned to {self.variable} has more than two levels,",
f"so {self.variable} values will cycle and may be uninterpretable",
])
# TODO fire in a "nice" way (see above)
warnings.warn(msg, UserWarning)
return [x for x, _ in zip(itertools.cycle([True, False]), range(n))]
| 19,373 |
mwaskom/seaborn
|
a47b97e4b98c809db55cbd283de21acba89fe186
|
seaborn/_core/properties.py
|
Fill.get_mapping
|
(self, scale: Scale, data: Series)
|
return mapping
|
Return a function that maps each data value to True or False.
|
Return a function that maps each data value to True or False.
| 764 | 781 |
def get_mapping(self, scale: Scale, data: Series) -> Mapping:
"""Return a function that maps each data value to True or False."""
boolean_scale = isinstance(scale, Boolean)
order = getattr(scale, "order", [True, False] if boolean_scale else None)
levels = categorical_order(data, order)
values = self._get_values(scale, levels)
if boolean_scale:
values = values[::-1]
def mapping(x):
ixs = np.asarray(x, np.intp)
return [
values[ix] if np.isfinite(x_i) else False
for x_i, ix in zip(x, ixs)
]
return mapping
|
https://github.com/mwaskom/seaborn/blob/a47b97e4b98c809db55cbd283de21acba89fe186/project26/seaborn/_core/properties.py#L764-L781
| 26 |
[
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
12,
13,
14,
15,
16,
17
] | 100 |
[] | 0 | true | 99.058824 | 18 | 4 | 100 | 1 |
def get_mapping(self, scale: Scale, data: Series) -> Mapping:
boolean_scale = isinstance(scale, Boolean)
order = getattr(scale, "order", [True, False] if boolean_scale else None)
levels = categorical_order(data, order)
values = self._get_values(scale, levels)
if boolean_scale:
values = values[::-1]
def mapping(x):
ixs = np.asarray(x, np.intp)
return [
values[ix] if np.isfinite(x_i) else False
for x_i, ix in zip(x, ixs)
]
return mapping
| 19,374 |
mwaskom/seaborn
|
a47b97e4b98c809db55cbd283de21acba89fe186
|
seaborn/_core/properties.py
|
Fill._get_values
|
(self, scale: Scale, levels: list)
|
return values
|
Validate scale.values and identify a value for each level.
|
Validate scale.values and identify a value for each level.
| 783 | 798 |
def _get_values(self, scale: Scale, levels: list) -> list:
"""Validate scale.values and identify a value for each level."""
if isinstance(scale.values, list):
values = [bool(x) for x in scale.values]
elif isinstance(scale.values, dict):
values = [bool(scale.values[x]) for x in levels]
elif scale.values is None:
values = self._default_values(len(levels))
else:
msg = " ".join([
f"Scale values for {self.variable} must be passed in",
f"a list or dict; not {type(scale.values)}."
])
raise TypeError(msg)
return values
|
https://github.com/mwaskom/seaborn/blob/a47b97e4b98c809db55cbd283de21acba89fe186/project26/seaborn/_core/properties.py#L783-L798
| 26 |
[
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
12,
13,
14,
15
] | 100 |
[] | 0 | true | 99.058824 | 16 | 6 | 100 | 1 |
def _get_values(self, scale: Scale, levels: list) -> list:
if isinstance(scale.values, list):
values = [bool(x) for x in scale.values]
elif isinstance(scale.values, dict):
values = [bool(scale.values[x]) for x in levels]
elif scale.values is None:
values = self._default_values(len(levels))
else:
msg = " ".join([
f"Scale values for {self.variable} must be passed in",
f"a list or dict; not {type(scale.values)}."
