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Akshay0724/scikit-learn | examples/preprocessing/plot_scaling_importance.py | 45 | 5269 | #!/usr/bin/python
# -*- coding: utf-8 -*-
"""
=========================================================
Importance of Feature Scaling
=========================================================
Feature scaling though standardization (or Z-score normalization)
can be an important preprocessing step for many machine learning
algorithms. Standardization involves rescaling the features such
that they have the properties of a standard normal distribution
with a mean of zero and a standard deviation of one.
While many algorithms (such as SVM, K-nearest neighbors, and logistic
regression) require features to be normalized, intuitively we can
think of Principle Component Analysis (PCA) as being a prime example
of when normalization is important. In PCA we are interested in the
components that maximize the variance. If one component (e.g. human
height) varies less than another (e.g. weight) because of their
respective scales (meters vs. kilos), PCA might determine that the
direction of maximal variance more closely corresponds with the
'weight' axis, if those features are not scaled. As a change in
height of one meter can be considered much more important than the
change in weight of one kilogram, this is clearly incorrect.
To illustrate this, PCA is performed comparing the use of data with
:class:`StandardScaler <sklearn.preprocessing.StandardScaler>` applied,
to unscaled data. The results are visualized and a clear difference noted.
The 1st principal component in the unscaled set can be seen. It can be seen
that feature #13 dominates the direction, being a whole two orders of
magnitude above the other features. This is contrasted when observing
the principal component for the scaled version of the data. In the scaled
version, the orders of magnitude are roughly the same across all the features.
The dataset used is the Wine Dataset available at UCI. This dataset
has continuous features that are heterogeneous in scale due to differing
properties that they measure (i.e alcohol content, and malic acid).
The transformed data is then used to train a naive Bayes classifier, and a
clear difference in prediction accuracies is observed wherein the dataset
which is scaled before PCA vastly outperforms the unscaled version.
"""
from __future__ import print_function
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA
from sklearn.naive_bayes import GaussianNB
from sklearn import metrics
import matplotlib.pyplot as plt
from sklearn.datasets import load_wine
from sklearn.pipeline import make_pipeline
print(__doc__)
# Code source: Tyler Lanigan <[email protected]>
# Sebastian Raschka <[email protected]>
# License: BSD 3 clause
RANDOM_STATE = 42
FIG_SIZE = (10, 7)
features, target = load_wine(return_X_y=True)
# Make a train/test split using 30% test size
X_train, X_test, y_train, y_test = train_test_split(features, target,
test_size=0.30,
random_state=RANDOM_STATE)
# Fit to data and predict using pipelined GNB and PCA.
unscaled_clf = make_pipeline(PCA(n_components=2), GaussianNB())
unscaled_clf.fit(X_train, y_train)
pred_test = unscaled_clf.predict(X_test)
# Fit to data and predict using pipelined scaling, GNB and PCA.
std_clf = make_pipeline(StandardScaler(), PCA(n_components=2), GaussianNB())
std_clf.fit(X_train, y_train)
pred_test_std = std_clf.predict(X_test)
# Show prediction accuracies in scaled and unscaled data.
print('\nPrediction accuracy for the normal test dataset with PCA')
print('{:.2%}\n'.format(metrics.accuracy_score(y_test, pred_test)))
print('\nPrediction accuracy for the standardized test dataset with PCA')
print('{:.2%}\n'.format(metrics.accuracy_score(y_test, pred_test_std)))
# Extract PCA from pipeline
pca = unscaled_clf.named_steps['pca']
pca_std = std_clf.named_steps['pca']
# Show first principal componenets
print('\nPC 1 without scaling:\n', pca.components_[0])
print('\nPC 1 with scaling:\n', pca_std.components_[0])
# Scale and use PCA on X_train data for visualization.
scaler = std_clf.named_steps['standardscaler']
X_train_std = pca_std.transform(scaler.transform(X_train))
# visualize standardized vs. untouched dataset with PCA performed
fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=FIG_SIZE)
for l, c, m in zip(range(0, 3), ('blue', 'red', 'green'), ('^', 's', 'o')):
ax1.scatter(X_train[y_train == l, 0], X_train[y_train == l, 1],
color=c,
label='class %s' % l,
alpha=0.5,
marker=m
)
for l, c, m in zip(range(0, 3), ('blue', 'red', 'green'), ('^', 's', 'o')):
ax2.scatter(X_train_std[y_train == l, 0], X_train_std[y_train == l, 1],
color=c,
label='class %s' % l,
alpha=0.5,
marker=m
)
ax1.set_title('Training dataset after PCA')
ax2.set_title('Standardized training dataset after PCA')
for ax in (ax1, ax2):
ax.set_xlabel('1st principal component')
ax.set_ylabel('2nd principal component')
ax.legend(loc='upper right')
ax.grid()
plt.tight_layout()
plt.show()
| bsd-3-clause |
rs2/pandas | pandas/core/arrays/timedeltas.py | 1 | 35326 | from datetime import timedelta
from typing import List, Union
import numpy as np
from pandas._libs import lib, tslibs
from pandas._libs.tslibs import (
NaT,
NaTType,
Period,
Tick,
Timedelta,
Timestamp,
iNaT,
to_offset,
)
from pandas._libs.tslibs.conversion import precision_from_unit
from pandas._libs.tslibs.fields import get_timedelta_field
from pandas._libs.tslibs.timedeltas import array_to_timedelta64, parse_timedelta_unit
from pandas.compat.numpy import function as nv
from pandas.core.dtypes.common import (
DT64NS_DTYPE,
TD64NS_DTYPE,
is_dtype_equal,
is_float_dtype,
is_integer_dtype,
is_object_dtype,
is_scalar,
is_string_dtype,
is_timedelta64_dtype,
is_timedelta64_ns_dtype,
pandas_dtype,
)
from pandas.core.dtypes.dtypes import DatetimeTZDtype
from pandas.core.dtypes.generic import ABCSeries, ABCTimedeltaIndex
from pandas.core.dtypes.missing import isna
from pandas.core import nanops
from pandas.core.algorithms import checked_add_with_arr
from pandas.core.arrays import IntegerArray, datetimelike as dtl
from pandas.core.arrays._ranges import generate_regular_range
import pandas.core.common as com
from pandas.core.construction import extract_array
from pandas.core.ops.common import unpack_zerodim_and_defer
def _field_accessor(name, alias, docstring=None):
def f(self):
values = self.asi8
result = get_timedelta_field(values, alias)
if self._hasnans:
result = self._maybe_mask_results(
result, fill_value=None, convert="float64"
)
return result
f.__name__ = name
f.__doc__ = f"\n{docstring}\n"
return property(f)
class TimedeltaArray(dtl.DatetimeLikeArrayMixin, dtl.TimelikeOps):
"""
Pandas ExtensionArray for timedelta data.
.. versionadded:: 0.24.0
.. warning::
TimedeltaArray is currently experimental, and its API may change
without warning. In particular, :attr:`TimedeltaArray.dtype` is
expected to change to be an instance of an ``ExtensionDtype``
subclass.
Parameters
----------
values : array-like
The timedelta data.
dtype : numpy.dtype
Currently, only ``numpy.dtype("timedelta64[ns]")`` is accepted.
freq : Offset, optional
copy : bool, default False
Whether to copy the underlying array of data.
Attributes
----------
None
Methods
-------
None
"""
_typ = "timedeltaarray"
_scalar_type = Timedelta
_recognized_scalars = (timedelta, np.timedelta64, Tick)
_is_recognized_dtype = is_timedelta64_dtype
__array_priority__ = 1000
# define my properties & methods for delegation
_other_ops: List[str] = []
_bool_ops: List[str] = []
_object_ops = ["freq"]
_field_ops = ["days", "seconds", "microseconds", "nanoseconds"]
_datetimelike_ops = _field_ops + _object_ops + _bool_ops
_datetimelike_methods = [
"to_pytimedelta",
"total_seconds",
"round",
"floor",
"ceil",
]
# Note: ndim must be defined to ensure NaT.__richcmp(TimedeltaArray)
# operates pointwise.
def _box_func(self, x) -> Union[Timedelta, NaTType]:
return Timedelta(x, unit="ns")
@property
def dtype(self):
"""
The dtype for the TimedeltaArray.
.. warning::
A future version of pandas will change dtype to be an instance
of a :class:`pandas.api.extensions.ExtensionDtype` subclass,
not a ``numpy.dtype``.
Returns
-------
numpy.dtype
"""
return TD64NS_DTYPE
# ----------------------------------------------------------------
# Constructors
def __init__(self, values, dtype=TD64NS_DTYPE, freq=lib.no_default, copy=False):
values = extract_array(values)
inferred_freq = getattr(values, "_freq", None)
explicit_none = freq is None
freq = freq if freq is not lib.no_default else None
if isinstance(values, type(self)):
if explicit_none:
# dont inherit from values
pass
elif freq is None:
freq = values.freq
elif freq and values.freq:
freq = to_offset(freq)
freq, _ = dtl.validate_inferred_freq(freq, values.freq, False)
values = values._data
if not isinstance(values, np.ndarray):
msg = (
f"Unexpected type '{type(values).__name__}'. 'values' must be a "
"TimedeltaArray ndarray, or Series or Index containing one of those."
)
raise ValueError(msg)
if values.ndim not in [1, 2]:
raise ValueError("Only 1-dimensional input arrays are supported.")
if values.dtype == "i8":
# for compat with datetime/timedelta/period shared methods,
# we can sometimes get here with int64 values. These represent
# nanosecond UTC (or tz-naive) unix timestamps
values = values.view(TD64NS_DTYPE)
_validate_td64_dtype(values.dtype)
dtype = _validate_td64_dtype(dtype)
if freq == "infer":
msg = (
"Frequency inference not allowed in TimedeltaArray.__init__. "
"Use 'pd.array()' instead."
)
raise ValueError(msg)
if copy:
values = values.copy()
if freq:
freq = to_offset(freq)
self._data = values
self._dtype = dtype
self._freq = freq
if inferred_freq is None and freq is not None:
type(self)._validate_frequency(self, freq)
@classmethod
def _simple_new(cls, values, freq=None, dtype=TD64NS_DTYPE):
assert dtype == TD64NS_DTYPE, dtype
assert isinstance(values, np.ndarray), type(values)
if values.dtype != TD64NS_DTYPE:
assert values.dtype == "i8"
values = values.view(TD64NS_DTYPE)
result = object.__new__(cls)
result._data = values
result._freq = to_offset(freq)
result._dtype = TD64NS_DTYPE
return result
@classmethod
def _from_sequence(
cls, data, dtype=TD64NS_DTYPE, copy=False, freq=lib.no_default, unit=None
):
if dtype:
_validate_td64_dtype(dtype)
explicit_none = freq is None
freq = freq if freq is not lib.no_default else None
freq, freq_infer = dtl.maybe_infer_freq(freq)
data, inferred_freq = sequence_to_td64ns(data, copy=copy, unit=unit)
freq, freq_infer = dtl.validate_inferred_freq(freq, inferred_freq, freq_infer)
if explicit_none:
freq = None
result = cls._simple_new(data, freq=freq)
if inferred_freq is None and freq is not None:
# this condition precludes `freq_infer`
cls._validate_frequency(result, freq)
elif freq_infer:
# Set _freq directly to bypass duplicative _validate_frequency
# check.
result._freq = to_offset(result.inferred_freq)
return result
@classmethod
def _generate_range(cls, start, end, periods, freq, closed=None):
periods = dtl.validate_periods(periods)
if freq is None and any(x is None for x in [periods, start, end]):
raise ValueError("Must provide freq argument if no data is supplied")
if com.count_not_none(start, end, periods, freq) != 3:
raise ValueError(
"Of the four parameters: start, end, periods, "
"and freq, exactly three must be specified"
)
if start is not None:
start = Timedelta(start)
if end is not None:
end = Timedelta(end)
left_closed, right_closed = dtl.validate_endpoints(closed)
if freq is not None:
index = generate_regular_range(start, end, periods, freq)
else:
index = np.linspace(start.value, end.value, periods).astype("i8")
if not left_closed:
index = index[1:]
if not right_closed:
index = index[:-1]
return cls._simple_new(index, freq=freq)
# ----------------------------------------------------------------
# DatetimeLike Interface
@classmethod
def _rebox_native(cls, value: int) -> np.timedelta64:
return np.int64(value).view("m8[ns]")
def _unbox_scalar(self, value, setitem: bool = False):
if not isinstance(value, self._scalar_type) and value is not NaT:
raise ValueError("'value' should be a Timedelta.")
self._check_compatible_with(value, setitem=setitem)
return value.value
def _scalar_from_string(self, value):
return Timedelta(value)
def _check_compatible_with(self, other, setitem: bool = False):
# we don't have anything to validate.
pass
def _maybe_clear_freq(self):
self._freq = None
# ----------------------------------------------------------------
# Array-Like / EA-Interface Methods
def astype(self, dtype, copy=True):
# We handle
# --> timedelta64[ns]
# --> timedelta64
# DatetimeLikeArrayMixin super call handles other cases
dtype = pandas_dtype(dtype)
if is_timedelta64_dtype(dtype) and not is_timedelta64_ns_dtype(dtype):
# by pandas convention, converting to non-nano timedelta64
# returns an int64-dtyped array with ints representing multiples
# of the desired timedelta unit. This is essentially division
if self._hasnans:
# avoid double-copying
result = self._data.astype(dtype, copy=False)
values = self._maybe_mask_results(
result, fill_value=None, convert="float64"
)
return values
result = self._data.astype(dtype, copy=copy)
return result.astype("i8")
elif is_timedelta64_ns_dtype(dtype):
if copy:
return self.copy()
return self
return dtl.DatetimeLikeArrayMixin.astype(self, dtype, copy=copy)
# ----------------------------------------------------------------
# Reductions
def sum(
self,
axis=None,
dtype=None,
out=None,
keepdims: bool = False,
initial=None,
skipna: bool = True,
min_count: int = 0,
):
nv.validate_sum(
(), dict(dtype=dtype, out=out, keepdims=keepdims, initial=initial)
)
if not len(self):
return NaT
if not skipna and self._hasnans:
return NaT
result = nanops.nansum(
self._data, axis=axis, skipna=skipna, min_count=min_count
)
return Timedelta(result)
def std(
self,
axis=None,
dtype=None,
out=None,
ddof: int = 1,
keepdims: bool = False,
skipna: bool = True,
):
nv.validate_stat_ddof_func(
(), dict(dtype=dtype, out=out, keepdims=keepdims), fname="std"
)
if not len(self):
return NaT
if not skipna and self._hasnans:
return NaT
result = nanops.nanstd(self._data, axis=axis, skipna=skipna, ddof=ddof)
return Timedelta(result)
def median(
self,
axis=None,
out=None,
overwrite_input: bool = False,
keepdims: bool = False,
skipna: bool = True,
):
nv.validate_median(
(), dict(out=out, overwrite_input=overwrite_input, keepdims=keepdims)
)
return nanops.nanmedian(self._data, axis=axis, skipna=skipna)
# ----------------------------------------------------------------
# Rendering Methods
def _formatter(self, boxed=False):
from pandas.io.formats.format import get_format_timedelta64
return get_format_timedelta64(self, box=True)
def _format_native_types(self, na_rep="NaT", date_format=None, **kwargs):
from pandas.io.formats.format import get_format_timedelta64
formatter = get_format_timedelta64(self._data, na_rep)
return np.array([formatter(x) for x in self._data.ravel()]).reshape(self.shape)
# ----------------------------------------------------------------
# Arithmetic Methods
def _add_offset(self, other):
assert not isinstance(other, Tick)
raise TypeError(
f"cannot add the type {type(other).__name__} to a {type(self).__name__}"
)
def _add_period(self, other: Period):
"""
Add a Period object.
"""
# We will wrap in a PeriodArray and defer to the reversed operation
from .period import PeriodArray
i8vals = np.broadcast_to(other.ordinal, self.shape)
oth = PeriodArray(i8vals, freq=other.freq)
return oth + self
def _add_datetime_arraylike(self, other):
"""
Add DatetimeArray/Index or ndarray[datetime64] to TimedeltaArray.
"""
if isinstance(other, np.ndarray):
# At this point we have already checked that dtype is datetime64
from pandas.core.arrays import DatetimeArray
other = DatetimeArray(other)
# defer to implementation in DatetimeArray
return other + self
def _add_datetimelike_scalar(self, other):
# adding a timedeltaindex to a datetimelike
from pandas.core.arrays import DatetimeArray
assert other is not NaT
other = Timestamp(other)
if other is NaT:
# In this case we specifically interpret NaT as a datetime, not
# the timedelta interpretation we would get by returning self + NaT
result = self.asi8.view("m8[ms]") + NaT.to_datetime64()
return DatetimeArray(result)
i8 = self.asi8
result = checked_add_with_arr(i8, other.value, arr_mask=self._isnan)
result = self._maybe_mask_results(result)
dtype = DatetimeTZDtype(tz=other.tz) if other.tz else DT64NS_DTYPE
return DatetimeArray(result, dtype=dtype, freq=self.freq)
def _addsub_object_array(self, other, op):
# Add or subtract Array-like of objects
try:
# TimedeltaIndex can only operate with a subset of DateOffset
# subclasses. Incompatible classes will raise AttributeError,
# which we re-raise as TypeError
return super()._addsub_object_array(other, op)
except AttributeError as err:
raise TypeError(
f"Cannot add/subtract non-tick DateOffset to {type(self).__name__}"
) from err
@unpack_zerodim_and_defer("__mul__")
def __mul__(self, other):
if is_scalar(other):
# numpy will accept float and int, raise TypeError for others
result = self._data * other
freq = None
if self.freq is not None and not isna(other):
freq = self.freq * other
return type(self)(result, freq=freq)
if not hasattr(other, "dtype"):
# list, tuple
other = np.array(other)
if len(other) != len(self) and not is_timedelta64_dtype(other.dtype):
# Exclude timedelta64 here so we correctly raise TypeError
# for that instead of ValueError
raise ValueError("Cannot multiply with unequal lengths")
if is_object_dtype(other.dtype):
# this multiplication will succeed only if all elements of other
# are int or float scalars, so we will end up with
# timedelta64[ns]-dtyped result
result = [self[n] * other[n] for n in range(len(self))]
result = np.array(result)
return type(self)(result)
# numpy will accept float or int dtype, raise TypeError for others
result = self._data * other
return type(self)(result)
__rmul__ = __mul__
@unpack_zerodim_and_defer("__truediv__")
def __truediv__(self, other):
# timedelta / X is well-defined for timedelta-like or numeric X
if isinstance(other, (timedelta, np.timedelta64, Tick)):
other = Timedelta(other)
if other is NaT:
# specifically timedelta64-NaT
result = np.empty(self.shape, dtype=np.float64)
result.fill(np.nan)
return result
# otherwise, dispatch to Timedelta implementation
return self._data / other
elif lib.is_scalar(other):
# assume it is numeric
result = self._data / other
freq = None
if self.freq is not None:
# Tick division is not implemented, so operate on Timedelta
freq = self.freq.delta / other
return type(self)(result, freq=freq)
if not hasattr(other, "dtype"):
# e.g. list, tuple
other = np.array(other)
if len(other) != len(self):
raise ValueError("Cannot divide vectors with unequal lengths")
elif is_timedelta64_dtype(other.dtype):
# let numpy handle it
return self._data / other
elif is_object_dtype(other.dtype):
# We operate on raveled arrays to avoid problems in inference
# on NaT
srav = self.ravel()
orav = other.ravel()
result = [srav[n] / orav[n] for n in range(len(srav))]
result = np.array(result).reshape(self.shape)
# We need to do dtype inference in order to keep DataFrame ops
# behavior consistent with Series behavior
inferred = lib.infer_dtype(result)
if inferred == "timedelta":
flat = result.ravel()
result = type(self)._from_sequence(flat).reshape(result.shape)
elif inferred == "floating":
result = result.astype(float)
return result
else:
result = self._data / other
return type(self)(result)
@unpack_zerodim_and_defer("__rtruediv__")
def __rtruediv__(self, other):
# X / timedelta is defined only for timedelta-like X
if isinstance(other, (timedelta, np.timedelta64, Tick)):
other = Timedelta(other)
if other is NaT:
# specifically timedelta64-NaT
result = np.empty(self.shape, dtype=np.float64)
result.fill(np.nan)
return result
# otherwise, dispatch to Timedelta implementation
return other / self._data
elif lib.is_scalar(other):
raise TypeError(
f"Cannot divide {type(other).__name__} by {type(self).__name__}"
)
if not hasattr(other, "dtype"):
# e.g. list, tuple
other = np.array(other)
if len(other) != len(self):
raise ValueError("Cannot divide vectors with unequal lengths")
elif is_timedelta64_dtype(other.dtype):
# let numpy handle it
return other / self._data
elif is_object_dtype(other.dtype):
# Note: unlike in __truediv__, we do not _need_ to do type
# inference on the result. It does not raise, a numeric array
# is returned. GH#23829
result = [other[n] / self[n] for n in range(len(self))]
return np.array(result)
else:
raise TypeError(
f"Cannot divide {other.dtype} data by {type(self).__name__}"
)
@unpack_zerodim_and_defer("__floordiv__")
def __floordiv__(self, other):
if is_scalar(other):
if isinstance(other, (timedelta, np.timedelta64, Tick)):
other = Timedelta(other)
if other is NaT:
# treat this specifically as timedelta-NaT
result = np.empty(self.shape, dtype=np.float64)
result.fill(np.nan)
return result
# dispatch to Timedelta implementation
result = other.__rfloordiv__(self._data)
return result
# at this point we should only have numeric scalars; anything
# else will raise
result = self.asi8 // other
result[self._isnan] = iNaT
freq = None
if self.freq is not None:
# Note: freq gets division, not floor-division
freq = self.freq / other
if freq.nanos == 0 and self.freq.nanos != 0:
# e.g. if self.freq is Nano(1) then dividing by 2
# rounds down to zero
freq = None
return type(self)(result.view("m8[ns]"), freq=freq)
if not hasattr(other, "dtype"):
# list, tuple
other = np.array(other)
if len(other) != len(self):
raise ValueError("Cannot divide with unequal lengths")
elif is_timedelta64_dtype(other.dtype):
other = type(self)(other)
# numpy timedelta64 does not natively support floordiv, so operate
# on the i8 values
result = self.asi8 // other.asi8
mask = self._isnan | other._isnan
if mask.any():
result = result.astype(np.float64)
result[mask] = np.nan
return result
elif is_object_dtype(other.dtype):
result = [self[n] // other[n] for n in range(len(self))]
result = np.array(result)
if lib.infer_dtype(result, skipna=False) == "timedelta":
result, _ = sequence_to_td64ns(result)
return type(self)(result)
return result
elif is_integer_dtype(other.dtype) or is_float_dtype(other.dtype):
result = self._data // other
return type(self)(result)
else:
dtype = getattr(other, "dtype", type(other).__name__)
raise TypeError(f"Cannot divide {dtype} by {type(self).__name__}")
@unpack_zerodim_and_defer("__rfloordiv__")
def __rfloordiv__(self, other):
if is_scalar(other):
if isinstance(other, (timedelta, np.timedelta64, Tick)):
other = Timedelta(other)
if other is NaT:
# treat this specifically as timedelta-NaT
result = np.empty(self.shape, dtype=np.float64)
result.fill(np.nan)
return result
# dispatch to Timedelta implementation
result = other.__floordiv__(self._data)
return result
raise TypeError(
f"Cannot divide {type(other).__name__} by {type(self).__name__}"
)
if not hasattr(other, "dtype"):
# list, tuple
other = np.array(other)
if len(other) != len(self):
raise ValueError("Cannot divide with unequal lengths")
elif is_timedelta64_dtype(other.dtype):
other = type(self)(other)
# numpy timedelta64 does not natively support floordiv, so operate
# on the i8 values
result = other.asi8 // self.asi8
mask = self._isnan | other._isnan
if mask.any():
result = result.astype(np.float64)
result[mask] = np.nan
return result
elif is_object_dtype(other.dtype):
result = [other[n] // self[n] for n in range(len(self))]
result = np.array(result)
return result
else:
dtype = getattr(other, "dtype", type(other).__name__)
raise TypeError(f"Cannot divide {dtype} by {type(self).__name__}")
@unpack_zerodim_and_defer("__mod__")
def __mod__(self, other):
# Note: This is a naive implementation, can likely be optimized
if isinstance(other, (timedelta, np.timedelta64, Tick)):
other = Timedelta(other)
return self - (self // other) * other
@unpack_zerodim_and_defer("__rmod__")
def __rmod__(self, other):
# Note: This is a naive implementation, can likely be optimized
if isinstance(other, (timedelta, np.timedelta64, Tick)):
other = Timedelta(other)
return other - (other // self) * self
@unpack_zerodim_and_defer("__divmod__")
def __divmod__(self, other):
# Note: This is a naive implementation, can likely be optimized
if isinstance(other, (timedelta, np.timedelta64, Tick)):
other = Timedelta(other)
res1 = self // other
res2 = self - res1 * other
return res1, res2
@unpack_zerodim_and_defer("__rdivmod__")
def __rdivmod__(self, other):
# Note: This is a naive implementation, can likely be optimized
if isinstance(other, (timedelta, np.timedelta64, Tick)):
other = Timedelta(other)
res1 = other // self
res2 = other - res1 * self
return res1, res2
def __neg__(self):
if self.freq is not None:
return type(self)(-self._data, freq=-self.freq)
return type(self)(-self._data)
def __pos__(self):
return type(self)(self._data, freq=self.freq)
def __abs__(self):
# Note: freq is not preserved
return type(self)(np.abs(self._data))
# ----------------------------------------------------------------
# Conversion Methods - Vectorized analogues of Timedelta methods
def total_seconds(self):
"""
Return total duration of each element expressed in seconds.
This method is available directly on TimedeltaArray, TimedeltaIndex
and on Series containing timedelta values under the ``.dt`` namespace.
Returns
-------
seconds : [ndarray, Float64Index, Series]
When the calling object is a TimedeltaArray, the return type
is ndarray. When the calling object is a TimedeltaIndex,
the return type is a Float64Index. When the calling object
is a Series, the return type is Series of type `float64` whose
index is the same as the original.
See Also
--------
datetime.timedelta.total_seconds : Standard library version
of this method.
TimedeltaIndex.components : Return a DataFrame with components of
each Timedelta.
Examples
--------
**Series**
>>> s = pd.Series(pd.to_timedelta(np.arange(5), unit='d'))
>>> s
0 0 days
1 1 days
2 2 days
3 3 days
4 4 days
dtype: timedelta64[ns]
>>> s.dt.total_seconds()
0 0.0
1 86400.0
2 172800.0
3 259200.0
4 345600.0
dtype: float64
**TimedeltaIndex**
>>> idx = pd.to_timedelta(np.arange(5), unit='d')
>>> idx
TimedeltaIndex(['0 days', '1 days', '2 days', '3 days', '4 days'],
dtype='timedelta64[ns]', freq=None)
>>> idx.total_seconds()
Float64Index([0.0, 86400.0, 172800.0, 259200.00000000003, 345600.0],
dtype='float64')
"""
return self._maybe_mask_results(1e-9 * self.asi8, fill_value=None)
def to_pytimedelta(self) -> np.ndarray:
"""
Return Timedelta Array/Index as object ndarray of datetime.timedelta
objects.
Returns
-------
datetimes : ndarray
"""
return tslibs.ints_to_pytimedelta(self.asi8)
days = _field_accessor("days", "days", "Number of days for each element.")
seconds = _field_accessor(
"seconds",
"seconds",
"Number of seconds (>= 0 and less than 1 day) for each element.",
)
microseconds = _field_accessor(
"microseconds",
"microseconds",
"Number of microseconds (>= 0 and less than 1 second) for each element.",
)
nanoseconds = _field_accessor(
"nanoseconds",
"nanoseconds",
"Number of nanoseconds (>= 0 and less than 1 microsecond) for each element.",
)
@property
def components(self):
"""
Return a dataframe of the components (days, hours, minutes,
seconds, milliseconds, microseconds, nanoseconds) of the Timedeltas.
Returns
-------
a DataFrame
"""
from pandas import DataFrame
columns = [
"days",
"hours",
"minutes",
"seconds",
"milliseconds",
"microseconds",
"nanoseconds",
]
hasnans = self._hasnans
if hasnans:
def f(x):
if isna(x):
return [np.nan] * len(columns)
return x.components
else:
def f(x):
return x.components
result = DataFrame([f(x) for x in self], columns=columns)
if not hasnans:
result = result.astype("int64")
return result
# ---------------------------------------------------------------------
# Constructor Helpers
def sequence_to_td64ns(data, copy=False, unit=None, errors="raise"):
"""
Parameters
----------
data : list-like
copy : bool, default False
unit : str, optional
The timedelta unit to treat integers as multiples of. For numeric
data this defaults to ``'ns'``.
Must be un-specified if the data contains a str and ``errors=="raise"``.
errors : {"raise", "coerce", "ignore"}, default "raise"
How to handle elements that cannot be converted to timedelta64[ns].
See ``pandas.to_timedelta`` for details.
Returns
-------
converted : numpy.ndarray
The sequence converted to a numpy array with dtype ``timedelta64[ns]``.
inferred_freq : Tick or None
The inferred frequency of the sequence.
Raises
------
ValueError : Data cannot be converted to timedelta64[ns].
Notes
-----
Unlike `pandas.to_timedelta`, if setting ``errors=ignore`` will not cause
errors to be ignored; they are caught and subsequently ignored at a
higher level.
"""
inferred_freq = None
if unit is not None:
unit = parse_timedelta_unit(unit)
# Unwrap whatever we have into a np.ndarray
if not hasattr(data, "dtype"):
# e.g. list, tuple
if np.ndim(data) == 0:
# i.e. generator
data = list(data)
data = np.array(data, copy=False)
elif isinstance(data, ABCSeries):
data = data._values
elif isinstance(data, (ABCTimedeltaIndex, TimedeltaArray)):
inferred_freq = data.freq
data = data._data
elif isinstance(data, IntegerArray):
data = data.to_numpy("int64", na_value=tslibs.iNaT)
# Convert whatever we have into timedelta64[ns] dtype
if is_object_dtype(data.dtype) or is_string_dtype(data.dtype):
# no need to make a copy, need to convert if string-dtyped
data = objects_to_td64ns(data, unit=unit, errors=errors)
copy = False
elif is_integer_dtype(data.dtype):
# treat as multiples of the given unit
data, copy_made = ints_to_td64ns(data, unit=unit)
copy = copy and not copy_made
elif is_float_dtype(data.dtype):
# cast the unit, multiply base/frac separately
# to avoid precision issues from float -> int
mask = np.isnan(data)
m, p = precision_from_unit(unit or "ns")
base = data.astype(np.int64)
frac = data - base
if p:
frac = np.round(frac, p)
data = (base * m + (frac * m).astype(np.int64)).view("timedelta64[ns]")
data[mask] = iNaT
copy = False
elif is_timedelta64_dtype(data.dtype):
if data.dtype != TD64NS_DTYPE:
# non-nano unit
# TODO: watch out for overflows
data = data.astype(TD64NS_DTYPE)
copy = False
else:
# This includes datetime64-dtype, see GH#23539, GH#29794
raise TypeError(f"dtype {data.dtype} cannot be converted to timedelta64[ns]")
data = np.array(data, copy=copy)
assert data.dtype == "m8[ns]", data
return data, inferred_freq
def ints_to_td64ns(data, unit="ns"):
"""
Convert an ndarray with integer-dtype to timedelta64[ns] dtype, treating
the integers as multiples of the given timedelta unit.
Parameters
----------
data : numpy.ndarray with integer-dtype
unit : str, default "ns"
The timedelta unit to treat integers as multiples of.
Returns
-------
numpy.ndarray : timedelta64[ns] array converted from data
bool : whether a copy was made
"""
copy_made = False
unit = unit if unit is not None else "ns"
if data.dtype != np.int64:
# converting to int64 makes a copy, so we can avoid
# re-copying later
data = data.astype(np.int64)
copy_made = True
if unit != "ns":
dtype_str = f"timedelta64[{unit}]"
data = data.view(dtype_str)
# TODO: watch out for overflows when converting from lower-resolution
data = data.astype("timedelta64[ns]")
# the astype conversion makes a copy, so we can avoid re-copying later
copy_made = True
else:
data = data.view("timedelta64[ns]")
return data, copy_made
def objects_to_td64ns(data, unit=None, errors="raise"):
"""
Convert a object-dtyped or string-dtyped array into an
timedelta64[ns]-dtyped array.
Parameters
----------
data : ndarray or Index
unit : str, default "ns"
The timedelta unit to treat integers as multiples of.
Must not be specified if the data contains a str.
errors : {"raise", "coerce", "ignore"}, default "raise"
How to handle elements that cannot be converted to timedelta64[ns].
See ``pandas.to_timedelta`` for details.
Returns
-------
numpy.ndarray : timedelta64[ns] array converted from data
Raises
------
ValueError : Data cannot be converted to timedelta64[ns].
Notes
-----
Unlike `pandas.to_timedelta`, if setting `errors=ignore` will not cause
errors to be ignored; they are caught and subsequently ignored at a
higher level.
"""
# coerce Index to np.ndarray, converting string-dtype if necessary
values = np.array(data, dtype=np.object_, copy=False)
result = array_to_timedelta64(values, unit=unit, errors=errors)
return result.view("timedelta64[ns]")
def _validate_td64_dtype(dtype):
dtype = pandas_dtype(dtype)
if is_dtype_equal(dtype, np.dtype("timedelta64")):
# no precision disallowed GH#24806
msg = (
"Passing in 'timedelta' dtype with no precision is not allowed. "
"Please pass in 'timedelta64[ns]' instead."
)
raise ValueError(msg)
if not is_dtype_equal(dtype, TD64NS_DTYPE):
raise ValueError(f"dtype {dtype} cannot be converted to timedelta64[ns]")
return dtype
| bsd-3-clause |
numenta-ci/nupic | external/linux32/lib/python2.6/site-packages/matplotlib/_cm.py | 70 | 375423 | """
Color data and pre-defined cmap objects.
This is a helper for cm.py, originally part of that file.
Separating the data (this file) from cm.py makes both easier
to deal with.
Objects visible in cm.py are the individual cmap objects ('autumn',
etc.) and a dictionary, 'datad', including all of these objects.
"""
import matplotlib as mpl
import matplotlib.colors as colors
LUTSIZE = mpl.rcParams['image.lut']
_binary_data = {
'red' : ((0., 1., 1.), (1., 0., 0.)),
'green': ((0., 1., 1.), (1., 0., 0.)),
'blue' : ((0., 1., 1.), (1., 0., 0.))
}
_bone_data = {'red': ((0., 0., 0.),(1.0, 1.0, 1.0)),
'green': ((0., 0., 0.),(1.0, 1.0, 1.0)),
'blue': ((0., 0., 0.),(1.0, 1.0, 1.0))}
_autumn_data = {'red': ((0., 1.0, 1.0),(1.0, 1.0, 1.0)),
'green': ((0., 0., 0.),(1.0, 1.0, 1.0)),
'blue': ((0., 0., 0.),(1.0, 0., 0.))}
_bone_data = {'red': ((0., 0., 0.),(0.746032, 0.652778, 0.652778),(1.0, 1.0, 1.0)),
'green': ((0., 0., 0.),(0.365079, 0.319444, 0.319444),
(0.746032, 0.777778, 0.777778),(1.0, 1.0, 1.0)),
'blue': ((0., 0., 0.),(0.365079, 0.444444, 0.444444),(1.0, 1.0, 1.0))}
_cool_data = {'red': ((0., 0., 0.), (1.0, 1.0, 1.0)),
'green': ((0., 1., 1.), (1.0, 0., 0.)),
'blue': ((0., 1., 1.), (1.0, 1., 1.))}
_copper_data = {'red': ((0., 0., 0.),(0.809524, 1.000000, 1.000000),(1.0, 1.0, 1.0)),
'green': ((0., 0., 0.),(1.0, 0.7812, 0.7812)),
'blue': ((0., 0., 0.),(1.0, 0.4975, 0.4975))}
_flag_data = {'red': ((0., 1., 1.),(0.015873, 1.000000, 1.000000),
(0.031746, 0.000000, 0.000000),(0.047619, 0.000000, 0.000000),
(0.063492, 1.000000, 1.000000),(0.079365, 1.000000, 1.000000),
(0.095238, 0.000000, 0.000000),(0.111111, 0.000000, 0.000000),
(0.126984, 1.000000, 1.000000),(0.142857, 1.000000, 1.000000),
(0.158730, 0.000000, 0.000000),(0.174603, 0.000000, 0.000000),
(0.190476, 1.000000, 1.000000),(0.206349, 1.000000, 1.000000),
(0.222222, 0.000000, 0.000000),(0.238095, 0.000000, 0.000000),
(0.253968, 1.000000, 1.000000),(0.269841, 1.000000, 1.000000),
(0.285714, 0.000000, 0.000000),(0.301587, 0.000000, 0.000000),