])
raise TypeError(msg)
return values
| 19,375 |
mwaskom/seaborn
|
a47b97e4b98c809db55cbd283de21acba89fe186
|
seaborn/_core/typing.py
|
Default.__repr__
|
(self)
|
return "<default>"
| 35 | 36 |
def __repr__(self):
return "<default>"
|
https://github.com/mwaskom/seaborn/blob/a47b97e4b98c809db55cbd283de21acba89fe186/project26/seaborn/_core/typing.py#L35-L36
| 26 |
[
0,
1
] | 100 |
[] | 0 | true | 100 | 2 | 1 | 100 | 0 |
def __repr__(self):
return "<default>"
| 19,376 |
||
Suor/funcy
|
5f23815f224e595b8a54822bcb330cd31a799c78
|
funcy/funcmakers.py
|
make_func
|
(f, builtin=False, test=False)
| 10 | 26 |
def make_func(f, builtin=False, test=False):
if callable(f):
return f
elif f is None:
# pass None to builtin as predicate or mapping function for speed
return None if builtin else \
bool if test else lambda x: x
elif isinstance(f, (basestring, _re_type)):
return re_tester(f) if test else re_finder(f)
elif isinstance(f, (int, slice)):
return itemgetter(f)
elif isinstance(f, Mapping):
return f.__getitem__
elif isinstance(f, Set):
return f.__contains__
else:
raise TypeError("Can't make a func from %s" % f.__class__.__name__)
|
https://github.com/Suor/funcy/blob/5f23815f224e595b8a54822bcb330cd31a799c78/project29/funcy/funcmakers.py#L10-L26
| 29 |
[
0,
1,
2,
3,
4,
5,
7,
8,
9,
10,
11,
12,
13,
14
] | 82.352941 |
[
16
] | 5.882353 | false | 95 | 17 | 7 | 94.117647 | 0 |
def make_func(f, builtin=False, test=False):
if callable(f):
return f
elif f is None:
# pass None to builtin as predicate or mapping function for speed
return None if builtin else \
bool if test else lambda x: x
elif isinstance(f, (basestring, _re_type)):
return re_tester(f) if test else re_finder(f)
elif isinstance(f, (int, slice)):
return itemgetter(f)
elif isinstance(f, Mapping):
return f.__getitem__
elif isinstance(f, Set):
return f.__contains__
else:
raise TypeError("Can't make a func from %s" % f.__class__.__name__)
| 19,943 |
|||
Suor/funcy
|
5f23815f224e595b8a54822bcb330cd31a799c78
|
funcy/funcmakers.py
|
make_pred
|
(pred, builtin=False)
|
return make_func(pred, builtin=builtin, test=True)
| 28 | 29 |
def make_pred(pred, builtin=False):
return make_func(pred, builtin=builtin, test=True)
|
https://github.com/Suor/funcy/blob/5f23815f224e595b8a54822bcb330cd31a799c78/project29/funcy/funcmakers.py#L28-L29
| 29 |
[
0,
1
] | 100 |
[] | 0 | true | 95 | 2 | 1 | 100 | 0 |
def make_pred(pred, builtin=False):
return make_func(pred, builtin=builtin, test=True)
| 19,944 |
||
Suor/funcy
|
5f23815f224e595b8a54822bcb330cd31a799c78
|
funcy/debug.py
|
tap
|
(x, label=None)
|
return x
|
Prints x and then returns it.
|
Prints x and then returns it.
| 26 | 32 |
def tap(x, label=None):
"""Prints x and then returns it."""
if label:
print('%s: %s' % (label, x))
else:
print(x)
return x
|
https://github.com/Suor/funcy/blob/5f23815f224e595b8a54822bcb330cd31a799c78/project29/funcy/debug.py#L26-L32
| 29 |
[
0,
1,
2,
3,
4,
5,
6
] | 100 |
[] | 0 | true | 82.781457 | 7 | 2 | 100 | 1 |
def tap(x, label=None):
if label:
print('%s: %s' % (label, x))
else:
print(x)
return x
| 19,945 |
Suor/funcy
|
5f23815f224e595b8a54822bcb330cd31a799c78
|
funcy/debug.py
|
log_calls
|
(call, print_func, errors=True, stack=True, repr_len=REPR_LEN)
|
Logs or prints all function calls,
including arguments, results and raised exceptions.
|
Logs or prints all function calls,
including arguments, results and raised exceptions.
| 36 | 49 |
def log_calls(call, print_func, errors=True, stack=True, repr_len=REPR_LEN):
"""Logs or prints all function calls,
including arguments, results and raised exceptions."""