(0.317460, 1.000000, 1.000000),(0.333333, 1.000000, 1.000000),
(0.349206, 0.000000, 0.000000),(0.365079, 0.000000, 0.000000),
(0.380952, 1.000000, 1.000000),(0.396825, 1.000000, 1.000000),
(0.412698, 0.000000, 0.000000),(0.428571, 0.000000, 0.000000),
(0.444444, 1.000000, 1.000000),(0.460317, 1.000000, 1.000000),
(0.476190, 0.000000, 0.000000),(0.492063, 0.000000, 0.000000),
(0.507937, 1.000000, 1.000000),(0.523810, 1.000000, 1.000000),
(0.539683, 0.000000, 0.000000),(0.555556, 0.000000, 0.000000),
(0.571429, 1.000000, 1.000000),(0.587302, 1.000000, 1.000000),
(0.603175, 0.000000, 0.000000),(0.619048, 0.000000, 0.000000),
(0.634921, 1.000000, 1.000000),(0.650794, 1.000000, 1.000000),
(0.666667, 0.000000, 0.000000),(0.682540, 0.000000, 0.000000),
(0.698413, 1.000000, 1.000000),(0.714286, 1.000000, 1.000000),
(0.730159, 0.000000, 0.000000),(0.746032, 0.000000, 0.000000),
(0.761905, 1.000000, 1.000000),(0.777778, 1.000000, 1.000000),
(0.793651, 0.000000, 0.000000),(0.809524, 0.000000, 0.000000),
(0.825397, 1.000000, 1.000000),(0.841270, 1.000000, 1.000000),
(0.857143, 0.000000, 0.000000),(0.873016, 0.000000, 0.000000),
(0.888889, 1.000000, 1.000000),(0.904762, 1.000000, 1.000000),
(0.920635, 0.000000, 0.000000),(0.936508, 0.000000, 0.000000),
(0.952381, 1.000000, 1.000000),(0.968254, 1.000000, 1.000000),
(0.984127, 0.000000, 0.000000),(1.0, 0., 0.)),
'green': ((0., 0., 0.),(0.015873, 1.000000, 1.000000),
(0.031746, 0.000000, 0.000000),(0.063492, 0.000000, 0.000000),
(0.079365, 1.000000, 1.000000),(0.095238, 0.000000, 0.000000),
(0.126984, 0.000000, 0.000000),(0.142857, 1.000000, 1.000000),
(0.158730, 0.000000, 0.000000),(0.190476, 0.000000, 0.000000),
(0.206349, 1.000000, 1.000000),(0.222222, 0.000000, 0.000000),
(0.253968, 0.000000, 0.000000),(0.269841, 1.000000, 1.000000),
(0.285714, 0.000000, 0.000000),(0.317460, 0.000000, 0.000000),
(0.333333, 1.000000, 1.000000),(0.349206, 0.000000, 0.000000),
(0.380952, 0.000000, 0.000000),(0.396825, 1.000000, 1.000000),
(0.412698, 0.000000, 0.000000),(0.444444, 0.000000, 0.000000),
(0.460317, 1.000000, 1.000000),(0.476190, 0.000000, 0.000000),
(0.507937, 0.000000, 0.000000),(0.523810, 1.000000, 1.000000),
(0.539683, 0.000000, 0.000000),(0.571429, 0.000000, 0.000000),
(0.587302, 1.000000, 1.000000),(0.603175, 0.000000, 0.000000),
(0.634921, 0.000000, 0.000000),(0.650794, 1.000000, 1.000000),
(0.666667, 0.000000, 0.000000),(0.698413, 0.000000, 0.000000),
(0.714286, 1.000000, 1.000000),(0.730159, 0.000000, 0.000000),
(0.761905, 0.000000, 0.000000),(0.777778, 1.000000, 1.000000),
(0.793651, 0.000000, 0.000000),(0.825397, 0.000000, 0.000000),
(0.841270, 1.000000, 1.000000),(0.857143, 0.000000, 0.000000),
(0.888889, 0.000000, 0.000000),(0.904762, 1.000000, 1.000000),
(0.920635, 0.000000, 0.000000),(0.952381, 0.000000, 0.000000),
(0.968254, 1.000000, 1.000000),(0.984127, 0.000000, 0.000000),
(1.0, 0., 0.)),
'blue': ((0., 0., 0.),(0.015873, 1.000000, 1.000000),
(0.031746, 1.000000, 1.000000),(0.047619, 0.000000, 0.000000),
(0.063492, 0.000000, 0.000000),(0.079365, 1.000000, 1.000000),
(0.095238, 1.000000, 1.000000),(0.111111, 0.000000, 0.000000),
(0.126984, 0.000000, 0.000000),(0.142857, 1.000000, 1.000000),
(0.158730, 1.000000, 1.000000),(0.174603, 0.000000, 0.000000),
(0.190476, 0.000000, 0.000000),(0.206349, 1.000000, 1.000000),
(0.222222, 1.000000, 1.000000),(0.238095, 0.000000, 0.000000),
(0.253968, 0.000000, 0.000000),(0.269841, 1.000000, 1.000000),
(0.285714, 1.000000, 1.000000),(0.301587, 0.000000, 0.000000),
(0.317460, 0.000000, 0.000000),(0.333333, 1.000000, 1.000000),
(0.349206, 1.000000, 1.000000),(0.365079, 0.000000, 0.000000),
(0.380952, 0.000000, 0.000000),(0.396825, 1.000000, 1.000000),
(0.412698, 1.000000, 1.000000),(0.428571, 0.000000, 0.000000),
(0.444444, 0.000000, 0.000000),(0.460317, 1.000000, 1.000000),
(0.476190, 1.000000, 1.000000),(0.492063, 0.000000, 0.000000),
(0.507937, 0.000000, 0.000000),(0.523810, 1.000000, 1.000000),
(0.539683, 1.000000, 1.000000),(0.555556, 0.000000, 0.000000),
(0.571429, 0.000000, 0.000000),(0.587302, 1.000000, 1.000000),
(0.603175, 1.000000, 1.000000),(0.619048, 0.000000, 0.000000),
(0.634921, 0.000000, 0.000000),(0.650794, 1.000000, 1.000000),
(0.666667, 1.000000, 1.000000),(0.682540, 0.000000, 0.000000),
(0.698413, 0.000000, 0.000000),(0.714286, 1.000000, 1.000000),
(0.730159, 1.000000, 1.000000),(0.746032, 0.000000, 0.000000),
(0.761905, 0.000000, 0.000000),(0.777778, 1.000000, 1.000000),
(0.793651, 1.000000, 1.000000),(0.809524, 0.000000, 0.000000),
(0.825397, 0.000000, 0.000000),(0.841270, 1.000000, 1.000000),
(0.857143, 1.000000, 1.000000),(0.873016, 0.000000, 0.000000),
(0.888889, 0.000000, 0.000000),(0.904762, 1.000000, 1.000000),
(0.920635, 1.000000, 1.000000),(0.936508, 0.000000, 0.000000),
(0.952381, 0.000000, 0.000000),(0.968254, 1.000000, 1.000000),
(0.984127, 1.000000, 1.000000),(1.0, 0., 0.))}
_gray_data = {'red': ((0., 0, 0), (1., 1, 1)),
'green': ((0., 0, 0), (1., 1, 1)),
'blue': ((0., 0, 0), (1., 1, 1))}
_hot_data = {'red': ((0., 0.0416, 0.0416),(0.365079, 1.000000, 1.000000),(1.0, 1.0, 1.0)),
'green': ((0., 0., 0.),(0.365079, 0.000000, 0.000000),
(0.746032, 1.000000, 1.000000),(1.0, 1.0, 1.0)),
'blue': ((0., 0., 0.),(0.746032, 0.000000, 0.000000),(1.0, 1.0, 1.0))}
_hsv_data = {'red': ((0., 1., 1.),(0.158730, 1.000000, 1.000000),
(0.174603, 0.968750, 0.968750),(0.333333, 0.031250, 0.031250),
(0.349206, 0.000000, 0.000000),(0.666667, 0.000000, 0.000000),
(0.682540, 0.031250, 0.031250),(0.841270, 0.968750, 0.968750),
(0.857143, 1.000000, 1.000000),(1.0, 1.0, 1.0)),
'green': ((0., 0., 0.),(0.158730, 0.937500, 0.937500),
(0.174603, 1.000000, 1.000000),(0.507937, 1.000000, 1.000000),
(0.666667, 0.062500, 0.062500),(0.682540, 0.000000, 0.000000),
(1.0, 0., 0.)),
'blue': ((0., 0., 0.),(0.333333, 0.000000, 0.000000),
(0.349206, 0.062500, 0.062500),(0.507937, 1.000000, 1.000000),
(0.841270, 1.000000, 1.000000),(0.857143, 0.937500, 0.937500),
(1.0, 0.09375, 0.09375))}
_jet_data = {'red': ((0., 0, 0), (0.35, 0, 0), (0.66, 1, 1), (0.89,1, 1),
(1, 0.5, 0.5)),
'green': ((0., 0, 0), (0.125,0, 0), (0.375,1, 1), (0.64,1, 1),
(0.91,0,0), (1, 0, 0)),
'blue': ((0., 0.5, 0.5), (0.11, 1, 1), (0.34, 1, 1), (0.65,0, 0),
(1, 0, 0))}
_pink_data = {'red': ((0., 0.1178, 0.1178),(0.015873, 0.195857, 0.195857),
(0.031746, 0.250661, 0.250661),(0.047619, 0.295468, 0.295468),
(0.063492, 0.334324, 0.334324),(0.079365, 0.369112, 0.369112),
(0.095238, 0.400892, 0.400892),(0.111111, 0.430331, 0.430331),
(0.126984, 0.457882, 0.457882),(0.142857, 0.483867, 0.483867),
(0.158730, 0.508525, 0.508525),(0.174603, 0.532042, 0.532042),
(0.190476, 0.554563, 0.554563),(0.206349, 0.576204, 0.576204),
(0.222222, 0.597061, 0.597061),(0.238095, 0.617213, 0.617213),
(0.253968, 0.636729, 0.636729),(0.269841, 0.655663, 0.655663),
(0.285714, 0.674066, 0.674066),(0.301587, 0.691980, 0.691980),
(0.317460, 0.709441, 0.709441),(0.333333, 0.726483, 0.726483),
(0.349206, 0.743134, 0.743134),(0.365079, 0.759421, 0.759421),
(0.380952, 0.766356, 0.766356),(0.396825, 0.773229, 0.773229),
(0.412698, 0.780042, 0.780042),(0.428571, 0.786796, 0.786796),
(0.444444, 0.793492, 0.793492),(0.460317, 0.800132, 0.800132),
(0.476190, 0.806718, 0.806718),(0.492063, 0.813250, 0.813250),
(0.507937, 0.819730, 0.819730),(0.523810, 0.826160, 0.826160),
(0.539683, 0.832539, 0.832539),(0.555556, 0.838870, 0.838870),
(0.571429, 0.845154, 0.845154),(0.587302, 0.851392, 0.851392),
(0.603175, 0.857584, 0.857584),(0.619048, 0.863731, 0.863731),
(0.634921, 0.869835, 0.869835),(0.650794, 0.875897, 0.875897),
(0.666667, 0.881917, 0.881917),(0.682540, 0.887896, 0.887896),
(0.698413, 0.893835, 0.893835),(0.714286, 0.899735, 0.899735),
(0.730159, 0.905597, 0.905597),(0.746032, 0.911421, 0.911421),
(0.761905, 0.917208, 0.917208),(0.777778, 0.922958, 0.922958),
(0.793651, 0.928673, 0.928673),(0.809524, 0.934353, 0.934353),
(0.825397, 0.939999, 0.939999),(0.841270, 0.945611, 0.945611),
(0.857143, 0.951190, 0.951190),(0.873016, 0.956736, 0.956736),
(0.888889, 0.962250, 0.962250),(0.904762, 0.967733, 0.967733),
(0.920635, 0.973185, 0.973185),(0.936508, 0.978607, 0.978607),
(0.952381, 0.983999, 0.983999),(0.968254, 0.989361, 0.989361),
(0.984127, 0.994695, 0.994695),(1.0, 1.0, 1.0)),
'green': ((0., 0., 0.),(0.015873, 0.102869, 0.102869),
(0.031746, 0.145479, 0.145479),(0.047619, 0.178174, 0.178174),
(0.063492, 0.205738, 0.205738),(0.079365, 0.230022, 0.230022),
(0.095238, 0.251976, 0.251976),(0.111111, 0.272166, 0.272166),
(0.126984, 0.290957, 0.290957),(0.142857, 0.308607, 0.308607),
(0.158730, 0.325300, 0.325300),(0.174603, 0.341178, 0.341178),
(0.190476, 0.356348, 0.356348),(0.206349, 0.370899, 0.370899),
(0.222222, 0.384900, 0.384900),(0.238095, 0.398410, 0.398410),
(0.253968, 0.411476, 0.411476),(0.269841, 0.424139, 0.424139),
(0.285714, 0.436436, 0.436436),(0.301587, 0.448395, 0.448395),
(0.317460, 0.460044, 0.460044),(0.333333, 0.471405, 0.471405),
(0.349206, 0.482498, 0.482498),(0.365079, 0.493342, 0.493342),
(0.380952, 0.517549, 0.517549),(0.396825, 0.540674, 0.540674),
(0.412698, 0.562849, 0.562849),(0.428571, 0.584183, 0.584183),
(0.444444, 0.604765, 0.604765),(0.460317, 0.624669, 0.624669),
(0.476190, 0.643958, 0.643958),(0.492063, 0.662687, 0.662687),
(0.507937, 0.680900, 0.680900),(0.523810, 0.698638, 0.698638),
(0.539683, 0.715937, 0.715937),(0.555556, 0.732828, 0.732828),
(0.571429, 0.749338, 0.749338),(0.587302, 0.765493, 0.765493),
(0.603175, 0.781313, 0.781313),(0.619048, 0.796819, 0.796819),
(0.634921, 0.812029, 0.812029),(0.650794, 0.826960, 0.826960),
(0.666667, 0.841625, 0.841625),(0.682540, 0.856040, 0.856040),
(0.698413, 0.870216, 0.870216),(0.714286, 0.884164, 0.884164),
(0.730159, 0.897896, 0.897896),(0.746032, 0.911421, 0.911421),
(0.761905, 0.917208, 0.917208),(0.777778, 0.922958, 0.922958),
(0.793651, 0.928673, 0.928673),(0.809524, 0.934353, 0.934353),
(0.825397, 0.939999, 0.939999),(0.841270, 0.945611, 0.945611),
(0.857143, 0.951190, 0.951190),(0.873016, 0.956736, 0.956736),
(0.888889, 0.962250, 0.962250),(0.904762, 0.967733, 0.967733),
(0.920635, 0.973185, 0.973185),(0.936508, 0.978607, 0.978607),
(0.952381, 0.983999, 0.983999),(0.968254, 0.989361, 0.989361),
(0.984127, 0.994695, 0.994695),(1.0, 1.0, 1.0)),
'blue': ((0., 0., 0.),(0.015873, 0.102869, 0.102869),
(0.031746, 0.145479, 0.145479),(0.047619, 0.178174, 0.178174),
(0.063492, 0.205738, 0.205738),(0.079365, 0.230022, 0.230022),
(0.095238, 0.251976, 0.251976),(0.111111, 0.272166, 0.272166),
(0.126984, 0.290957, 0.290957),(0.142857, 0.308607, 0.308607),
(0.158730, 0.325300, 0.325300),(0.174603, 0.341178, 0.341178),
(0.190476, 0.356348, 0.356348),(0.206349, 0.370899, 0.370899),
(0.222222, 0.384900, 0.384900),(0.238095, 0.398410, 0.398410),
(0.253968, 0.411476, 0.411476),(0.269841, 0.424139, 0.424139),
(0.285714, 0.436436, 0.436436),(0.301587, 0.448395, 0.448395),
(0.317460, 0.460044, 0.460044),(0.333333, 0.471405, 0.471405),
(0.349206, 0.482498, 0.482498),(0.365079, 0.493342, 0.493342),
(0.380952, 0.503953, 0.503953),(0.396825, 0.514344, 0.514344),
(0.412698, 0.524531, 0.524531),(0.428571, 0.534522, 0.534522),
(0.444444, 0.544331, 0.544331),(0.460317, 0.553966, 0.553966),
(0.476190, 0.563436, 0.563436),(0.492063, 0.572750, 0.572750),
(0.507937, 0.581914, 0.581914),(0.523810, 0.590937, 0.590937),
(0.539683, 0.599824, 0.599824),(0.555556, 0.608581, 0.608581),
(0.571429, 0.617213, 0.617213),(0.587302, 0.625727, 0.625727),
(0.603175, 0.634126, 0.634126),(0.619048, 0.642416, 0.642416),
(0.634921, 0.650600, 0.650600),(0.650794, 0.658682, 0.658682),
(0.666667, 0.666667, 0.666667),(0.682540, 0.674556, 0.674556),
(0.698413, 0.682355, 0.682355),(0.714286, 0.690066, 0.690066),
(0.730159, 0.697691, 0.697691),(0.746032, 0.705234, 0.705234),
(0.761905, 0.727166, 0.727166),(0.777778, 0.748455, 0.748455),
(0.793651, 0.769156, 0.769156),(0.809524, 0.789314, 0.789314),
(0.825397, 0.808969, 0.808969),(0.841270, 0.828159, 0.828159),
(0.857143, 0.846913, 0.846913),(0.873016, 0.865261, 0.865261),
(0.888889, 0.883229, 0.883229),(0.904762, 0.900837, 0.900837),
(0.920635, 0.918109, 0.918109),(0.936508, 0.935061, 0.935061),
(0.952381, 0.951711, 0.951711),(0.968254, 0.968075, 0.968075),
(0.984127, 0.984167, 0.984167),(1.0, 1.0, 1.0))}
_prism_data = {'red': ((0., 1., 1.),(0.031746, 1.000000, 1.000000),
(0.047619, 0.000000, 0.000000),(0.063492, 0.000000, 0.000000),
(0.079365, 0.666667, 0.666667),(0.095238, 1.000000, 1.000000),
(0.126984, 1.000000, 1.000000),(0.142857, 0.000000, 0.000000),
(0.158730, 0.000000, 0.000000),(0.174603, 0.666667, 0.666667),
(0.190476, 1.000000, 1.000000),(0.222222, 1.000000, 1.000000),
(0.238095, 0.000000, 0.000000),(0.253968, 0.000000, 0.000000),
(0.269841, 0.666667, 0.666667),(0.285714, 1.000000, 1.000000),
(0.317460, 1.000000, 1.000000),(0.333333, 0.000000, 0.000000),
(0.349206, 0.000000, 0.000000),(0.365079, 0.666667, 0.666667),
(0.380952, 1.000000, 1.000000),(0.412698, 1.000000, 1.000000),
(0.428571, 0.000000, 0.000000),(0.444444, 0.000000, 0.000000),
(0.460317, 0.666667, 0.666667),(0.476190, 1.000000, 1.000000),
(0.507937, 1.000000, 1.000000),(0.523810, 0.000000, 0.000000),
(0.539683, 0.000000, 0.000000),(0.555556, 0.666667, 0.666667),
(0.571429, 1.000000, 1.000000),(0.603175, 1.000000, 1.000000),
(0.619048, 0.000000, 0.000000),(0.634921, 0.000000, 0.000000),
(0.650794, 0.666667, 0.666667),(0.666667, 1.000000, 1.000000),
(0.698413, 1.000000, 1.000000),(0.714286, 0.000000, 0.000000),
(0.730159, 0.000000, 0.000000),(0.746032, 0.666667, 0.666667),
(0.761905, 1.000000, 1.000000),(0.793651, 1.000000, 1.000000),
(0.809524, 0.000000, 0.000000),(0.825397, 0.000000, 0.000000),
(0.841270, 0.666667, 0.666667),(0.857143, 1.000000, 1.000000),
(0.888889, 1.000000, 1.000000),(0.904762, 0.000000, 0.000000),
(0.920635, 0.000000, 0.000000),(0.936508, 0.666667, 0.666667),
(0.952381, 1.000000, 1.000000),(0.984127, 1.000000, 1.000000),
(1.0, 0.0, 0.0)),
'green': ((0., 0., 0.),(0.031746, 1.000000, 1.000000),
(0.047619, 1.000000, 1.000000),(0.063492, 0.000000, 0.000000),
(0.095238, 0.000000, 0.000000),(0.126984, 1.000000, 1.000000),
(0.142857, 1.000000, 1.000000),(0.158730, 0.000000, 0.000000),
(0.190476, 0.000000, 0.000000),(0.222222, 1.000000, 1.000000),
(0.238095, 1.000000, 1.000000),(0.253968, 0.000000, 0.000000),
(0.285714, 0.000000, 0.000000),(0.317460, 1.000000, 1.000000),
(0.333333, 1.000000, 1.000000),(0.349206, 0.000000, 0.000000),
(0.380952, 0.000000, 0.000000),(0.412698, 1.000000, 1.000000),
(0.428571, 1.000000, 1.000000),(0.444444, 0.000000, 0.000000),
(0.476190, 0.000000, 0.000000),(0.507937, 1.000000, 1.000000),
(0.523810, 1.000000, 1.000000),(0.539683, 0.000000, 0.000000),
(0.571429, 0.000000, 0.000000),(0.603175, 1.000000, 1.000000),
(0.619048, 1.000000, 1.000000),(0.634921, 0.000000, 0.000000),
(0.666667, 0.000000, 0.000000),(0.698413, 1.000000, 1.000000),
(0.714286, 1.000000, 1.000000),(0.730159, 0.000000, 0.000000),
(0.761905, 0.000000, 0.000000),(0.793651, 1.000000, 1.000000),
(0.809524, 1.000000, 1.000000),(0.825397, 0.000000, 0.000000),
(0.857143, 0.000000, 0.000000),(0.888889, 1.000000, 1.000000),
(0.904762, 1.000000, 1.000000),(0.920635, 0.000000, 0.000000),
(0.952381, 0.000000, 0.000000),(0.984127, 1.000000, 1.000000),
(1.0, 1.0, 1.0)),
'blue': ((0., 0., 0.),(0.047619, 0.000000, 0.000000),
(0.063492, 1.000000, 1.000000),(0.079365, 1.000000, 1.000000),
(0.095238, 0.000000, 0.000000),(0.142857, 0.000000, 0.000000),
(0.158730, 1.000000, 1.000000),(0.174603, 1.000000, 1.000000),
(0.190476, 0.000000, 0.000000),(0.238095, 0.000000, 0.000000),
(0.253968, 1.000000, 1.000000),(0.269841, 1.000000, 1.000000),
(0.285714, 0.000000, 0.000000),(0.333333, 0.000000, 0.000000),
(0.349206, 1.000000, 1.000000),(0.365079, 1.000000, 1.000000),
(0.380952, 0.000000, 0.000000),(0.428571, 0.000000, 0.000000),
(0.444444, 1.000000, 1.000000),(0.460317, 1.000000, 1.000000),
(0.476190, 0.000000, 0.000000),(0.523810, 0.000000, 0.000000),
(0.539683, 1.000000, 1.000000),(0.555556, 1.000000, 1.000000),
(0.571429, 0.000000, 0.000000),(0.619048, 0.000000, 0.000000),
(0.634921, 1.000000, 1.000000),(0.650794, 1.000000, 1.000000),
(0.666667, 0.000000, 0.000000),(0.714286, 0.000000, 0.000000),
(0.730159, 1.000000, 1.000000),(0.746032, 1.000000, 1.000000),
(0.761905, 0.000000, 0.000000),(0.809524, 0.000000, 0.000000),
(0.825397, 1.000000, 1.000000),(0.841270, 1.000000, 1.000000),
(0.857143, 0.000000, 0.000000),(0.904762, 0.000000, 0.000000),
(0.920635, 1.000000, 1.000000),(0.936508, 1.000000, 1.000000),
(0.952381, 0.000000, 0.000000),(1.0, 0.0, 0.0))}
_spring_data = {'red': ((0., 1., 1.),(1.0, 1.0, 1.0)),
'green': ((0., 0., 0.),(1.0, 1.0, 1.0)),
'blue': ((0., 1., 1.),(1.0, 0.0, 0.0))}
_summer_data = {'red': ((0., 0., 0.),(1.0, 1.0, 1.0)),
'green': ((0., 0.5, 0.5),(1.0, 1.0, 1.0)),
'blue': ((0., 0.4, 0.4),(1.0, 0.4, 0.4))}
_winter_data = {'red': ((0., 0., 0.),(1.0, 0.0, 0.0)),
'green': ((0., 0., 0.),(1.0, 1.0, 1.0)),
'blue': ((0., 1., 1.),(1.0, 0.5, 0.5))}
_spectral_data = {'red': [(0.0, 0.0, 0.0), (0.05, 0.4667, 0.4667),
(0.10, 0.5333, 0.5333), (0.15, 0.0, 0.0),
(0.20, 0.0, 0.0), (0.25, 0.0, 0.0),
(0.30, 0.0, 0.0), (0.35, 0.0, 0.0),
(0.40, 0.0, 0.0), (0.45, 0.0, 0.0),
(0.50, 0.0, 0.0), (0.55, 0.0, 0.0),
(0.60, 0.0, 0.0), (0.65, 0.7333, 0.7333),
(0.70, 0.9333, 0.9333), (0.75, 1.0, 1.0),
(0.80, 1.0, 1.0), (0.85, 1.0, 1.0),
(0.90, 0.8667, 0.8667), (0.95, 0.80, 0.80),
(1.0, 0.80, 0.80)],
'green': [(0.0, 0.0, 0.0), (0.05, 0.0, 0.0),
(0.10, 0.0, 0.0), (0.15, 0.0, 0.0),
(0.20, 0.0, 0.0), (0.25, 0.4667, 0.4667),
(0.30, 0.6000, 0.6000), (0.35, 0.6667, 0.6667),
(0.40, 0.6667, 0.6667), (0.45, 0.6000, 0.6000),
(0.50, 0.7333, 0.7333), (0.55, 0.8667, 0.8667),
(0.60, 1.0, 1.0), (0.65, 1.0, 1.0),
(0.70, 0.9333, 0.9333), (0.75, 0.8000, 0.8000),
(0.80, 0.6000, 0.6000), (0.85, 0.0, 0.0),
(0.90, 0.0, 0.0), (0.95, 0.0, 0.0),
(1.0, 0.80, 0.80)],
'blue': [(0.0, 0.0, 0.0), (0.05, 0.5333, 0.5333),
(0.10, 0.6000, 0.6000), (0.15, 0.6667, 0.6667),
(0.20, 0.8667, 0.8667), (0.25, 0.8667, 0.8667),
(0.30, 0.8667, 0.8667), (0.35, 0.6667, 0.6667),
(0.40, 0.5333, 0.5333), (0.45, 0.0, 0.0),
(0.5, 0.0, 0.0), (0.55, 0.0, 0.0),
(0.60, 0.0, 0.0), (0.65, 0.0, 0.0),
(0.70, 0.0, 0.0), (0.75, 0.0, 0.0),
(0.80, 0.0, 0.0), (0.85, 0.0, 0.0),
(0.90, 0.0, 0.0), (0.95, 0.0, 0.0),
(1.0, 0.80, 0.80)]}
autumn = colors.LinearSegmentedColormap('autumn', _autumn_data, LUTSIZE)
bone = colors.LinearSegmentedColormap('bone ', _bone_data, LUTSIZE)
binary = colors.LinearSegmentedColormap('binary ', _binary_data, LUTSIZE)
cool = colors.LinearSegmentedColormap('cool', _cool_data, LUTSIZE)
copper = colors.LinearSegmentedColormap('copper', _copper_data, LUTSIZE)
flag = colors.LinearSegmentedColormap('flag', _flag_data, LUTSIZE)
gray = colors.LinearSegmentedColormap('gray', _gray_data, LUTSIZE)
hot = colors.LinearSegmentedColormap('hot', _hot_data, LUTSIZE)
hsv = colors.LinearSegmentedColormap('hsv', _hsv_data, LUTSIZE)
jet = colors.LinearSegmentedColormap('jet', _jet_data, LUTSIZE)
pink = colors.LinearSegmentedColormap('pink', _pink_data, LUTSIZE)
prism = colors.LinearSegmentedColormap('prism', _prism_data, LUTSIZE)
spring = colors.LinearSegmentedColormap('spring', _spring_data, LUTSIZE)
summer = colors.LinearSegmentedColormap('summer', _summer_data, LUTSIZE)
winter = colors.LinearSegmentedColormap('winter', _winter_data, LUTSIZE)
spectral = colors.LinearSegmentedColormap('spectral', _spectral_data, LUTSIZE)