signature = signature_repr(call, repr_len)
try:
print_func('Call %s' % signature)
result = call()
# NOTE: using full repr of result
print_func('-> %s from %s' % (smart_repr(result, max_len=None), signature))
return result
except BaseException as e:
if errors:
print_func('-> ' + _format_error(signature, e, stack))
raise
|
https://github.com/Suor/funcy/blob/5f23815f224e595b8a54822bcb330cd31a799c78/project29/funcy/debug.py#L36-L49
| 29 |
[
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
12,
13
] | 100 |
[] | 0 | true | 82.781457 | 14 | 3 | 100 | 2 |
def log_calls(call, print_func, errors=True, stack=True, repr_len=REPR_LEN):
signature = signature_repr(call, repr_len)
try:
print_func('Call %s' % signature)
result = call()
# NOTE: using full repr of result
print_func('-> %s from %s' % (smart_repr(result, max_len=None), signature))
return result
except BaseException as e:
if errors:
print_func('-> ' + _format_error(signature, e, stack))
raise
| 19,946 |
|
Suor/funcy
|
5f23815f224e595b8a54822bcb330cd31a799c78
|
funcy/debug.py
|
print_calls
|
(errors=True, stack=True, repr_len=REPR_LEN)
| 51 | 55 |
def print_calls(errors=True, stack=True, repr_len=REPR_LEN):
if callable(errors):
return log_calls(print)(errors)
else:
return log_calls(print, errors, stack, repr_len)
|
https://github.com/Suor/funcy/blob/5f23815f224e595b8a54822bcb330cd31a799c78/project29/funcy/debug.py#L51-L55
| 29 |
[
0,
1,
2,
4
] | 80 |
[] | 0 | false | 82.781457 | 5 | 2 | 100 | 0 |
def print_calls(errors=True, stack=True, repr_len=REPR_LEN):
if callable(errors):
return log_calls(print)(errors)
else:
return log_calls(print, errors, stack, repr_len)
| 19,947 |
|||
Suor/funcy
|
5f23815f224e595b8a54822bcb330cd31a799c78
|
funcy/debug.py
|
log_enters
|
(call, print_func, repr_len=REPR_LEN)
|
return call()
|
Logs each entrance to a function.
|
Logs each entrance to a function.
| 60 | 63 |
def log_enters(call, print_func, repr_len=REPR_LEN):
"""Logs each entrance to a function."""
print_func('Call %s' % signature_repr(call, repr_len))
return call()
|
https://github.com/Suor/funcy/blob/5f23815f224e595b8a54822bcb330cd31a799c78/project29/funcy/debug.py#L60-L63
| 29 |
[
0,
1
] | 50 |
[
2,
3
] | 50 | false | 82.781457 | 4 | 1 | 50 | 1 |
def log_enters(call, print_func, repr_len=REPR_LEN):
print_func('Call %s' % signature_repr(call, repr_len))
return call()
| 19,948 |
Suor/funcy
|
5f23815f224e595b8a54822bcb330cd31a799c78
|
funcy/debug.py
|
print_enters
|
(repr_len=REPR_LEN)
|
Prints on each entrance to a function.
|
Prints on each entrance to a function.
| 66 | 71 |
def print_enters(repr_len=REPR_LEN):
"""Prints on each entrance to a function."""
if callable(repr_len):
return log_enters(print)(repr_len)
else:
return log_enters(print, repr_len)
|
https://github.com/Suor/funcy/blob/5f23815f224e595b8a54822bcb330cd31a799c78/project29/funcy/debug.py#L66-L71
| 29 |
[
0,
1
] | 33.333333 |
[
2,
3,
5
] | 50 | false | 82.781457 | 6 | 2 | 50 | 1 |
def print_enters(repr_len=REPR_LEN):
if callable(repr_len):
return log_enters(print)(repr_len)
else:
return log_enters(print, repr_len)
| 19,949 |
|
Suor/funcy
|
5f23815f224e595b8a54822bcb330cd31a799c78
|
funcy/debug.py
|
log_exits
|
(call, print_func, errors=True, stack=True, repr_len=REPR_LEN)
|
Logs exits from a function.
|
Logs exits from a function.
| 75 | 86 |
def log_exits(call, print_func, errors=True, stack=True, repr_len=REPR_LEN):
"""Logs exits from a function."""
signature = signature_repr(call, repr_len)
try:
result = call()
# NOTE: using full repr of result
print_func('-> %s from %s' % (smart_repr(result, max_len=None), signature))
return result
except BaseException as e:
if errors:
print_func('-> ' + _format_error(signature, e, stack))
raise
|
https://github.com/Suor/funcy/blob/5f23815f224e595b8a54822bcb330cd31a799c78/project29/funcy/debug.py#L75-L86
| 29 |
[
0,
1
] | 16.666667 |
[
2,
3,
4,
6,
7,
8,
9,
10,
11
] | 75 | false | 82.781457 | 12 | 3 | 25 | 1 |
def log_exits(call, print_func, errors=True, stack=True, repr_len=REPR_LEN):
signature = signature_repr(call, repr_len)
try:
result = call()
# NOTE: using full repr of result
print_func('-> %s from %s' % (smart_repr(result, max_len=None), signature))
return result
except BaseException as e:
if errors:
print_func('-> ' + _format_error(signature, e, stack))
raise
| 19,950 |
|
Suor/funcy
|
5f23815f224e595b8a54822bcb330cd31a799c78
|
funcy/debug.py
|
print_exits
|
(errors=True, stack=True, repr_len=REPR_LEN)
|
Prints on exits from a function.
|
Prints on exits from a function.
| 88 | 93 |
def print_exits(errors=True, stack=True, repr_len=REPR_LEN):
"""Prints on exits from a function."""
if callable(errors):
return log_exits(print)(errors)
else:
return log_exits(print, errors, stack, repr_len)
|
https://github.com/Suor/funcy/blob/5f23815f224e595b8a54822bcb330cd31a799c78/project29/funcy/debug.py#L88-L93
| 29 |
[
0,
1
] | 33.333333 |
[
2,
3,
5
] | 50 | false | 82.781457 | 6 | 2 | 50 | 1 |
def print_exits(errors=True, stack=True, repr_len=REPR_LEN):
if callable(errors):
return log_exits(print)(errors)
else:
return log_exits(print, errors, stack, repr_len)
| 19,951 |
|
Suor/funcy
|
5f23815f224e595b8a54822bcb330cd31a799c78
|
funcy/debug.py
|
format_time
|
(sec)
| 145 | 153 |
def format_time(sec):
if sec < 1e-6:
return '%8.2f ns' % (sec * 1e9)
elif sec < 1e-3:
return '%8.2f mks' % (sec * 1e6)
elif sec < 1:
return '%8.2f ms' % (sec * 1e3)
else:
return '%8.2f s' % sec
|
https://github.com/Suor/funcy/blob/5f23815f224e595b8a54822bcb330cd31a799c78/project29/funcy/debug.py#L145-L153
| 29 |
[
0,
1,
2,
3,
4,
5,
6
] | 77.777778 |
[
8
] | 11.111111 | false | 82.781457 | 9 | 4 | 88.888889 | 0 |
def format_time(sec):
if sec < 1e-6:
return '%8.2f ns' % (sec * 1e9)
elif sec < 1e-3:
return '%8.2f mks' % (sec * 1e6)
elif sec < 1:
return '%8.2f ms' % (sec * 1e3)
else:
return '%8.2f s' % sec
| 19,952 |
|||
Suor/funcy
|
5f23815f224e595b8a54822bcb330cd31a799c78
|
funcy/debug.py
|
log_iter_durations
|
(seq, print_func, label=None, unit='auto')
|
Times processing of each item in seq.
|
Times processing of each item in seq.
| 186 | 197 |
def log_iter_durations(seq, print_func, label=None, unit='auto'):
"""Times processing of each item in seq."""
if unit not in time_formatters:
raise ValueError('Unknown time unit: %s. It should be ns, mks, ms, s or auto.' % unit)
_format_time = time_formatters[unit]
suffix = " of %s" % label if label else ""
it = iter(seq)
for i, item in enumerate(it):
start = timer()
yield item
duration = _format_time(timer() - start)
print_func("%s in iteration %d%s" % (duration, i, suffix))
|
https://github.com/Suor/funcy/blob/5f23815f224e595b8a54822bcb330cd31a799c78/project29/funcy/debug.py#L186-L197
| 29 |
[
0,
1,
2,
4,
5,
6,
7,
8,
9,
10,
11
] | 91.666667 |
[
3
] | 8.333333 | false | 82.781457 | 12 | 3 | 91.666667 | 1 |
def log_iter_durations(seq, print_func, label=None, unit='auto'):
if unit not in time_formatters:
raise ValueError('Unknown time unit: %s. It should be ns, mks, ms, s or auto.' % unit)
_format_time = time_formatters[unit]
suffix = " of %s" % label if label else ""
it = iter(seq)
for i, item in enumerate(it):
start = timer()
yield item
duration = _format_time(timer() - start)
print_func("%s in iteration %d%s" % (duration, i, suffix))
| 19,953 |
|
Suor/funcy
|
5f23815f224e595b8a54822bcb330cd31a799c78
|
funcy/debug.py
|
print_iter_durations
|
(seq, label=None, unit='auto')
|
return log_iter_durations(seq, print, label, unit=unit)
|
Times processing of each item in seq.