datad = {
'autumn': _autumn_data,
'bone': _bone_data,
'binary': _binary_data,
'cool': _cool_data,
'copper': _copper_data,
'flag': _flag_data,
'gray' : _gray_data,
'hot': _hot_data,
'hsv': _hsv_data,
'jet' : _jet_data,
'pink': _pink_data,
'prism': _prism_data,
'spring': _spring_data,
'summer': _summer_data,
'winter': _winter_data,
'spectral': _spectral_data
}
# 34 colormaps based on color specifications and designs
# developed by Cynthia Brewer (http://colorbrewer.org).
# The ColorBrewer palettes have been included under the terms
# of an Apache-stype license (for details, see the file
# LICENSE_COLORBREWER in the license directory of the matplotlib
# source distribution).
_Accent_data = {'blue': [(0.0, 0.49803921580314636,
0.49803921580314636), (0.14285714285714285, 0.83137255907058716,
0.83137255907058716), (0.2857142857142857, 0.52549022436141968,
0.52549022436141968), (0.42857142857142855, 0.60000002384185791,
0.60000002384185791), (0.5714285714285714, 0.69019609689712524,
0.69019609689712524), (0.7142857142857143, 0.49803921580314636,
0.49803921580314636), (0.8571428571428571, 0.090196080505847931,
0.090196080505847931), (1.0, 0.40000000596046448,
0.40000000596046448)],
'green': [(0.0, 0.78823530673980713, 0.78823530673980713),
(0.14285714285714285, 0.68235296010971069, 0.68235296010971069),
(0.2857142857142857, 0.75294119119644165, 0.75294119119644165),
(0.42857142857142855, 1.0, 1.0), (0.5714285714285714,
0.42352941632270813, 0.42352941632270813), (0.7142857142857143,
0.0078431377187371254, 0.0078431377187371254),
(0.8571428571428571, 0.35686275362968445, 0.35686275362968445),
(1.0, 0.40000000596046448, 0.40000000596046448)],
'red': [(0.0, 0.49803921580314636, 0.49803921580314636),
(0.14285714285714285, 0.7450980544090271, 0.7450980544090271),
(0.2857142857142857, 0.99215686321258545, 0.99215686321258545),
(0.42857142857142855, 1.0, 1.0), (0.5714285714285714,
0.21960784494876862, 0.21960784494876862), (0.7142857142857143,
0.94117647409439087, 0.94117647409439087), (0.8571428571428571,
0.74901962280273438, 0.74901962280273438), (1.0,
0.40000000596046448, 0.40000000596046448)]}
_Blues_data = {'blue': [(0.0, 1.0, 1.0), (0.125, 0.9686274528503418,
0.9686274528503418), (0.25, 0.93725490570068359, 0.93725490570068359),
(0.375, 0.88235294818878174, 0.88235294818878174), (0.5,
0.83921569585800171, 0.83921569585800171), (0.625, 0.7764706015586853,
0.7764706015586853), (0.75, 0.70980393886566162, 0.70980393886566162),
(0.875, 0.61176472902297974, 0.61176472902297974), (1.0,
0.41960784792900085, 0.41960784792900085)],
'green': [(0.0, 0.9843137264251709, 0.9843137264251709), (0.125,
0.92156863212585449, 0.92156863212585449), (0.25,
0.85882353782653809, 0.85882353782653809), (0.375,
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(0.18181818181818182, 0.69803923368453979, 0.69803923368453979),
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0.7921568751335144, 0.7921568751335144), (0.81818181818181823,
0.41568627953529358, 0.41568627953529358), (0.90909090909090906,
1.0, 1.0), (1.0, 0.69411766529083252, 0.69411766529083252)]}
_Pastel1_data = {'blue': [(0.0, 0.68235296010971069,
0.68235296010971069), (0.125, 0.89019608497619629,
0.89019608497619629), (0.25, 0.77254903316497803,
0.77254903316497803), (0.375, 0.89411765336990356,
0.89411765336990356), (0.5, 0.65098041296005249, 0.65098041296005249),
(0.625, 0.80000001192092896, 0.80000001192092896), (0.75,
0.74117648601531982, 0.74117648601531982), (0.875,
0.92549020051956177, 0.92549020051956177), (1.0, 0.94901961088180542,
0.94901961088180542)],
'green': [(0.0, 0.70588237047195435, 0.70588237047195435), (0.125,
0.80392158031463623, 0.80392158031463623), (0.25,
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0.85098040103912354, 0.85098040103912354), (0.625, 1.0, 1.0),
(0.75, 0.84705883264541626, 0.84705883264541626), (0.875,
0.85490196943283081, 0.85490196943283081), (1.0,
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'red': [(0.0, 0.9843137264251709, 0.9843137264251709), (0.125,
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0.87058824300765991, 0.87058824300765991), (0.5,
0.99607843160629272, 0.99607843160629272), (0.625, 1.0, 1.0),
(0.75, 0.89803922176361084, 0.89803922176361084), (0.875,
0.99215686321258545, 0.99215686321258545), (1.0,
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_Pastel2_data = {'blue': [(0.0, 0.80392158031463623,
0.80392158031463623), (0.14285714285714285, 0.67450982332229614,
0.67450982332229614), (0.2857142857142857, 0.90980392694473267,
0.90980392694473267), (0.42857142857142855, 0.89411765336990356,
0.89411765336990356), (0.5714285714285714, 0.78823530673980713,
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0.68235296010971069), (0.8571428571428571, 0.80000001192092896,
0.80000001192092896), (1.0, 0.80000001192092896,
0.80000001192092896)],
'green': [(0.0, 0.88627451658248901, 0.88627451658248901),
(0.14285714285714285, 0.80392158031463623, 0.80392158031463623),
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(1.0, 0.80000001192092896, 0.80000001192092896)],
'red': [(0.0, 0.70196080207824707, 0.70196080207824707),
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(0.7142857142857143, 1.0, 1.0), (0.8571428571428571,
0.94509804248809814, 0.94509804248809814), (1.0,
0.80000001192092896, 0.80000001192092896)]}
_PiYG_data = {'blue': [(0.0, 0.32156863808631897,
0.32156863808631897), (0.10000000000000001, 0.49019607901573181,
0.49019607901573181), (0.20000000000000001, 0.68235296010971069,
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0.93725490570068359), (0.5, 0.9686274528503418, 0.9686274528503418),
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0.098039217293262482, 0.098039217293262482)],
'green': [(0.0, 0.0039215688593685627, 0.0039215688593685627),
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'red': [(0.0, 0.55686277151107788, 0.55686277151107788),
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_PRGn_data = {'blue': [(0.0, 0.29411765933036804,
0.29411765933036804), (0.10000000000000001, 0.51372551918029785,
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0.67058825492858887), (0.29999999999999999, 0.81176471710205078,
0.81176471710205078), (0.40000000000000002, 0.90980392694473267,
0.90980392694473267), (0.5, 0.9686274528503418, 0.9686274528503418),
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'green': [(0.0, 0.0, 0.0), (0.10000000000000001,
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'red': [(0.0, 0.25098040699958801, 0.25098040699958801),
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_PuBu_data = {'blue': [(0.0, 0.9843137264251709, 0.9843137264251709),
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0.81176471710205078), (0.625, 0.75294119119644165,
0.75294119119644165), (0.75, 0.69019609689712524,
0.69019609689712524), (0.875, 0.55294120311737061,
0.55294120311737061), (1.0, 0.34509804844856262,
0.34509804844856262)],
'green': [(0.0, 0.9686274528503418, 0.9686274528503418), (0.125,
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'red': [(0.0, 1.0, 1.0), (0.125, 0.92549020051956177,
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_PuBuGn_data = {'blue': [(0.0, 0.9843137264251709,
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'green': [(0.0, 0.9686274528503418, 0.9686274528503418), (0.125,
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'red': [(0.0, 1.0, 1.0), (0.125, 0.92549020051956177,
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0.0039215688593685627), (1.0, 0.0039215688593685627,
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_PuOr_data = {'blue': [(0.0, 0.031372550874948502,
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'red': [(0.0, 0.49803921580314636, 0.49803921580314636),
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_PuRd_data = {'blue': [(0.0, 0.97647058963775635,
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'green': [(0.0, 0.95686274766921997, 0.95686274766921997), (0.125,
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'red': [(0.0, 0.9686274528503418, 0.9686274528503418), (0.125,
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0.59607845544815063, 0.59607845544815063), (1.0,
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_Purples_data = {'blue': [(0.0, 0.99215686321258545,
0.99215686321258545), (0.125, 0.96078431606292725,
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'green': [(0.0, 0.9843137264251709, 0.9843137264251709), (0.125,
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'red': [(0.0, 0.98823529481887817, 0.98823529481887817), (0.125,
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_RdBu_data = {'blue': [(0.0, 0.12156862765550613,
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_RdGy_data = {'blue': [(0.0, 0.12156862765550613,
0.12156862765550613), (0.10000000000000001, 0.16862745583057404,
0.16862745583057404), (0.20000000000000001, 0.30196079611778259,
0.30196079611778259), (0.29999999999999999, 0.50980395078659058,
0.50980395078659058), (0.40000000000000002, 0.78039216995239258,
0.78039216995239258), (0.5, 1.0, 1.0), (0.59999999999999998,
0.87843137979507446, 0.87843137979507446), (0.69999999999999996,
0.729411780834198, 0.729411780834198), (0.80000000000000004,
0.52941179275512695, 0.52941179275512695), (0.90000000000000002,
0.30196079611778259, 0.30196079611778259), (1.0, 0.10196078568696976,
0.10196078568696976)],
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0.85882353782653809, 0.85882353782653809), (0.5, 1.0, 1.0),
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(1.0, 0.10196078568696976, 0.10196078568696976)],
'red': [(0.0, 0.40392157435417175, 0.40392157435417175),
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0.87843137979507446), (0.69999999999999996, 0.729411780834198,
0.729411780834198), (0.80000000000000004, 0.52941179275512695,
0.52941179275512695), (0.90000000000000002, 0.30196079611778259,
0.30196079611778259), (1.0, 0.10196078568696976,
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_RdPu_data = {'blue': [(0.0, 0.9529411792755127, 0.9529411792755127),
(0.125, 0.86666667461395264, 0.86666667461395264), (0.25,
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0.63137257099151611), (0.625, 0.59215688705444336,
0.59215688705444336), (0.75, 0.49411764740943909,
0.49411764740943909), (0.875, 0.46666666865348816,
0.46666666865348816), (1.0, 0.41568627953529358,
0.41568627953529358)],
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0.47843137383460999), (1.0, 0.28627452254295349,
0.28627452254295349)]}
_RdYlBu_data = {'blue': [(0.0, 0.14901961386203766,
0.14901961386203766), (0.10000000149011612,
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0.26274511218070984), (0.30000001192092896,
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0.56470590829849243), (0.5, 0.74901962280273438,
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0.91372549533843994), (0.80000001192092896,
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0.70588237047195435), (1.0, 0.58431375026702881,
0.58431375026702881)], 'green': [(0.0, 0.0, 0.0),
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0.18823529779911041), (0.20000000298023224,
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0.68235296010971069), (0.40000000596046448,
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1.0), (0.60000002384185791, 0.9529411792755127,
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1.0), (0.60000002384185791, 0.87843137979507446,
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0.45490196347236633), (0.89999997615814209,
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0.19215686619281769, 0.19215686619281769)]}
_RdYlGn_data = {'blue': [(0.0, 0.14901961386203766,
0.14901961386203766), (0.10000000000000001, 0.15294118225574493,
0.15294118225574493), (0.20000000000000001, 0.26274511218070984,
0.26274511218070984), (0.29999999999999999, 0.3803921639919281,
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0.54509806632995605), (0.5, 0.74901962280273438, 0.74901962280273438),
(0.59999999999999998, 0.54509806632995605, 0.54509806632995605),
(0.69999999999999996, 0.41568627953529358, 0.41568627953529358),
(0.80000000000000004, 0.38823530077934265, 0.38823530077934265),
(0.90000000000000002, 0.31372550129890442, 0.31372550129890442), (1.0,
0.21568627655506134, 0.21568627655506134)],
'green': [(0.0, 0.0, 0.0), (0.10000000000000001,
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0.87843137979507446, 0.87843137979507446), (0.5, 1.0, 1.0),
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'red': [(0.0, 0.64705884456634521, 0.64705884456634521),
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0.40000000596046448), (0.90000000000000002, 0.10196078568696976,
0.10196078568696976), (1.0, 0.0, 0.0)]}
_Reds_data = {'blue': [(0.0, 0.94117647409439087,
0.94117647409439087), (0.125, 0.82352942228317261,
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0.63137257099151611), (0.375, 0.44705882668495178,
0.44705882668495178), (0.5, 0.29019609093666077, 0.29019609093666077),
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0.050980392843484879)],
'green': [(0.0, 0.96078431606292725, 0.96078431606292725), (0.125,
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'red': [(0.0, 1.0, 1.0), (0.125, 0.99607843160629272,
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_Set1_data = {'blue': [(0.0, 0.10980392247438431,
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_Set2_data = {'blue': [(0.0, 0.64705884456634521,
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'red': [(0.0, 0.40000000596046448, 0.40000000596046448),
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_Set3_data = {'blue': [(0.0, 0.78039216995239258,
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'red': [(0.0, 0.55294120311737061, 0.55294120311737061),
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_Spectral_data = {'blue': [(0.0, 0.25882354378700256,
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(0.20000000000000001, 0.42745098471641541, 0.42745098471641541),
(0.29999999999999999, 0.68235296010971069, 0.68235296010971069),
(0.40000000000000002, 0.87843137979507446, 0.87843137979507446),
(0.5, 1.0, 1.0), (0.59999999999999998, 0.96078431606292725,
0.96078431606292725), (0.69999999999999996, 0.86666667461395264,
0.86666667461395264), (0.80000000000000004, 0.7607843279838562,
0.7607843279838562), (0.90000000000000002, 0.53333336114883423,
0.53333336114883423), (1.0, 0.30980393290519714,
0.30980393290519714)],
'red': [(0.0, 0.61960786581039429, 0.61960786581039429),
(0.10000000000000001, 0.83529412746429443, 0.83529412746429443),
(0.20000000000000001, 0.95686274766921997, 0.95686274766921997),
(0.29999999999999999, 0.99215686321258545, 0.99215686321258545),
(0.40000000000000002, 0.99607843160629272, 0.99607843160629272),
(0.5, 1.0, 1.0), (0.59999999999999998, 0.90196079015731812,
0.90196079015731812), (0.69999999999999996, 0.67058825492858887,
0.67058825492858887), (0.80000000000000004, 0.40000000596046448,
0.40000000596046448), (0.90000000000000002, 0.19607843458652496,
0.19607843458652496), (1.0, 0.36862745881080627,
0.36862745881080627)]}
_YlGn_data = {'blue': [(0.0, 0.89803922176361084,
0.89803922176361084), (0.125, 0.72549021244049072,
0.72549021244049072), (0.25, 0.63921570777893066,
0.63921570777893066), (0.375, 0.55686277151107788,
0.55686277151107788), (0.5, 0.47450980544090271, 0.47450980544090271),
(0.625, 0.364705890417099, 0.364705890417099), (0.75,
0.26274511218070984, 0.26274511218070984), (0.875,
0.21568627655506134, 0.21568627655506134), (1.0, 0.16078431904315948,
0.16078431904315948)],
'green': [(0.0, 1.0, 1.0), (0.125, 0.98823529481887817,
0.98823529481887817), (0.25, 0.94117647409439087,
0.94117647409439087), (0.375, 0.86666667461395264,
0.86666667461395264), (0.5, 0.7764706015586853,
0.7764706015586853), (0.625, 0.67058825492858887,
0.67058825492858887), (0.75, 0.51764708757400513,
0.51764708757400513), (0.875, 0.40784314274787903,
0.40784314274787903), (1.0, 0.27058824896812439,
0.27058824896812439)],
'red': [(0.0, 1.0, 1.0), (0.125, 0.9686274528503418,
0.9686274528503418), (0.25, 0.85098040103912354,
0.85098040103912354), (0.375, 0.67843139171600342,
0.67843139171600342), (0.5, 0.47058823704719543,
0.47058823704719543), (0.625, 0.25490197539329529,
0.25490197539329529), (0.75, 0.13725490868091583,
0.13725490868091583), (0.875, 0.0, 0.0), (1.0, 0.0, 0.0)]}
_YlGnBu_data = {'blue': [(0.0, 0.85098040103912354,
0.85098040103912354), (0.125, 0.69411766529083252,
0.69411766529083252), (0.25, 0.70588237047195435,
0.70588237047195435), (0.375, 0.73333334922790527,
0.73333334922790527), (0.5, 0.76862746477127075, 0.76862746477127075),
(0.625, 0.75294119119644165, 0.75294119119644165), (0.75,
0.65882354974746704, 0.65882354974746704), (0.875,
0.58039218187332153, 0.58039218187332153), (1.0, 0.34509804844856262,
0.34509804844856262)],
'green': [(0.0, 1.0, 1.0), (0.125, 0.97254902124404907,
0.97254902124404907), (0.25, 0.91372549533843994,
0.91372549533843994), (0.375, 0.80392158031463623,
0.80392158031463623), (0.5, 0.7137255072593689,
0.7137255072593689), (0.625, 0.56862747669219971,
0.56862747669219971), (0.75, 0.36862745881080627,
0.36862745881080627), (0.875, 0.20392157137393951,
0.20392157137393951), (1.0, 0.11372549086809158,
0.11372549086809158)],
'red': [(0.0, 1.0, 1.0), (0.125, 0.92941176891326904,
0.92941176891326904), (0.25, 0.78039216995239258,
0.78039216995239258), (0.375, 0.49803921580314636,
0.49803921580314636), (0.5, 0.25490197539329529,
0.25490197539329529), (0.625, 0.11372549086809158,
0.11372549086809158), (0.75, 0.13333334028720856,
0.13333334028720856), (0.875, 0.14509804546833038,
0.14509804546833038), (1.0, 0.031372550874948502,
0.031372550874948502)]}
_YlOrBr_data = {'blue': [(0.0, 0.89803922176361084,
0.89803922176361084), (0.125, 0.73725491762161255,
0.73725491762161255), (0.25, 0.56862747669219971,
0.56862747669219971), (0.375, 0.30980393290519714,
0.30980393290519714), (0.5, 0.16078431904315948, 0.16078431904315948),
(0.625, 0.078431375324726105, 0.078431375324726105), (0.75,
0.0078431377187371254, 0.0078431377187371254), (0.875,
0.015686275437474251, 0.015686275437474251), (1.0,
0.023529412224888802, 0.023529412224888802)],
'green': [(0.0, 1.0, 1.0), (0.125, 0.9686274528503418,
0.9686274528503418), (0.25, 0.89019608497619629,
0.89019608497619629), (0.375, 0.76862746477127075,
0.76862746477127075), (0.5, 0.60000002384185791,
0.60000002384185791), (0.625, 0.43921568989753723,
0.43921568989753723), (0.75, 0.29803922772407532,
0.29803922772407532), (0.875, 0.20392157137393951,
0.20392157137393951), (1.0, 0.14509804546833038,
0.14509804546833038)],
'red': [(0.0, 1.0, 1.0), (0.125, 1.0, 1.0), (0.25,
0.99607843160629272, 0.99607843160629272), (0.375,
0.99607843160629272, 0.99607843160629272), (0.5,
0.99607843160629272, 0.99607843160629272), (0.625,
0.92549020051956177, 0.92549020051956177), (0.75,
0.80000001192092896, 0.80000001192092896), (0.875,
0.60000002384185791, 0.60000002384185791), (1.0,
0.40000000596046448, 0.40000000596046448)]}
_YlOrRd_data = {'blue': [(0.0, 0.80000001192092896,
0.80000001192092896), (0.125, 0.62745100259780884,
0.62745100259780884), (0.25, 0.46274510025978088,
0.46274510025978088), (0.375, 0.29803922772407532,
0.29803922772407532), (0.5, 0.23529411852359772, 0.23529411852359772),
(0.625, 0.16470588743686676, 0.16470588743686676), (0.75,
0.10980392247438431, 0.10980392247438431), (0.875,
0.14901961386203766, 0.14901961386203766), (1.0, 0.14901961386203766,
0.14901961386203766)],
'green': [(0.0, 1.0, 1.0), (0.125, 0.92941176891326904,
0.92941176891326904), (0.25, 0.85098040103912354,
0.85098040103912354), (0.375, 0.69803923368453979,
0.69803923368453979), (0.5, 0.55294120311737061,
0.55294120311737061), (0.625, 0.30588236451148987,
0.30588236451148987), (0.75, 0.10196078568696976,
0.10196078568696976), (0.875, 0.0, 0.0), (1.0, 0.0, 0.0)],
'red': [(0.0, 1.0, 1.0), (0.125, 1.0, 1.0), (0.25,
0.99607843160629272, 0.99607843160629272), (0.375,
0.99607843160629272, 0.99607843160629272), (0.5,
0.99215686321258545, 0.99215686321258545), (0.625,
0.98823529481887817, 0.98823529481887817), (0.75,
0.89019608497619629, 0.89019608497619629), (0.875,
0.74117648601531982, 0.74117648601531982), (1.0,
0.50196081399917603, 0.50196081399917603)]}
# The next 7 palettes are from the Yorick scientific visalisation package,
# an evolution of the GIST package, both by David H. Munro.
# They are released under a BSD-like license (see LICENSE_YORICK in
# the license directory of the matplotlib source distribution).
_gist_earth_data = {'blue': [(0.0, 0.0, 0.0), (0.0042016808874905109,
0.18039216101169586, 0.18039216101169586), (0.0084033617749810219,
0.22745098173618317, 0.22745098173618317), (0.012605042196810246,
0.27058824896812439, 0.27058824896812439), (0.016806723549962044,
0.31764706969261169, 0.31764706969261169), (0.021008403971791267,
0.36078432202339172, 0.36078432202339172), (0.025210084393620491,
0.40784314274787903, 0.40784314274787903), (0.029411764815449715,
0.45490196347236633, 0.45490196347236633), (0.033613447099924088,
0.45490196347236633, 0.45490196347236633), (0.037815127521753311,
0.45490196347236633, 0.45490196347236633), (0.042016807943582535,
0.45490196347236633, 0.45490196347236633), (0.046218488365411758,
0.45490196347236633, 0.45490196347236633), (0.050420168787240982,
0.45882353186607361, 0.45882353186607361), (0.054621849209070206,
0.45882353186607361, 0.45882353186607361), (0.058823529630899429,
0.45882353186607361, 0.45882353186607361), (0.063025213778018951,
0.45882353186607361, 0.45882353186607361), (0.067226894199848175,
0.45882353186607361, 0.45882353186607361), (0.071428574621677399,
0.46274510025978088, 0.46274510025978088), (0.075630255043506622,
0.46274510025978088, 0.46274510025978088), (0.079831935465335846,
0.46274510025978088, 0.46274510025978088), (0.08403361588716507,
0.46274510025978088, 0.46274510025978088), (0.088235296308994293,
0.46274510025978088, 0.46274510025978088), (0.092436976730823517,
0.46666666865348816, 0.46666666865348816), (0.09663865715265274,
0.46666666865348816, 0.46666666865348816), (0.10084033757448196,
0.46666666865348816, 0.46666666865348816), (0.10504201799631119,
0.46666666865348816, 0.46666666865348816), (0.10924369841814041,
0.46666666865348816, 0.46666666865348816), (0.11344537883996964,
0.47058823704719543, 0.47058823704719543), (0.11764705926179886,
0.47058823704719543, 0.47058823704719543), (0.12184873968362808,
0.47058823704719543, 0.47058823704719543), (0.1260504275560379,
0.47058823704719543, 0.47058823704719543), (0.13025210797786713,
0.47058823704719543, 0.47058823704719543), (0.13445378839969635,
0.47450980544090271, 0.47450980544090271), (0.13865546882152557,
0.47450980544090271, 0.47450980544090271), (0.1428571492433548,
0.47450980544090271, 0.47450980544090271), (0.14705882966518402,
0.47450980544090271, 0.47450980544090271), (0.15126051008701324,
0.47450980544090271, 0.47450980544090271), (0.15546219050884247,
0.47843137383460999, 0.47843137383460999), (0.15966387093067169,
0.47843137383460999, 0.47843137383460999), (0.16386555135250092,
0.47843137383460999, 0.47843137383460999), (0.16806723177433014,
0.47843137383460999, 0.47843137383460999), (0.17226891219615936,
0.47843137383460999, 0.47843137383460999), (0.17647059261798859,
0.48235294222831726, 0.48235294222831726), (0.18067227303981781,
0.48235294222831726, 0.48235294222831726), (0.18487395346164703,
0.48235294222831726, 0.48235294222831726), (0.18907563388347626,
0.48235294222831726, 0.48235294222831726), (0.19327731430530548,
0.48235294222831726, 0.48235294222831726), (0.1974789947271347,
0.48627451062202454, 0.48627451062202454), (0.20168067514896393,
0.48627451062202454, 0.48627451062202454), (0.20588235557079315,
0.48627451062202454, 0.48627451062202454), (0.21008403599262238,
0.48627451062202454, 0.48627451062202454), (0.2142857164144516,
0.48627451062202454, 0.48627451062202454), (0.21848739683628082,
0.49019607901573181, 0.49019607901573181), (0.22268907725811005,
0.49019607901573181, 0.49019607901573181), (0.22689075767993927,
0.49019607901573181, 0.49019607901573181), (0.23109243810176849,
0.49019607901573181, 0.49019607901573181), (0.23529411852359772,
0.49019607901573181, 0.49019607901573181), (0.23949579894542694,
0.49411764740943909, 0.49411764740943909), (0.24369747936725616,
0.49411764740943909, 0.49411764740943909), (0.24789915978908539,
0.49411764740943909, 0.49411764740943909), (0.25210085511207581,
0.49411764740943909, 0.49411764740943909), (0.25630253553390503,
0.49411764740943909, 0.49411764740943909), (0.26050421595573425,
0.49803921580314636, 0.49803921580314636), (0.26470589637756348,
0.49803921580314636, 0.49803921580314636), (0.2689075767993927,
0.49803921580314636, 0.49803921580314636), (0.27310925722122192,
0.49803921580314636, 0.49803921580314636), (0.27731093764305115,
0.49803921580314636, 0.49803921580314636), (0.28151261806488037,
0.50196081399917603, 0.50196081399917603), (0.28571429848670959,
0.49411764740943909, 0.49411764740943909), (0.28991597890853882,
0.49019607901573181, 0.49019607901573181), (0.29411765933036804,
0.48627451062202454, 0.48627451062202454), (0.29831933975219727,
0.48235294222831726, 0.48235294222831726), (0.30252102017402649,
0.47843137383460999, 0.47843137383460999), (0.30672270059585571,
0.47058823704719543, 0.47058823704719543), (0.31092438101768494,
0.46666666865348816, 0.46666666865348816), (0.31512606143951416,
0.46274510025978088, 0.46274510025978088), (0.31932774186134338,
0.45882353186607361, 0.45882353186607361), (0.32352942228317261,
0.45098039507865906, 0.45098039507865906), (0.32773110270500183,
0.44705882668495178, 0.44705882668495178), (0.33193278312683105,
0.44313725829124451, 0.44313725829124451), (0.33613446354866028,
0.43529412150382996, 0.43529412150382996), (0.3403361439704895,
0.43137255311012268, 0.43137255311012268), (0.34453782439231873,
0.42745098471641541, 0.42745098471641541), (0.34873950481414795,
0.42352941632270813, 0.42352941632270813), (0.35294118523597717,
0.41568627953529358, 0.41568627953529358), (0.3571428656578064,
0.4117647111415863, 0.4117647111415863), (0.36134454607963562,
0.40784314274787903, 0.40784314274787903), (0.36554622650146484,
0.40000000596046448, 0.40000000596046448), (0.36974790692329407,
0.3960784375667572, 0.3960784375667572), (0.37394958734512329,
0.39215686917304993, 0.39215686917304993), (0.37815126776695251,
0.38431373238563538, 0.38431373238563538), (0.38235294818878174,
0.3803921639919281, 0.3803921639919281), (0.38655462861061096,
0.37647059559822083, 0.37647059559822083), (0.39075630903244019,
0.36862745881080627, 0.36862745881080627), (0.39495798945426941,
0.364705890417099, 0.364705890417099), (0.39915966987609863,
0.36078432202339172, 0.36078432202339172), (0.40336135029792786,
0.35294118523597717, 0.35294118523597717), (0.40756303071975708,
0.3490196168422699, 0.3490196168422699), (0.4117647111415863,
0.34509804844856262, 0.34509804844856262), (0.41596639156341553,
0.33725491166114807, 0.33725491166114807), (0.42016807198524475,
0.3333333432674408, 0.3333333432674408), (0.42436975240707397,
0.32941177487373352, 0.32941177487373352), (0.4285714328289032,
0.32156863808631897, 0.32156863808631897), (0.43277311325073242,
0.31764706969261169, 0.31764706969261169), (0.43697479367256165,
0.31372550129890442, 0.31372550129890442), (0.44117647409439087,
0.30588236451148987, 0.30588236451148987), (0.44537815451622009,
0.30196079611778259, 0.30196079611778259), (0.44957983493804932,
0.29803922772407532, 0.29803922772407532), (0.45378151535987854,
0.29019609093666077, 0.29019609093666077), (0.45798319578170776,
0.28627452254295349, 0.28627452254295349), (0.46218487620353699,
0.27843138575553894, 0.27843138575553894), (0.46638655662536621,
0.27450981736183167, 0.27450981736183167), (0.47058823704719543,
0.27843138575553894, 0.27843138575553894), (0.47478991746902466,
0.28235295414924622, 0.28235295414924622), (0.47899159789085388,
0.28235295414924622, 0.28235295414924622), (0.48319327831268311,
0.28627452254295349, 0.28627452254295349), (0.48739495873451233,
0.28627452254295349, 0.28627452254295349), (0.49159663915634155,
0.29019609093666077, 0.29019609093666077), (0.49579831957817078,
0.29411765933036804, 0.29411765933036804), (0.5, 0.29411765933036804,
0.29411765933036804), (0.50420171022415161, 0.29803922772407532,
0.29803922772407532), (0.50840336084365845, 0.29803922772407532,
0.29803922772407532), (0.51260507106781006, 0.30196079611778259,
0.30196079611778259), (0.51680672168731689, 0.30196079611778259,
0.30196079611778259), (0.52100843191146851, 0.30588236451148987,
0.30588236451148987), (0.52521008253097534, 0.30980393290519714,
0.30980393290519714), (0.52941179275512695, 0.30980393290519714,
0.30980393290519714), (0.53361344337463379, 0.31372550129890442,
0.31372550129890442), (0.5378151535987854, 0.31372550129890442,
0.31372550129890442), (0.54201680421829224, 0.31764706969261169,
0.31764706969261169), (0.54621851444244385, 0.32156863808631897,
0.32156863808631897), (0.55042016506195068, 0.32156863808631897,
0.32156863808631897), (0.55462187528610229, 0.32156863808631897,
0.32156863808631897), (0.55882352590560913, 0.32549020648002625,
0.32549020648002625), (0.56302523612976074, 0.32549020648002625,
0.32549020648002625), (0.56722688674926758, 0.32549020648002625,
0.32549020648002625), (0.57142859697341919, 0.32941177487373352,
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0.0078431377187371254, 0.0078431377187371254), (1.0,
0.0039215688593685627, 0.0039215688593685627)]}
Accent = colors.LinearSegmentedColormap('Accent', _Accent_data, LUTSIZE)
Blues = colors.LinearSegmentedColormap('Blues', _Blues_data, LUTSIZE)
BrBG = colors.LinearSegmentedColormap('BrBG', _BrBG_data, LUTSIZE)
BuGn = colors.LinearSegmentedColormap('BuGn', _BuGn_data, LUTSIZE)
BuPu = colors.LinearSegmentedColormap('BuPu', _BuPu_data, LUTSIZE)
Dark2 = colors.LinearSegmentedColormap('Dark2', _Dark2_data, LUTSIZE)
GnBu = colors.LinearSegmentedColormap('GnBu', _GnBu_data, LUTSIZE)
Greens = colors.LinearSegmentedColormap('Greens', _Greens_data, LUTSIZE)
Greys = colors.LinearSegmentedColormap('Greys', _Greys_data, LUTSIZE)
Oranges = colors.LinearSegmentedColormap('Oranges', _Oranges_data, LUTSIZE)
OrRd = colors.LinearSegmentedColormap('OrRd', _OrRd_data, LUTSIZE)
Paired = colors.LinearSegmentedColormap('Paired', _Paired_data, LUTSIZE)
Pastel1 = colors.LinearSegmentedColormap('Pastel1', _Pastel1_data, LUTSIZE)
Pastel2 = colors.LinearSegmentedColormap('Pastel2', _Pastel2_data, LUTSIZE)
PiYG = colors.LinearSegmentedColormap('PiYG', _PiYG_data, LUTSIZE)
PRGn = colors.LinearSegmentedColormap('PRGn', _PRGn_data, LUTSIZE)
PuBu = colors.LinearSegmentedColormap('PuBu', _PuBu_data, LUTSIZE)
PuBuGn = colors.LinearSegmentedColormap('PuBuGn', _PuBuGn_data, LUTSIZE)
PuOr = colors.LinearSegmentedColormap('PuOr', _PuOr_data, LUTSIZE)
PuRd = colors.LinearSegmentedColormap('PuRd', _PuRd_data, LUTSIZE)
Purples = colors.LinearSegmentedColormap('Purples', _Purples_data, LUTSIZE)
RdBu = colors.LinearSegmentedColormap('RdBu', _RdBu_data, LUTSIZE)
RdGy = colors.LinearSegmentedColormap('RdGy', _RdGy_data, LUTSIZE)
RdPu = colors.LinearSegmentedColormap('RdPu', _RdPu_data, LUTSIZE)
RdYlBu = colors.LinearSegmentedColormap('RdYlBu', _RdYlBu_data, LUTSIZE)
RdYlGn = colors.LinearSegmentedColormap('RdYlGn', _RdYlGn_data, LUTSIZE)
Reds = colors.LinearSegmentedColormap('Reds', _Reds_data, LUTSIZE)
Set1 = colors.LinearSegmentedColormap('Set1', _Set1_data, LUTSIZE)
Set2 = colors.LinearSegmentedColormap('Set2', _Set2_data, LUTSIZE)
Set3 = colors.LinearSegmentedColormap('Set3', _Set3_data, LUTSIZE)
Spectral = colors.LinearSegmentedColormap('Spectral', _Spectral_data, LUTSIZE)
YlGn = colors.LinearSegmentedColormap('YlGn', _YlGn_data, LUTSIZE)
YlGnBu = colors.LinearSegmentedColormap('YlGnBu', _YlGnBu_data, LUTSIZE)
YlOrBr = colors.LinearSegmentedColormap('YlOrBr', _YlOrBr_data, LUTSIZE)
YlOrRd = colors.LinearSegmentedColormap('YlOrRd', _YlOrRd_data, LUTSIZE)
gist_earth = colors.LinearSegmentedColormap('gist_earth', _gist_earth_data, LUTSIZE)
gist_gray = colors.LinearSegmentedColormap('gist_gray', _gist_gray_data, LUTSIZE)
gist_heat = colors.LinearSegmentedColormap('gist_heat', _gist_heat_data, LUTSIZE)
gist_ncar = colors.LinearSegmentedColormap('gist_ncar', _gist_ncar_data, LUTSIZE)
gist_rainbow = colors.LinearSegmentedColormap('gist_rainbow', _gist_rainbow_data, LUTSIZE)
gist_stern = colors.LinearSegmentedColormap('gist_stern', _gist_stern_data, LUTSIZE)
gist_yarg = colors.LinearSegmentedColormap('gist_yarg', _gist_yarg_data, LUTSIZE)
datad['Accent']=_Accent_data
datad['Blues']=_Blues_data
datad['BrBG']=_BrBG_data
datad['BuGn']=_BuGn_data
datad['BuPu']=_BuPu_data
datad['Dark2']=_Dark2_data
datad['GnBu']=_GnBu_data
datad['Greens']=_Greens_data
datad['Greys']=_Greys_data
datad['Oranges']=_Oranges_data
datad['OrRd']=_OrRd_data
datad['Paired']=_Paired_data
datad['Pastel1']=_Pastel1_data
datad['Pastel2']=_Pastel2_data
datad['PiYG']=_PiYG_data
datad['PRGn']=_PRGn_data
datad['PuBu']=_PuBu_data
datad['PuBuGn']=_PuBuGn_data
datad['PuOr']=_PuOr_data
datad['PuRd']=_PuRd_data
datad['Purples']=_Purples_data
datad['RdBu']=_RdBu_data
datad['RdGy']=_RdGy_data
datad['RdPu']=_RdPu_data
datad['RdYlBu']=_RdYlBu_data
datad['RdYlGn']=_RdYlGn_data
datad['Reds']=_Reds_data
datad['Set1']=_Set1_data
datad['Set2']=_Set2_data
datad['Set3']=_Set3_data
datad['Spectral']=_Spectral_data
datad['YlGn']=_YlGn_data
datad['YlGnBu']=_YlGnBu_data
datad['YlOrBr']=_YlOrBr_data
datad['YlOrRd']=_YlOrRd_data
datad['gist_earth']=_gist_earth_data
datad['gist_gray']=_gist_gray_data
datad['gist_heat']=_gist_heat_data
datad['gist_ncar']=_gist_ncar_data
datad['gist_rainbow']=_gist_rainbow_data
datad['gist_stern']=_gist_stern_data
datad['gist_yarg']=_gist_yarg_data
# reverse all the colormaps.
# reversed colormaps have '_r' appended to the name.
def revcmap(data):
data_r = {}
for key, val in data.iteritems():
valnew = [(1.-a, b, c) for a, b, c in reversed(val)]
data_r[key] = valnew
return data_r
cmapnames = datad.keys()
for cmapname in cmapnames:
cmapname_r = cmapname+'_r'
cmapdat_r = revcmap(datad[cmapname])
datad[cmapname_r] = cmapdat_r
locals()[cmapname_r] = colors.LinearSegmentedColormap(cmapname_r, cmapdat_r, LUTSIZE)
| agpl-3.0 |
fbagirov/scikit-learn | sklearn/metrics/cluster/unsupervised.py | 230 | 8281 | """ Unsupervised evaluation metrics. """
# Authors: Robert Layton <[email protected]>
#
# License: BSD 3 clause
import numpy as np
from ...utils import check_random_state
from ..pairwise import pairwise_distances
def silhouette_score(X, labels, metric='euclidean', sample_size=None,
random_state=None, **kwds):
"""Compute the mean Silhouette Coefficient of all samples.