|
Times processing of each item in seq.
| 199 | 201 |
def print_iter_durations(seq, label=None, unit='auto'):
"""Times processing of each item in seq."""
return log_iter_durations(seq, print, label, unit=unit)
|
https://github.com/Suor/funcy/blob/5f23815f224e595b8a54822bcb330cd31a799c78/project29/funcy/debug.py#L199-L201
| 29 |
[
0,
1
] | 66.666667 |
[
2
] | 33.333333 | false | 82.781457 | 3 | 1 | 66.666667 | 1 |
def print_iter_durations(seq, label=None, unit='auto'):
return log_iter_durations(seq, print, label, unit=unit)
| 19,954 |
Suor/funcy
|
5f23815f224e595b8a54822bcb330cd31a799c78
|
funcy/debug.py
|
_format_error
|
(label, e, stack=True)
| 206 | 219 |
def _format_error(label, e, stack=True):
if isinstance(e, Exception):
if stack:
e_message = traceback.format_exc()
else:
e_message = '%s: %s' % (e.__class__.__name__, e)
else:
e_message = e
if label:
template = '%s raised in %s' if stack else '%s raised in %s'
return template % (e_message, label)
else:
return e_message
|
https://github.com/Suor/funcy/blob/5f23815f224e595b8a54822bcb330cd31a799c78/project29/funcy/debug.py#L206-L219
| 29 |
[
0,
1,
2,
5,
7,
8,
9,
10,
11,
13
] | 71.428571 |
[
3
] | 7.142857 | false | 82.781457 | 14 | 4 | 92.857143 | 0 |
def _format_error(label, e, stack=True):
if isinstance(e, Exception):
if stack:
e_message = traceback.format_exc()
else:
e_message = '%s: %s' % (e.__class__.__name__, e)
else:
e_message = e
if label:
template = '%s raised in %s' if stack else '%s raised in %s'
return template % (e_message, label)
else:
return e_message
| 19,955 |
|||
Suor/funcy
|
5f23815f224e595b8a54822bcb330cd31a799c78
|
funcy/debug.py
|
signature_repr
|
(call, repr_len=REPR_LEN)
|
return '%s(%s)' % (name, ', '.join(chain(args_repr, kwargs_repr)))
| 224 | 235 |
def signature_repr(call, repr_len=REPR_LEN):
if isinstance(call._func, partial):
if hasattr(call._func.func, '__name__'):
name = '<%s partial>' % call._func.func.__name__
else:
name = '<unknown partial>'
else:
name = getattr(call._func, '__name__', '<unknown>')
args_repr = (smart_repr(arg, repr_len) for arg in call._args)
kwargs_repr = ('%s=%s' % (key, smart_repr(value, repr_len))
for key, value in call._kwargs.items())
return '%s(%s)' % (name, ', '.join(chain(args_repr, kwargs_repr)))
|
https://github.com/Suor/funcy/blob/5f23815f224e595b8a54822bcb330cd31a799c78/project29/funcy/debug.py#L224-L235
| 29 |
[
0,
1,
7,
8,
9,
11
] | 50 |
[
2,
3,
5
] | 25 | false | 82.781457 | 12 | 3 | 75 | 0 |
def signature_repr(call, repr_len=REPR_LEN):
if isinstance(call._func, partial):
if hasattr(call._func.func, '__name__'):
name = '<%s partial>' % call._func.func.__name__
else:
name = '<unknown partial>'
else:
name = getattr(call._func, '__name__', '<unknown>')
args_repr = (smart_repr(arg, repr_len) for arg in call._args)
kwargs_repr = ('%s=%s' % (key, smart_repr(value, repr_len))
for key, value in call._kwargs.items())
return '%s(%s)' % (name, ', '.join(chain(args_repr, kwargs_repr)))
| 19,956 |
||
Suor/funcy
|
5f23815f224e595b8a54822bcb330cd31a799c78
|
funcy/debug.py
|
smart_repr
|
(value, max_len=REPR_LEN)
|
return res
| 237 | 246 |
def smart_repr(value, max_len=REPR_LEN):
if isinstance(value, basestring):
res = repr(value)
else:
res = str(value)
res = re.sub(r'\s+', ' ', res)
if max_len and len(res) > max_len:
res = res[:max_len-3] + '...'