The Silhouette Coefficient is calculated using the mean intra-cluster
distance (``a``) and the mean nearest-cluster distance (``b``) for each
sample. The Silhouette Coefficient for a sample is ``(b - a) / max(a,
b)``. To clarify, ``b`` is the distance between a sample and the nearest
cluster that the sample is not a part of.
Note that Silhouette Coefficent is only defined if number of labels
is 2 <= n_labels <= n_samples - 1.
This function returns the mean Silhouette Coefficient over all samples.
To obtain the values for each sample, use :func:`silhouette_samples`.
The best value is 1 and the worst value is -1. Values near 0 indicate
overlapping clusters. Negative values generally indicate that a sample has
been assigned to the wrong cluster, as a different cluster is more similar.
Read more in the :ref:`User Guide <silhouette_coefficient>`.
Parameters
----------
X : array [n_samples_a, n_samples_a] if metric == "precomputed", or, \
[n_samples_a, n_features] otherwise
Array of pairwise distances between samples, or a feature array.
labels : array, shape = [n_samples]
Predicted labels for each sample.
metric : string, or callable
The metric to use when calculating distance between instances in a
feature array. If metric is a string, it must be one of the options
allowed by :func:`metrics.pairwise.pairwise_distances
<sklearn.metrics.pairwise.pairwise_distances>`. If X is the distance
array itself, use ``metric="precomputed"``.
sample_size : int or None
The size of the sample to use when computing the Silhouette Coefficient
on a random subset of the data.
If ``sample_size is None``, no sampling is used.
random_state : integer or numpy.RandomState, optional
The generator used to randomly select a subset of samples if
``sample_size is not None``. If an integer is given, it fixes the seed.
Defaults to the global numpy random number generator.
`**kwds` : optional keyword parameters
Any further parameters are passed directly to the distance function.
If using a scipy.spatial.distance metric, the parameters are still
metric dependent. See the scipy docs for usage examples.
Returns
-------
silhouette : float
Mean Silhouette Coefficient for all samples.
References
----------
.. [1] `Peter J. Rousseeuw (1987). "Silhouettes: a Graphical Aid to the
Interpretation and Validation of Cluster Analysis". Computational
and Applied Mathematics 20: 53-65.
<http://www.sciencedirect.com/science/article/pii/0377042787901257>`_
.. [2] `Wikipedia entry on the Silhouette Coefficient
<http://en.wikipedia.org/wiki/Silhouette_(clustering)>`_
"""
n_labels = len(np.unique(labels))
n_samples = X.shape[0]
if not 1 < n_labels < n_samples:
raise ValueError("Number of labels is %d. Valid values are 2 "
"to n_samples - 1 (inclusive)" % n_labels)
if sample_size is not None:
random_state = check_random_state(random_state)
indices = random_state.permutation(X.shape[0])[:sample_size]
if metric == "precomputed":
X, labels = X[indices].T[indices].T, labels[indices]
else:
X, labels = X[indices], labels[indices]
return np.mean(silhouette_samples(X, labels, metric=metric, **kwds))
def silhouette_samples(X, labels, metric='euclidean', **kwds):
"""Compute the Silhouette Coefficient for each sample.
The Silhouette Coefficient is a measure of how well samples are clustered
with samples that are similar to themselves. Clustering models with a high
Silhouette Coefficient are said to be dense, where samples in the same
cluster are similar to each other, and well separated, where samples in
different clusters are not very similar to each other.
The Silhouette Coefficient is calculated using the mean intra-cluster
distance (``a``) and the mean nearest-cluster distance (``b``) for each
sample. The Silhouette Coefficient for a sample is ``(b - a) / max(a,
b)``.
Note that Silhouette Coefficent is only defined if number of labels
is 2 <= n_labels <= n_samples - 1.
This function returns the Silhouette Coefficient for each sample.
The best value is 1 and the worst value is -1. Values near 0 indicate
overlapping clusters.
Read more in the :ref:`User Guide <silhouette_coefficient>`.
Parameters
----------
X : array [n_samples_a, n_samples_a] if metric == "precomputed", or, \
[n_samples_a, n_features] otherwise
Array of pairwise distances between samples, or a feature array.
labels : array, shape = [n_samples]
label values for each sample
metric : string, or callable
The metric to use when calculating distance between instances in a
feature array. If metric is a string, it must be one of the options
allowed by :func:`sklearn.metrics.pairwise.pairwise_distances`. If X is
the distance array itself, use "precomputed" as the metric.
`**kwds` : optional keyword parameters
Any further parameters are passed directly to the distance function.
If using a ``scipy.spatial.distance`` metric, the parameters are still
metric dependent. See the scipy docs for usage examples.
Returns
-------
silhouette : array, shape = [n_samples]
Silhouette Coefficient for each samples.
References
----------
.. [1] `Peter J. Rousseeuw (1987). "Silhouettes: a Graphical Aid to the
Interpretation and Validation of Cluster Analysis". Computational
and Applied Mathematics 20: 53-65.
<http://www.sciencedirect.com/science/article/pii/0377042787901257>`_
.. [2] `Wikipedia entry on the Silhouette Coefficient
<http://en.wikipedia.org/wiki/Silhouette_(clustering)>`_
"""
distances = pairwise_distances(X, metric=metric, **kwds)
n = labels.shape[0]
A = np.array([_intra_cluster_distance(distances[i], labels, i)
for i in range(n)])
B = np.array([_nearest_cluster_distance(distances[i], labels, i)
for i in range(n)])
sil_samples = (B - A) / np.maximum(A, B)
return sil_samples
def _intra_cluster_distance(distances_row, labels, i):
"""Calculate the mean intra-cluster distance for sample i.
Parameters
----------
distances_row : array, shape = [n_samples]
Pairwise distance matrix between sample i and each sample.
labels : array, shape = [n_samples]
label values for each sample
i : int
Sample index being calculated. It is excluded from calculation and
used to determine the current label
Returns
-------
a : float
Mean intra-cluster distance for sample i
"""
mask = labels == labels[i]
mask[i] = False
if not np.any(mask):
# cluster of size 1
return 0
a = np.mean(distances_row[mask])
return a
def _nearest_cluster_distance(distances_row, labels, i):
"""Calculate the mean nearest-cluster distance for sample i.
Parameters
----------
distances_row : array, shape = [n_samples]
Pairwise distance matrix between sample i and each sample.
labels : array, shape = [n_samples]
label values for each sample
i : int
Sample index being calculated. It is used to determine the current
label.
Returns
-------
b : float
Mean nearest-cluster distance for sample i
"""
label = labels[i]
b = np.min([np.mean(distances_row[labels == cur_label])
for cur_label in set(labels) if not cur_label == label])
return b
| bsd-3-clause |
jshiv/turntable | test/lib/python2.7/site-packages/scipy/signal/windows.py | 7 | 48626 | """The suite of window functions."""
from __future__ import division, print_function, absolute_import
import warnings
import numpy as np
from scipy import special, linalg
from scipy.fftpack import fft
from scipy.lib.six import string_types
__all__ = ['boxcar', 'triang', 'parzen', 'bohman', 'blackman', 'nuttall',
'blackmanharris', 'flattop', 'bartlett', 'hanning', 'barthann',
'hamming', 'kaiser', 'gaussian', 'general_gaussian', 'chebwin',
'slepian', 'cosine', 'hann', 'get_window']
def boxcar(M, sym=True):
"""Return a boxcar or rectangular window.
Included for completeness, this is equivalent to no window at all.
Parameters
----------
M : int
Number of points in the output window. If zero or less, an empty
array is returned.
sym : bool, optional
Whether the window is symmetric. (Has no effect for boxcar.)
Returns
-------
w : ndarray
The window, with the maximum value normalized to 1.
Examples
--------
Plot the window and its frequency response:
>>> from scipy import signal
>>> from scipy.fftpack import fft, fftshift
>>> import matplotlib.pyplot as plt
>>> window = signal.boxcar(51)
>>> plt.plot(window)
>>> plt.title("Boxcar window")
>>> plt.ylabel("Amplitude")
>>> plt.xlabel("Sample")
>>> plt.figure()
>>> A = fft(window, 2048) / (len(window)/2.0)
>>> freq = np.linspace(-0.5, 0.5, len(A))
>>> response = 20 * np.log10(np.abs(fftshift(A / abs(A).max())))
>>> plt.plot(freq, response)
>>> plt.axis([-0.5, 0.5, -120, 0])
>>> plt.title("Frequency response of the boxcar window")
>>> plt.ylabel("Normalized magnitude [dB]")
>>> plt.xlabel("Normalized frequency [cycles per sample]")
"""
return np.ones(M, float)
def triang(M, sym=True):
"""Return a triangular window.
Parameters
----------
M : int
Number of points in the output window. If zero or less, an empty
array is returned.
sym : bool, optional
When True (default), generates a symmetric window, for use in filter
design.
When False, generates a periodic window, for use in spectral analysis.
Returns
-------
w : ndarray
The window, with the maximum value normalized to 1 (though the value 1
does not appear if `M` is even and `sym` is True).
Examples
--------
Plot the window and its frequency response:
>>> from scipy import signal
>>> from scipy.fftpack import fft, fftshift
>>> import matplotlib.pyplot as plt
>>> window = signal.triang(51)
>>> plt.plot(window)
>>> plt.title("Triangular window")
>>> plt.ylabel("Amplitude")
>>> plt.xlabel("Sample")
>>> plt.figure()
>>> A = fft(window, 2048) / (len(window)/2.0)
>>> freq = np.linspace(-0.5, 0.5, len(A))
>>> response = 20 * np.log10(np.abs(fftshift(A / abs(A).max())))
>>> plt.plot(freq, response)
>>> plt.axis([-0.5, 0.5, -120, 0])
>>> plt.title("Frequency response of the triangular window")
>>> plt.ylabel("Normalized magnitude [dB]")
>>> plt.xlabel("Normalized frequency [cycles per sample]")
"""
if M < 1:
return np.array([])
if M == 1:
return np.ones(1, 'd')
odd = M % 2
if not sym and not odd:
M = M + 1
n = np.arange(1, (M + 1) // 2 + 1)
if M % 2 == 0:
w = (2 * n - 1.0) / M
w = np.r_[w, w[::-1]]
else:
w = 2 * n / (M + 1.0)
w = np.r_[w, w[-2::-1]]
if not sym and not odd:
w = w[:-1]
return w
def parzen(M, sym=True):
"""Return a Parzen window.
Parameters
----------
M : int
Number of points in the output window. If zero or less, an empty
array is returned.
sym : bool, optional
When True (default), generates a symmetric window, for use in filter
design.
When False, generates a periodic window, for use in spectral analysis.
Returns
-------
w : ndarray
The window, with the maximum value normalized to 1 (though the value 1
does not appear if `M` is even and `sym` is True).
Examples
--------
Plot the window and its frequency response:
>>> from scipy import signal
>>> from scipy.fftpack import fft, fftshift
>>> import matplotlib.pyplot as plt
>>> window = signal.parzen(51)
>>> plt.plot(window)
>>> plt.title("Parzen window")
>>> plt.ylabel("Amplitude")
>>> plt.xlabel("Sample")
>>> plt.figure()
>>> A = fft(window, 2048) / (len(window)/2.0)
>>> freq = np.linspace(-0.5, 0.5, len(A))
>>> response = 20 * np.log10(np.abs(fftshift(A / abs(A).max())))
>>> plt.plot(freq, response)
>>> plt.axis([-0.5, 0.5, -120, 0])
>>> plt.title("Frequency response of the Parzen window")
>>> plt.ylabel("Normalized magnitude [dB]")
>>> plt.xlabel("Normalized frequency [cycles per sample]")
"""
if M < 1:
return np.array([])
if M == 1:
return np.ones(1, 'd')
odd = M % 2
if not sym and not odd:
M = M + 1
n = np.arange(-(M - 1) / 2.0, (M - 1) / 2.0 + 0.5, 1.0)
na = np.extract(n < -(M - 1) / 4.0, n)
nb = np.extract(abs(n) <= (M - 1) / 4.0, n)
wa = 2 * (1 - np.abs(na) / (M / 2.0)) ** 3.0
wb = (1 - 6 * (np.abs(nb) / (M / 2.0)) ** 2.0 +
6 * (np.abs(nb) / (M / 2.0)) ** 3.0)
w = np.r_[wa, wb, wa[::-1]]
if not sym and not odd:
w = w[:-1]
return w
def bohman(M, sym=True):
"""Return a Bohman window.
Parameters
----------
M : int
Number of points in the output window. If zero or less, an empty
array is returned.
sym : bool, optional
When True (default), generates a symmetric window, for use in filter
design.
When False, generates a periodic window, for use in spectral analysis.
Returns
-------
w : ndarray
The window, with the maximum value normalized to 1 (though the value 1
does not appear if `M` is even and `sym` is True).
Examples
--------
Plot the window and its frequency response:
>>> from scipy import signal
>>> from scipy.fftpack import fft, fftshift
>>> import matplotlib.pyplot as plt
>>> window = signal.bohman(51)
>>> plt.plot(window)
>>> plt.title("Bohman window")
>>> plt.ylabel("Amplitude")
>>> plt.xlabel("Sample")
>>> plt.figure()
>>> A = fft(window, 2048) / (len(window)/2.0)
>>> freq = np.linspace(-0.5, 0.5, len(A))
>>> response = 20 * np.log10(np.abs(fftshift(A / abs(A).max())))
>>> plt.plot(freq, response)
>>> plt.axis([-0.5, 0.5, -120, 0])
>>> plt.title("Frequency response of the Bohman window")
>>> plt.ylabel("Normalized magnitude [dB]")
>>> plt.xlabel("Normalized frequency [cycles per sample]")
"""
if M < 1:
return np.array([])
if M == 1:
return np.ones(1, 'd')
odd = M % 2
if not sym and not odd:
M = M + 1
fac = np.abs(np.linspace(-1, 1, M)[1:-1])
w = (1 - fac) * np.cos(np.pi * fac) + 1.0 / np.pi * np.sin(np.pi * fac)
w = np.r_[0, w, 0]
if not sym and not odd:
w = w[:-1]
return w
def blackman(M, sym=True):
r"""
Return a Blackman window.
The Blackman window is a taper formed by using the first three terms of
a summation of cosines. It was designed to have close to the minimal
leakage possible. It is close to optimal, only slightly worse than a
Kaiser window.
Parameters
----------
M : int
Number of points in the output window. If zero or less, an empty
array is returned.
sym : bool, optional
When True (default), generates a symmetric window, for use in filter
design.
When False, generates a periodic window, for use in spectral analysis.
Returns
-------
w : ndarray
The window, with the maximum value normalized to 1 (though the value 1
does not appear if `M` is even and `sym` is True).
Notes
-----
The Blackman window is defined as
.. math:: w(n) = 0.42 - 0.5 \cos(2\pi n/M) + 0.08 \cos(4\pi n/M)
Most references to the Blackman window come from the signal processing
literature, where it is used as one of many windowing functions for
smoothing values. It is also known as an apodization (which means
"removing the foot", i.e. smoothing discontinuities at the beginning
and end of the sampled signal) or tapering function. It is known as a
"near optimal" tapering function, almost as good (by some measures)
as the Kaiser window.
References
----------
.. [1] Blackman, R.B. and Tukey, J.W., (1958) The measurement of power
spectra, Dover Publications, New York.
.. [2] Oppenheim, A.V., and R.W. Schafer. Discrete-Time Signal Processing.
Upper Saddle River, NJ: Prentice-Hall, 1999, pp. 468-471.
Examples
--------
Plot the window and its frequency response:
>>> from scipy import signal
>>> from scipy.fftpack import fft, fftshift
>>> import matplotlib.pyplot as plt
>>> window = signal.blackman(51)
>>> plt.plot(window)
>>> plt.title("Blackman window")
>>> plt.ylabel("Amplitude")
>>> plt.xlabel("Sample")
>>> plt.figure()
>>> A = fft(window, 2048) / (len(window)/2.0)
>>> freq = np.linspace(-0.5, 0.5, len(A))
>>> response = 20 * np.log10(np.abs(fftshift(A / abs(A).max())))
>>> plt.plot(freq, response)
>>> plt.axis([-0.5, 0.5, -120, 0])
>>> plt.title("Frequency response of the Blackman window")
>>> plt.ylabel("Normalized magnitude [dB]")
>>> plt.xlabel("Normalized frequency [cycles per sample]")
"""
# Docstring adapted from NumPy's blackman function
if M < 1:
return np.array([])
if M == 1:
return np.ones(1, 'd')
odd = M % 2
if not sym and not odd:
M = M + 1
n = np.arange(0, M)
w = (0.42 - 0.5 * np.cos(2.0 * np.pi * n / (M - 1)) +
0.08 * np.cos(4.0 * np.pi * n / (M - 1)))
if not sym and not odd:
w = w[:-1]
return w
def nuttall(M, sym=True):
"""Return a minimum 4-term Blackman-Harris window according to Nuttall.
Parameters
----------
M : int
Number of points in the output window. If zero or less, an empty
array is returned.
sym : bool, optional
When True (default), generates a symmetric window, for use in filter
design.
When False, generates a periodic window, for use in spectral analysis.
Returns
-------
w : ndarray
The window, with the maximum value normalized to 1 (though the value 1
does not appear if `M` is even and `sym` is True).
Examples
--------
Plot the window and its frequency response:
>>> from scipy import signal
>>> from scipy.fftpack import fft, fftshift
>>> import matplotlib.pyplot as plt
>>> window = signal.nuttall(51)
>>> plt.plot(window)
>>> plt.title("Nuttall window")
>>> plt.ylabel("Amplitude")
>>> plt.xlabel("Sample")
>>> plt.figure()
>>> A = fft(window, 2048) / (len(window)/2.0)
>>> freq = np.linspace(-0.5, 0.5, len(A))
>>> response = 20 * np.log10(np.abs(fftshift(A / abs(A).max())))
>>> plt.plot(freq, response)
>>> plt.axis([-0.5, 0.5, -120, 0])
>>> plt.title("Frequency response of the Nuttall window")
>>> plt.ylabel("Normalized magnitude [dB]")
>>> plt.xlabel("Normalized frequency [cycles per sample]")
"""
if M < 1:
return np.array([])
if M == 1:
return np.ones(1, 'd')
odd = M % 2
if not sym and not odd:
M = M + 1
a = [0.3635819, 0.4891775, 0.1365995, 0.0106411]
n = np.arange(0, M)
fac = n * 2 * np.pi / (M - 1.0)
w = (a[0] - a[1] * np.cos(fac) +
a[2] * np.cos(2 * fac) - a[3] * np.cos(3 * fac))
if not sym and not odd:
w = w[:-1]
return w
def blackmanharris(M, sym=True):
"""Return a minimum 4-term Blackman-Harris window.
Parameters
----------
M : int
Number of points in the output window. If zero or less, an empty
array is returned.
sym : bool, optional
When True (default), generates a symmetric window, for use in filter
design.
When False, generates a periodic window, for use in spectral analysis.
Returns
-------
w : ndarray
The window, with the maximum value normalized to 1 (though the value 1
does not appear if `M` is even and `sym` is True).
Examples
--------
Plot the window and its frequency response:
>>> from scipy import signal
>>> from scipy.fftpack import fft, fftshift
>>> import matplotlib.pyplot as plt
>>> window = signal.blackmanharris(51)
>>> plt.plot(window)
>>> plt.title("Blackman-Harris window")
>>> plt.ylabel("Amplitude")
>>> plt.xlabel("Sample")
>>> plt.figure()
>>> A = fft(window, 2048) / (len(window)/2.0)
>>> freq = np.linspace(-0.5, 0.5, len(A))
>>> response = 20 * np.log10(np.abs(fftshift(A / abs(A).max())))
>>> plt.plot(freq, response)
>>> plt.axis([-0.5, 0.5, -120, 0])
>>> plt.title("Frequency response of the Blackman-Harris window")
>>> plt.ylabel("Normalized magnitude [dB]")
>>> plt.xlabel("Normalized frequency [cycles per sample]")
"""
if M < 1:
return np.array([])
if M == 1:
return np.ones(1, 'd')
odd = M % 2
if not sym and not odd:
M = M + 1
a = [0.35875, 0.48829, 0.14128, 0.01168]
n = np.arange(0, M)
fac = n * 2 * np.pi / (M - 1.0)
w = (a[0] - a[1] * np.cos(fac) +
a[2] * np.cos(2 * fac) - a[3] * np.cos(3 * fac))
if not sym and not odd:
w = w[:-1]
return w
def flattop(M, sym=True):
"""Return a flat top window.
Parameters
----------
M : int
Number of points in the output window. If zero or less, an empty
array is returned.
sym : bool, optional
When True (default), generates a symmetric window, for use in filter
design.
When False, generates a periodic window, for use in spectral analysis.
Returns
-------
w : ndarray
The window, with the maximum value normalized to 1 (though the value 1
does not appear if `M` is even and `sym` is True).
Examples
--------
Plot the window and its frequency response:
>>> from scipy import signal
>>> from scipy.fftpack import fft, fftshift
>>> import matplotlib.pyplot as plt
>>> window = signal.flattop(51)
>>> plt.plot(window)
>>> plt.title("Flat top window")
>>> plt.ylabel("Amplitude")
>>> plt.xlabel("Sample")
>>> plt.figure()
>>> A = fft(window, 2048) / (len(window)/2.0)
>>> freq = np.linspace(-0.5, 0.5, len(A))
>>> response = 20 * np.log10(np.abs(fftshift(A / abs(A).max())))
>>> plt.plot(freq, response)
>>> plt.axis([-0.5, 0.5, -120, 0])
>>> plt.title("Frequency response of the flat top window")
>>> plt.ylabel("Normalized magnitude [dB]")
>>> plt.xlabel("Normalized frequency [cycles per sample]")
"""
if M < 1:
return np.array([])
if M == 1:
return np.ones(1, 'd')
odd = M % 2
if not sym and not odd:
M = M + 1
a = [0.2156, 0.4160, 0.2781, 0.0836, 0.0069]
n = np.arange(0, M)
fac = n * 2 * np.pi / (M - 1.0)
w = (a[0] - a[1] * np.cos(fac) +
a[2] * np.cos(2 * fac) - a[3] * np.cos(3 * fac) +
a[4] * np.cos(4 * fac))
if not sym and not odd:
w = w[:-1]
return w
def bartlett(M, sym=True):
r"""
Return a Bartlett window.
The Bartlett window is very similar to a triangular window, except
that the end points are at zero. It is often used in signal
processing for tapering a signal, without generating too much
ripple in the frequency domain.
Parameters
----------
M : int
Number of points in the output window. If zero or less, an empty
array is returned.
sym : bool, optional
When True (default), generates a symmetric window, for use in filter
design.
When False, generates a periodic window, for use in spectral analysis.
Returns
-------
w : ndarray
The triangular window, with the first and last samples equal to zero
and the maximum value normalized to 1 (though the value 1 does not
appear if `M` is even and `sym` is True).
Notes
-----
The Bartlett window is defined as
.. math:: w(n) = \frac{2}{M-1} \left(
\frac{M-1}{2} - \left|n - \frac{M-1}{2}\right|
\right)
Most references to the Bartlett window come from the signal
processing literature, where it is used as one of many windowing
functions for smoothing values. Note that convolution with this
window produces linear interpolation. It is also known as an
apodization (which means"removing the foot", i.e. smoothing
discontinuities at the beginning and end of the sampled signal) or
tapering function. The Fourier transform of the Bartlett is the product
of two sinc functions.
Note the excellent discussion in Kanasewich.
References
----------
.. [1] M.S. Bartlett, "Periodogram Analysis and Continuous Spectra",
Biometrika 37, 1-16, 1950.
.. [2] E.R. Kanasewich, "Time Sequence Analysis in Geophysics",
The University of Alberta Press, 1975, pp. 109-110.
.. [3] A.V. Oppenheim and R.W. Schafer, "Discrete-Time Signal
Processing", Prentice-Hall, 1999, pp. 468-471.
.. [4] Wikipedia, "Window function",
http://en.wikipedia.org/wiki/Window_function
.. [5] W.H. Press, B.P. Flannery, S.A. Teukolsky, and W.T. Vetterling,
"Numerical Recipes", Cambridge University Press, 1986, page 429.
Examples
--------
Plot the window and its frequency response:
>>> from scipy import signal
>>> from scipy.fftpack import fft, fftshift
>>> import matplotlib.pyplot as plt
>>> window = signal.bartlett(51)
>>> plt.plot(window)
>>> plt.title("Bartlett window")
>>> plt.ylabel("Amplitude")
>>> plt.xlabel("Sample")
>>> plt.figure()
>>> A = fft(window, 2048) / (len(window)/2.0)
>>> freq = np.linspace(-0.5, 0.5, len(A))
>>> response = 20 * np.log10(np.abs(fftshift(A / abs(A).max())))
>>> plt.plot(freq, response)
>>> plt.axis([-0.5, 0.5, -120, 0])
>>> plt.title("Frequency response of the Bartlett window")
>>> plt.ylabel("Normalized magnitude [dB]")
>>> plt.xlabel("Normalized frequency [cycles per sample]")
"""
# Docstring adapted from NumPy's bartlett function
if M < 1:
return np.array([])
if M == 1:
return np.ones(1, 'd')
odd = M % 2
if not sym and not odd:
M = M + 1
n = np.arange(0, M)
w = np.where(np.less_equal(n, (M - 1) / 2.0),
2.0 * n / (M - 1), 2.0 - 2.0 * n / (M - 1))
if not sym and not odd:
w = w[:-1]
return w
def hann(M, sym=True):
r"""
Return a Hann window.
The Hann window is a taper formed by using a raised cosine or sine-squared
with ends that touch zero.
Parameters
----------
M : int
Number of points in the output window. If zero or less, an empty
array is returned.
sym : bool, optional
When True (default), generates a symmetric window, for use in filter
design.
When False, generates a periodic window, for use in spectral analysis.
Returns
-------
w : ndarray
The window, with the maximum value normalized to 1 (though the value 1
does not appear if `M` is even and `sym` is True).
Notes
-----
The Hann window is defined as
.. math:: w(n) = 0.5 - 0.5 \cos\left(\frac{2\pi{n}}{M-1}\right)
\qquad 0 \leq n \leq M-1
The window was named for Julius van Hann, an Austrian meteorologist. It is
also known as the Cosine Bell. It is sometimes erroneously referred to as
the "Hanning" window, from the use of "hann" as a verb in the original
paper and confusion with the very similar Hamming window.
Most references to the Hann window come from the signal processing
literature, where it is used as one of many windowing functions for
smoothing values. It is also known as an apodization (which means
"removing the foot", i.e. smoothing discontinuities at the beginning
and end of the sampled signal) or tapering function.
References
----------
.. [1] Blackman, R.B. and Tukey, J.W., (1958) The measurement of power
spectra, Dover Publications, New York.
.. [2] E.R. Kanasewich, "Time Sequence Analysis in Geophysics",
The University of Alberta Press, 1975, pp. 106-108.
.. [3] Wikipedia, "Window function",
http://en.wikipedia.org/wiki/Window_function
.. [4] W.H. Press, B.P. Flannery, S.A. Teukolsky, and W.T. Vetterling,
"Numerical Recipes", Cambridge University Press, 1986, page 425.
Examples
--------
Plot the window and its frequency response:
>>> from scipy import signal
>>> from scipy.fftpack import fft, fftshift
>>> import matplotlib.pyplot as plt
>>> window = signal.hann(51)
>>> plt.plot(window)
>>> plt.title("Hann window")
>>> plt.ylabel("Amplitude")
>>> plt.xlabel("Sample")
>>> plt.figure()
>>> A = fft(window, 2048) / (len(window)/2.0)
>>> freq = np.linspace(-0.5, 0.5, len(A))
>>> response = 20 * np.log10(np.abs(fftshift(A / abs(A).max())))
>>> plt.plot(freq, response)
>>> plt.axis([-0.5, 0.5, -120, 0])
>>> plt.title("Frequency response of the Hann window")
>>> plt.ylabel("Normalized magnitude [dB]")
>>> plt.xlabel("Normalized frequency [cycles per sample]")
"""
# Docstring adapted from NumPy's hanning function
if M < 1:
return np.array([])
if M == 1:
return np.ones(1, 'd')
odd = M % 2
if not sym and not odd:
M = M + 1
n = np.arange(0, M)
w = 0.5 - 0.5 * np.cos(2.0 * np.pi * n / (M - 1))
if not sym and not odd:
w = w[:-1]
return w
hanning = hann
def barthann(M, sym=True):
"""Return a modified Bartlett-Hann window.
Parameters
----------
M : int
Number of points in the output window. If zero or less, an empty
array is returned.
sym : bool, optional
When True (default), generates a symmetric window, for use in filter
design.
When False, generates a periodic window, for use in spectral analysis.
Returns
-------
w : ndarray
The window, with the maximum value normalized to 1 (though the value 1
does not appear if `M` is even and `sym` is True).
Examples
--------
Plot the window and its frequency response:
>>> from scipy import signal
>>> from scipy.fftpack import fft, fftshift
>>> import matplotlib.pyplot as plt
>>> window = signal.barthann(51)
>>> plt.plot(window)
>>> plt.title("Bartlett-Hann window")
>>> plt.ylabel("Amplitude")
>>> plt.xlabel("Sample")
>>> plt.figure()
>>> A = fft(window, 2048) / (len(window)/2.0)
>>> freq = np.linspace(-0.5, 0.5, len(A))
>>> response = 20 * np.log10(np.abs(fftshift(A / abs(A).max())))
>>> plt.plot(freq, response)
>>> plt.axis([-0.5, 0.5, -120, 0])
>>> plt.title("Frequency response of the Bartlett-Hann window")
>>> plt.ylabel("Normalized magnitude [dB]")
>>> plt.xlabel("Normalized frequency [cycles per sample]")
"""
if M < 1:
return np.array([])
if M == 1:
return np.ones(1, 'd')
odd = M % 2
if not sym and not odd:
M = M + 1
n = np.arange(0, M)
fac = np.abs(n / (M - 1.0) - 0.5)
w = 0.62 - 0.48 * fac + 0.38 * np.cos(2 * np.pi * fac)
if not sym and not odd:
w = w[:-1]
return w
def hamming(M, sym=True):
r"""Return a Hamming window.
The Hamming window is a taper formed by using a raised cosine with
non-zero endpoints, optimized to minimize the nearest side lobe.
Parameters
----------
M : int
Number of points in the output window. If zero or less, an empty
array is returned.
sym : bool, optional
When True (default), generates a symmetric window, for use in filter
design.
When False, generates a periodic window, for use in spectral analysis.
Returns
-------
w : ndarray
The window, with the maximum value normalized to 1 (though the value 1
does not appear if `M` is even and `sym` is True).
Notes
-----
The Hamming window is defined as
.. math:: w(n) = 0.54 - 0.46 \cos\left(\frac{2\pi{n}}{M-1}\right)
\qquad 0 \leq n \leq M-1
The Hamming was named for R. W. Hamming, an associate of J. W. Tukey and
is described in Blackman and Tukey. It was recommended for smoothing the
truncated autocovariance function in the time domain.
Most references to the Hamming window come from the signal processing
literature, where it is used as one of many windowing functions for
smoothing values. It is also known as an apodization (which means
"removing the foot", i.e. smoothing discontinuities at the beginning
and end of the sampled signal) or tapering function.
References
----------
.. [1] Blackman, R.B. and Tukey, J.W., (1958) The measurement of power
spectra, Dover Publications, New York.
.. [2] E.R. Kanasewich, "Time Sequence Analysis in Geophysics", The
University of Alberta Press, 1975, pp. 109-110.
.. [3] Wikipedia, "Window function",
http://en.wikipedia.org/wiki/Window_function
.. [4] W.H. Press, B.P. Flannery, S.A. Teukolsky, and W.T. Vetterling,
"Numerical Recipes", Cambridge University Press, 1986, page 425.
Examples
--------
Plot the window and its frequency response:
>>> from scipy import signal
>>> from scipy.fftpack import fft, fftshift
>>> import matplotlib.pyplot as plt
>>> window = signal.hamming(51)
>>> plt.plot(window)
>>> plt.title("Hamming window")
>>> plt.ylabel("Amplitude")
>>> plt.xlabel("Sample")
>>> plt.figure()
>>> A = fft(window, 2048) / (len(window)/2.0)
>>> freq = np.linspace(-0.5, 0.5, len(A))
>>> response = 20 * np.log10(np.abs(fftshift(A / abs(A).max())))
>>> plt.plot(freq, response)
>>> plt.axis([-0.5, 0.5, -120, 0])
>>> plt.title("Frequency response of the Hamming window")
>>> plt.ylabel("Normalized magnitude [dB]")
>>> plt.xlabel("Normalized frequency [cycles per sample]")
"""
# Docstring adapted from NumPy's hamming function
if M < 1:
return np.array([])
if M == 1:
return np.ones(1, 'd')
odd = M % 2
if not sym and not odd:
M = M + 1
n = np.arange(0, M)
w = 0.54 - 0.46 * np.cos(2.0 * np.pi * n / (M - 1))
if not sym and not odd:
w = w[:-1]
return w
def kaiser(M, beta, sym=True):
r"""Return a Kaiser window.
The Kaiser window is a taper formed by using a Bessel function.
Parameters
----------
M : int
Number of points in the output window. If zero or less, an empty
array is returned.
beta : float
Shape parameter, determines trade-off between main-lobe width and
side lobe level. As beta gets large, the window narrows.
sym : bool, optional
When True (default), generates a symmetric window, for use in filter
design.
When False, generates a periodic window, for use in spectral analysis.
Returns
-------
w : ndarray
The window, with the maximum value normalized to 1 (though the value 1
does not appear if `M` is even and `sym` is True).
Notes
-----
The Kaiser window is defined as
.. math:: w(n) = I_0\left( \beta \sqrt{1-\frac{4n^2}{(M-1)^2}}
\right)/I_0(\beta)
with
.. math:: \quad -\frac{M-1}{2} \leq n \leq \frac{M-1}{2},
where :math:`I_0` is the modified zeroth-order Bessel function.
The Kaiser was named for Jim Kaiser, who discovered a simple approximation
to the DPSS window based on Bessel functions.
The Kaiser window is a very good approximation to the Digital Prolate
Spheroidal Sequence, or Slepian window, which is the transform which
maximizes the energy in the main lobe of the window relative to total
energy.
The Kaiser can approximate many other windows by varying the beta
parameter.
==== =======================
beta Window shape
==== =======================
0 Rectangular
5 Similar to a Hamming
6 Similar to a Hann
8.6 Similar to a Blackman
==== =======================
A beta value of 14 is probably a good starting point. Note that as beta
gets large, the window narrows, and so the number of samples needs to be
large enough to sample the increasingly narrow spike, otherwise NaNs will
get returned.
Most references to the Kaiser window come from the signal processing
literature, where it is used as one of many windowing functions for
smoothing values. It is also known as an apodization (which means
"removing the foot", i.e. smoothing discontinuities at the beginning
and end of the sampled signal) or tapering function.
References
----------
.. [1] J. F. Kaiser, "Digital Filters" - Ch 7 in "Systems analysis by
digital computer", Editors: F.F. Kuo and J.F. Kaiser, p 218-285.
John Wiley and Sons, New York, (1966).
.. [2] E.R. Kanasewich, "Time Sequence Analysis in Geophysics", The
University of Alberta Press, 1975, pp. 177-178.
.. [3] Wikipedia, "Window function",
http://en.wikipedia.org/wiki/Window_function
Examples
--------
Plot the window and its frequency response:
>>> from scipy import signal
>>> from scipy.fftpack import fft, fftshift
>>> import matplotlib.pyplot as plt
>>> window = signal.kaiser(51, beta=14)
>>> plt.plot(window)
>>> plt.title(r"Kaiser window ($\beta$=14)")
>>> plt.ylabel("Amplitude")
>>> plt.xlabel("Sample")
>>> plt.figure()
>>> A = fft(window, 2048) / (len(window)/2.0)
>>> freq = np.linspace(-0.5, 0.5, len(A))
>>> response = 20 * np.log10(np.abs(fftshift(A / abs(A).max())))
>>> plt.plot(freq, response)
>>> plt.axis([-0.5, 0.5, -120, 0])
>>> plt.title(r"Frequency response of the Kaiser window ($\beta$=14)")
>>> plt.ylabel("Normalized magnitude [dB]")
>>> plt.xlabel("Normalized frequency [cycles per sample]")
"""
# Docstring adapted from NumPy's kaiser function
if M < 1:
return np.array([])
if M == 1:
return np.ones(1, 'd')
odd = M % 2
if not sym and not odd:
M = M + 1
n = np.arange(0, M)
alpha = (M - 1) / 2.0
w = (special.i0(beta * np.sqrt(1 - ((n - alpha) / alpha) ** 2.0)) /
special.i0(beta))
if not sym and not odd:
w = w[:-1]
return w
def gaussian(M, std, sym=True):
r"""Return a Gaussian window.
Parameters
----------
M : int
Number of points in the output window. If zero or less, an empty
array is returned.
std : float
The standard deviation, sigma.
sym : bool, optional
When True (default), generates a symmetric window, for use in filter
design.
When False, generates a periodic window, for use in spectral analysis.
Returns
-------
w : ndarray
The window, with the maximum value normalized to 1 (though the value 1
does not appear if `M` is even and `sym` is True).
Notes
-----
The Gaussian window is defined as
.. math:: w(n) = e^{ -\frac{1}{2}\left(\frac{n}{\sigma}\right)^2 }
Examples
--------
Plot the window and its frequency response:
>>> from scipy import signal
>>> from scipy.fftpack import fft, fftshift
>>> import matplotlib.pyplot as plt
>>> window = signal.gaussian(51, std=7)
>>> plt.plot(window)
>>> plt.title(r"Gaussian window ($\sigma$=7)")
>>> plt.ylabel("Amplitude")
>>> plt.xlabel("Sample")
>>> plt.figure()
>>> A = fft(window, 2048) / (len(window)/2.0)
>>> freq = np.linspace(-0.5, 0.5, len(A))
>>> response = 20 * np.log10(np.abs(fftshift(A / abs(A).max())))
>>> plt.plot(freq, response)
>>> plt.axis([-0.5, 0.5, -120, 0])
>>> plt.title(r"Frequency response of the Gaussian window ($\sigma$=7)")
>>> plt.ylabel("Normalized magnitude [dB]")
>>> plt.xlabel("Normalized frequency [cycles per sample]")
"""
if M < 1:
return np.array([])
if M == 1:
return np.ones(1, 'd')
odd = M % 2
if not sym and not odd:
M = M + 1
n = np.arange(0, M) - (M - 1.0) / 2.0
sig2 = 2 * std * std
w = np.exp(-n ** 2 / sig2)
if not sym and not odd:
w = w[:-1]
return w
def general_gaussian(M, p, sig, sym=True):
r"""Return a window with a generalized Gaussian shape.
Parameters
----------
M : int
Number of points in the output window. If zero or less, an empty
array is returned.
p : float
Shape parameter. p = 1 is identical to `gaussian`, p = 0.5 is
the same shape as the Laplace distribution.
sig : float
The standard deviation, sigma.
sym : bool, optional
When True (default), generates a symmetric window, for use in filter
design.
When False, generates a periodic window, for use in spectral analysis.