return res
|
https://github.com/Suor/funcy/blob/5f23815f224e595b8a54822bcb330cd31a799c78/project29/funcy/debug.py#L237-L246
| 29 |
[
0,
1,
2,
4,
5,
6,
7,
9
] | 80 |
[
8
] | 10 | false | 82.781457 | 10 | 4 | 90 | 0 |
def smart_repr(value, max_len=REPR_LEN):
if isinstance(value, basestring):
res = repr(value)
else:
res = str(value)
res = re.sub(r'\s+', ' ', res)
if max_len and len(res) > max_len:
res = res[:max_len-3] + '...'
return res
| 19,957 |
||
Suor/funcy
|
5f23815f224e595b8a54822bcb330cd31a799c78
|
funcy/debug.py
|
LabeledContextDecorator.__init__
|
(self, print_func, label=None, repr_len=REPR_LEN)
| 100 | 103 |
def __init__(self, print_func, label=None, repr_len=REPR_LEN):
self.print_func = print_func
self.label = label
self.repr_len = repr_len
|
https://github.com/Suor/funcy/blob/5f23815f224e595b8a54822bcb330cd31a799c78/project29/funcy/debug.py#L100-L103
| 29 |
[
0,
1,
2,
3
] | 100 |
[] | 0 | true | 82.781457 | 4 | 1 | 100 | 0 |
def __init__(self, print_func, label=None, repr_len=REPR_LEN):
self.print_func = print_func
self.label = label
self.repr_len = repr_len
| 19,958 |
|||
Suor/funcy
|
5f23815f224e595b8a54822bcb330cd31a799c78
|
funcy/debug.py
|
LabeledContextDecorator.__call__
|
(self, label=None, **kwargs)
| 105 | 109 |
def __call__(self, label=None, **kwargs):
if callable(label):
return self.decorator(label)
else:
return self.__class__(self.print_func, label, **kwargs)
|
https://github.com/Suor/funcy/blob/5f23815f224e595b8a54822bcb330cd31a799c78/project29/funcy/debug.py#L105-L109
| 29 |
[
0,
1,
2,
4
] | 80 |
[] | 0 | false | 82.781457 | 5 | 2 | 100 | 0 |
def __call__(self, label=None, **kwargs):
if callable(label):
return self.decorator(label)
else:
return self.__class__(self.print_func, label, **kwargs)
| 19,959 |
|||
Suor/funcy
|
5f23815f224e595b8a54822bcb330cd31a799c78
|
funcy/debug.py
|
LabeledContextDecorator.decorator
|
(self, func)
|
return inner
| 111 | 120 |
def decorator(self, func):
@wraps(func)
def inner(*args, **kwargs):
# Recreate self with a new label so that nested and recursive calls will work
cm = self.__class__.__new__(self.__class__)
cm.__dict__.update(self.__dict__)
cm.label = signature_repr(Call(func, args, kwargs), self.repr_len)
with cm:
return func(*args, **kwargs)
return inner
|
https://github.com/Suor/funcy/blob/5f23815f224e595b8a54822bcb330cd31a799c78/project29/funcy/debug.py#L111-L120
| 29 |
[
0,
1,
2,
3,
4,
5,
6,
7,
8,
9
] | 100 |
[] | 0 | true | 82.781457 | 10 | 3 | 100 | 0 |
def decorator(self, func):
@wraps(func)
def inner(*args, **kwargs):
# Recreate self with a new label so that nested and recursive calls will work
cm = self.__class__.__new__(self.__class__)
cm.__dict__.update(self.__dict__)
cm.label = signature_repr(Call(func, args, kwargs), self.repr_len)
with cm:
return func(*args, **kwargs)
return inner
| 19,960 |
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