Returns
-------
w : ndarray
The window, with the maximum value normalized to 1 (though the value 1
does not appear if `M` is even and `sym` is True).
Notes
-----
The generalized Gaussian window is defined as
.. math:: w(n) = e^{ -\frac{1}{2}\left|\frac{n}{\sigma}\right|^{2p} }
the half-power point is at
.. math:: (2 \log(2))^{1/(2 p)} \sigma
Examples
--------
Plot the window and its frequency response:
>>> from scipy import signal
>>> from scipy.fftpack import fft, fftshift
>>> import matplotlib.pyplot as plt
>>> window = signal.general_gaussian(51, p=1.5, sig=7)
>>> plt.plot(window)
>>> plt.title(r"Generalized Gaussian window (p=1.5, $\sigma$=7)")
>>> plt.ylabel("Amplitude")
>>> plt.xlabel("Sample")
>>> plt.figure()
>>> A = fft(window, 2048) / (len(window)/2.0)
>>> freq = np.linspace(-0.5, 0.5, len(A))
>>> response = 20 * np.log10(np.abs(fftshift(A / abs(A).max())))
>>> plt.plot(freq, response)
>>> plt.axis([-0.5, 0.5, -120, 0])
>>> plt.title(r"Freq. resp. of the gen. Gaussian window (p=1.5, $\sigma$=7)")
>>> plt.ylabel("Normalized magnitude [dB]")
>>> plt.xlabel("Normalized frequency [cycles per sample]")
"""
if M < 1:
return np.array([])
if M == 1:
return np.ones(1, 'd')
odd = M % 2
if not sym and not odd:
M = M + 1
n = np.arange(0, M) - (M - 1.0) / 2.0
w = np.exp(-0.5 * np.abs(n / sig) ** (2 * p))
if not sym and not odd:
w = w[:-1]
return w
# `chebwin` contributed by Kumar Appaiah.
def chebwin(M, at, sym=True):
r"""Return a Dolph-Chebyshev window.
Parameters
----------
M : int
Number of points in the output window. If zero or less, an empty
array is returned.
at : float
Attenuation (in dB).
sym : bool, optional
When True (default), generates a symmetric window, for use in filter
design.
When False, generates a periodic window, for use in spectral analysis.
Returns
-------
w : ndarray
The window, with the maximum value always normalized to 1
Notes
-----
This window optimizes for the narrowest main lobe width for a given order
`M` and sidelobe equiripple attenuation `at`, using Chebyshev
polynomials. It was originally developed by Dolph to optimize the
directionality of radio antenna arrays.
Unlike most windows, the Dolph-Chebyshev is defined in terms of its
frequency response:
.. math:: W(k) = \frac
{\cos\{M \cos^{-1}[\beta \cos(\frac{\pi k}{M})]\}}
{\cosh[M \cosh^{-1}(\beta)]}
where
.. math:: \beta = \cosh \left [\frac{1}{M}
\cosh^{-1}(10^\frac{A}{20}) \right ]
and 0 <= abs(k) <= M-1. A is the attenuation in decibels (`at`).
The time domain window is then generated using the IFFT, so
power-of-two `M` are the fastest to generate, and prime number `M` are
the slowest.
The equiripple condition in the frequency domain creates impulses in the
time domain, which appear at the ends of the window.
References
----------
.. [1] C. Dolph, "A current distribution for broadside arrays which
optimizes the relationship between beam width and side-lobe level",
Proceedings of the IEEE, Vol. 34, Issue 6
.. [2] Peter Lynch, "The Dolph-Chebyshev Window: A Simple Optimal Filter",
American Meteorological Society (April 1997)
http://mathsci.ucd.ie/~plynch/Publications/Dolph.pdf
.. [3] F. J. Harris, "On the use of windows for harmonic analysis with the
discrete Fourier transforms", Proceedings of the IEEE, Vol. 66,
No. 1, January 1978
Examples
--------
Plot the window and its frequency response:
>>> from scipy import signal
>>> from scipy.fftpack import fft, fftshift
>>> import matplotlib.pyplot as plt
>>> window = signal.chebwin(51, at=100)
>>> plt.plot(window)
>>> plt.title("Dolph-Chebyshev window (100 dB)")
>>> plt.ylabel("Amplitude")
>>> plt.xlabel("Sample")
>>> plt.figure()
>>> A = fft(window, 2048) / (len(window)/2.0)
>>> freq = np.linspace(-0.5, 0.5, len(A))
>>> response = 20 * np.log10(np.abs(fftshift(A / abs(A).max())))
>>> plt.plot(freq, response)
>>> plt.axis([-0.5, 0.5, -120, 0])
>>> plt.title("Frequency response of the Dolph-Chebyshev window (100 dB)")
>>> plt.ylabel("Normalized magnitude [dB]")
>>> plt.xlabel("Normalized frequency [cycles per sample]")
"""
if np.abs(at) < 45:
warnings.warn("This window is not suitable for spectral analysis "
"for attenuation values lower than about 45dB because "
"the equivalent noise bandwidth of a Chebyshev window "
"does not grow monotonically with increasing sidelobe "
"attenuation when the attenuation is smaller than "
"about 45 dB.")
if M < 1:
return np.array([])
if M == 1:
return np.ones(1, 'd')
odd = M % 2
if not sym and not odd:
M = M + 1
# compute the parameter beta
order = M - 1.0
beta = np.cosh(1.0 / order * np.arccosh(10 ** (np.abs(at) / 20.)))
k = np.r_[0:M] * 1.0
x = beta * np.cos(np.pi * k / M)
# Find the window's DFT coefficients
# Use analytic definition of Chebyshev polynomial instead of expansion
# from scipy.special. Using the expansion in scipy.special leads to errors.
p = np.zeros(x.shape)
p[x > 1] = np.cosh(order * np.arccosh(x[x > 1]))
p[x < -1] = (1 - 2 * (order % 2)) * np.cosh(order * np.arccosh(-x[x < -1]))
p[np.abs(x) <= 1] = np.cos(order * np.arccos(x[np.abs(x) <= 1]))
# Appropriate IDFT and filling up
# depending on even/odd M
if M % 2:
w = np.real(fft(p))
n = (M + 1) // 2
w = w[:n]
w = np.concatenate((w[n - 1:0:-1], w))
else:
p = p * np.exp(1.j * np.pi / M * np.r_[0:M])
w = np.real(fft(p))
n = M // 2 + 1
w = np.concatenate((w[n - 1:0:-1], w[1:n]))
w = w / max(w)
if not sym and not odd:
w = w[:-1]
return w
def slepian(M, width, sym=True):
"""Return a digital Slepian (DPSS) window.
Used to maximize the energy concentration in the main lobe. Also called
the digital prolate spheroidal sequence (DPSS).
Parameters
----------
M : int
Number of points in the output window. If zero or less, an empty
array is returned.
width : float
Bandwidth
sym : bool, optional
When True (default), generates a symmetric window, for use in filter
design.
When False, generates a periodic window, for use in spectral analysis.
Returns
-------
w : ndarray
The window, with the maximum value always normalized to 1
Examples
--------
Plot the window and its frequency response:
>>> from scipy import signal
>>> from scipy.fftpack import fft, fftshift
>>> import matplotlib.pyplot as plt
>>> window = signal.slepian(51, width=0.3)
>>> plt.plot(window)
>>> plt.title("Slepian (DPSS) window (BW=0.3)")
>>> plt.ylabel("Amplitude")
>>> plt.xlabel("Sample")
>>> plt.figure()
>>> A = fft(window, 2048) / (len(window)/2.0)
>>> freq = np.linspace(-0.5, 0.5, len(A))
>>> response = 20 * np.log10(np.abs(fftshift(A / abs(A).max())))
>>> plt.plot(freq, response)
>>> plt.axis([-0.5, 0.5, -120, 0])
>>> plt.title("Frequency response of the Slepian window (BW=0.3)")
>>> plt.ylabel("Normalized magnitude [dB]")
>>> plt.xlabel("Normalized frequency [cycles per sample]")
"""
if (M * width > 27.38):
raise ValueError("Cannot reliably obtain Slepian sequences for"
" M*width > 27.38.")
if M < 1:
return np.array([])
if M == 1:
return np.ones(1, 'd')
odd = M % 2
if not sym and not odd:
M = M + 1
twoF = width / 2.0
alpha = (M - 1) / 2.0
m = np.arange(0, M) - alpha
n = m[:, np.newaxis]
k = m[np.newaxis, :]
AF = twoF * special.sinc(twoF * (n - k))
[lam, vec] = linalg.eig(AF)
ind = np.argmax(abs(lam), axis=-1)
w = np.abs(vec[:, ind])
w = w / max(w)
if not sym and not odd:
w = w[:-1]
return w
def cosine(M, sym=True):
"""Return a window with a simple cosine shape.
Parameters
----------
M : int
Number of points in the output window. If zero or less, an empty
array is returned.
sym : bool, optional
When True (default), generates a symmetric window, for use in filter
design.
When False, generates a periodic window, for use in spectral analysis.
Returns
-------
w : ndarray
The window, with the maximum value normalized to 1 (though the value 1
does not appear if `M` is even and `sym` is True).
Notes
-----
.. versionadded:: 0.13.0
Examples
--------
Plot the window and its frequency response:
>>> from scipy import signal
>>> from scipy.fftpack import fft, fftshift
>>> import matplotlib.pyplot as plt
>>> window = signal.cosine(51)
>>> plt.plot(window)
>>> plt.title("Cosine window")
>>> plt.ylabel("Amplitude")
>>> plt.xlabel("Sample")
>>> plt.figure()
>>> A = fft(window, 2048) / (len(window)/2.0)
>>> freq = np.linspace(-0.5, 0.5, len(A))
>>> response = 20 * np.log10(np.abs(fftshift(A / abs(A).max())))
>>> plt.plot(freq, response)
>>> plt.axis([-0.5, 0.5, -120, 0])
>>> plt.title("Frequency response of the cosine window")
>>> plt.ylabel("Normalized magnitude [dB]")
>>> plt.xlabel("Normalized frequency [cycles per sample]")
>>> plt.show()
"""
if M < 1:
return np.array([])
if M == 1:
return np.ones(1, 'd')
odd = M % 2
if not sym and not odd:
M = M + 1
w = np.sin(np.pi / M * (np.arange(0, M) + .5))
if not sym and not odd:
w = w[:-1]
return w
def get_window(window, Nx, fftbins=True):
"""
Return a window.
Parameters
----------
window : string, float, or tuple
The type of window to create. See below for more details.
Nx : int
The number of samples in the window.
fftbins : bool, optional
If True, create a "periodic" window ready to use with ifftshift
and be multiplied by the result of an fft (SEE ALSO fftfreq).
Returns
-------
get_window : ndarray
Returns a window of length `Nx` and type `window`
Notes
-----
Window types:
boxcar, triang, blackman, hamming, hann, bartlett, flattop,
parzen, bohman, blackmanharris, nuttall, barthann,
kaiser (needs beta), gaussian (needs std),
general_gaussian (needs power, width),
slepian (needs width), chebwin (needs attenuation)
If the window requires no parameters, then `window` can be a string.
If the window requires parameters, then `window` must be a tuple
with the first argument the string name of the window, and the next
arguments the needed parameters.
If `window` is a floating point number, it is interpreted as the beta
parameter of the kaiser window.
Each of the window types listed above is also the name of
a function that can be called directly to create a window of
that type.
Examples
--------
>>> from scipy import signal
>>> signal.get_window('triang', 7)
array([ 0.25, 0.5 , 0.75, 1. , 0.75, 0.5 , 0.25])
>>> signal.get_window(('kaiser', 4.0), 9)
array([ 0.08848053, 0.32578323, 0.63343178, 0.89640418, 1. ,
0.89640418, 0.63343178, 0.32578323, 0.08848053])
>>> signal.get_window(4.0, 9)
array([ 0.08848053, 0.32578323, 0.63343178, 0.89640418, 1. ,
0.89640418, 0.63343178, 0.32578323, 0.08848053])
"""
sym = not fftbins
try:
beta = float(window)
except (TypeError, ValueError):
args = ()
if isinstance(window, tuple):
winstr = window[0]
if len(window) > 1:
args = window[1:]
elif isinstance(window, string_types):
if window in ['kaiser', 'ksr', 'gaussian', 'gauss', 'gss',
'general gaussian', 'general_gaussian',
'general gauss', 'general_gauss', 'ggs',
'slepian', 'optimal', 'slep', 'dss',
'chebwin', 'cheb']:
raise ValueError("The '" + window + "' window needs one or "
"more parameters -- pass a tuple.")
else:
winstr = window
else:
raise ValueError("%s as window type is not supported." % str(type(window)))
if winstr in ['blackman', 'black', 'blk']:
winfunc = blackman
elif winstr in ['triangle', 'triang', 'tri']:
winfunc = triang
elif winstr in ['hamming', 'hamm', 'ham']:
winfunc = hamming
elif winstr in ['bartlett', 'bart', 'brt']:
winfunc = bartlett
elif winstr in ['hanning', 'hann', 'han']:
winfunc = hann
elif winstr in ['blackmanharris', 'blackharr', 'bkh']:
winfunc = blackmanharris
elif winstr in ['parzen', 'parz', 'par']:
winfunc = parzen
elif winstr in ['bohman', 'bman', 'bmn']:
winfunc = bohman
elif winstr in ['nuttall', 'nutl', 'nut']:
winfunc = nuttall
elif winstr in ['barthann', 'brthan', 'bth']:
winfunc = barthann
elif winstr in ['flattop', 'flat', 'flt']:
winfunc = flattop
elif winstr in ['kaiser', 'ksr']:
winfunc = kaiser
elif winstr in ['gaussian', 'gauss', 'gss']:
winfunc = gaussian
elif winstr in ['general gaussian', 'general_gaussian',
'general gauss', 'general_gauss', 'ggs']:
winfunc = general_gaussian
elif winstr in ['boxcar', 'box', 'ones', 'rect', 'rectangular']:
winfunc = boxcar
elif winstr in ['slepian', 'slep', 'optimal', 'dpss', 'dss']:
winfunc = slepian
elif winstr in ['cosine', 'halfcosine']:
winfunc = cosine
elif winstr in ['chebwin', 'cheb']:
winfunc = chebwin
else:
raise ValueError("Unknown window type.")
params = (Nx,) + args + (sym,)
else:
winfunc = kaiser
params = (Nx, beta, sym)
return winfunc(*params)
| mit |
mikebenfield/scipy | scipy/cluster/hierarchy.py | 5 | 98379 | """
========================================================
Hierarchical clustering (:mod:`scipy.cluster.hierarchy`)
========================================================
.. currentmodule:: scipy.cluster.hierarchy
These functions cut hierarchical clusterings into flat clusterings
or find the roots of the forest formed by a cut by providing the flat
cluster ids of each observation.
.. autosummary::
:toctree: generated/
fcluster
fclusterdata
leaders
These are routines for agglomerative clustering.
.. autosummary::
:toctree: generated/
linkage
single
complete
average
weighted
centroid
median
ward
These routines compute statistics on hierarchies.
.. autosummary::
:toctree: generated/
cophenet
from_mlab_linkage
inconsistent
maxinconsts
maxdists
maxRstat
to_mlab_linkage
Routines for visualizing flat clusters.
.. autosummary::
:toctree: generated/
dendrogram
These are data structures and routines for representing hierarchies as
tree objects.
.. autosummary::
:toctree: generated/
ClusterNode
leaves_list
to_tree
cut_tree
These are predicates for checking the validity of linkage and
inconsistency matrices as well as for checking isomorphism of two
flat cluster assignments.
.. autosummary::
:toctree: generated/
is_valid_im
is_valid_linkage
is_isomorphic
is_monotonic
correspond
num_obs_linkage
Utility routines for plotting:
.. autosummary::
:toctree: generated/
set_link_color_palette
References
----------
.. [1] "Statistics toolbox." API Reference Documentation. The MathWorks.
http://www.mathworks.com/access/helpdesk/help/toolbox/stats/.
Accessed October 1, 2007.
.. [2] "Hierarchical clustering." API Reference Documentation.
The Wolfram Research, Inc.
http://reference.wolfram.com/mathematica/HierarchicalClustering/tutorial/
HierarchicalClustering.html.
Accessed October 1, 2007.
.. [3] Gower, JC and Ross, GJS. "Minimum Spanning Trees and Single Linkage
Cluster Analysis." Applied Statistics. 18(1): pp. 54--64. 1969.
.. [4] Ward Jr, JH. "Hierarchical grouping to optimize an objective
function." Journal of the American Statistical Association. 58(301):
pp. 236--44. 1963.
.. [5] Johnson, SC. "Hierarchical clustering schemes." Psychometrika.
32(2): pp. 241--54. 1966.
.. [6] Sneath, PH and Sokal, RR. "Numerical taxonomy." Nature. 193: pp.
855--60. 1962.
.. [7] Batagelj, V. "Comparing resemblance measures." Journal of
Classification. 12: pp. 73--90. 1995.
.. [8] Sokal, RR and Michener, CD. "A statistical method for evaluating
systematic relationships." Scientific Bulletins. 38(22):
pp. 1409--38. 1958.
.. [9] Edelbrock, C. "Mixture model tests of hierarchical clustering
algorithms: the problem of classifying everybody." Multivariate
Behavioral Research. 14: pp. 367--84. 1979.
.. [10] Jain, A., and Dubes, R., "Algorithms for Clustering Data."
Prentice-Hall. Englewood Cliffs, NJ. 1988.
.. [11] Fisher, RA "The use of multiple measurements in taxonomic
problems." Annals of Eugenics, 7(2): 179-188. 1936
* MATLAB and MathWorks are registered trademarks of The MathWorks, Inc.
* Mathematica is a registered trademark of The Wolfram Research, Inc.
"""
from __future__ import division, print_function, absolute_import
# Copyright (C) Damian Eads, 2007-2008. New BSD License.
# hierarchy.py (derived from cluster.py, http://scipy-cluster.googlecode.com)
#
# Author: Damian Eads
# Date: September 22, 2007
#
# Copyright (c) 2007, 2008, Damian Eads
#
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions
# are met:
# - Redistributions of source code must retain the above
# copyright notice, this list of conditions and the
# following disclaimer.
# - Redistributions in binary form must reproduce the above copyright
# notice, this list of conditions and the following disclaimer
# in the documentation and/or other materials provided with the
# distribution.
# - Neither the name of the author nor the names of its
# contributors may be used to endorse or promote products derived
# from this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
# A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
# OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
# SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
# LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
# DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
# THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import warnings
import bisect
from collections import deque
import numpy as np
from . import _hierarchy
import scipy.spatial.distance as distance
from scipy._lib.six import string_types
from scipy._lib.six import xrange
_LINKAGE_METHODS = {'single': 0, 'complete': 1, 'average': 2, 'centroid': 3,
'median': 4, 'ward': 5, 'weighted': 6}
_EUCLIDEAN_METHODS = ('centroid', 'median', 'ward')
__all__ = ['ClusterNode', 'average', 'centroid', 'complete', 'cophenet',
'correspond', 'cut_tree', 'dendrogram', 'fcluster', 'fclusterdata',
'from_mlab_linkage', 'inconsistent', 'is_isomorphic',
'is_monotonic', 'is_valid_im', 'is_valid_linkage', 'leaders',
'leaves_list', 'linkage', 'maxRstat', 'maxdists', 'maxinconsts',
'median', 'num_obs_linkage', 'set_link_color_palette', 'single',
'to_mlab_linkage', 'to_tree', 'ward', 'weighted', 'distance']
class ClusterWarning(UserWarning):
pass
def _warning(s):
warnings.warn('scipy.cluster: %s' % s, ClusterWarning, stacklevel=3)
def _copy_array_if_base_present(a):
"""
Copies the array if its base points to a parent array.
"""
if a.base is not None:
return a.copy()
elif np.issubsctype(a, np.float32):
return np.array(a, dtype=np.double)
else:
return a
def _copy_arrays_if_base_present(T):
"""
Accepts a tuple of arrays T. Copies the array T[i] if its base array
points to an actual array. Otherwise, the reference is just copied.
This is useful if the arrays are being passed to a C function that
does not do proper striding.
"""
l = [_copy_array_if_base_present(a) for a in T]
return l
def _randdm(pnts):
""" Generates a random distance matrix stored in condensed form. A
pnts * (pnts - 1) / 2 sized vector is returned.
"""
if pnts >= 2:
D = np.random.rand(pnts * (pnts - 1) / 2)
else:
raise ValueError("The number of points in the distance matrix "
"must be at least 2.")
return D
def single(y):
"""
Performs single/min/nearest linkage on the condensed distance matrix ``y``
Parameters
----------
y : ndarray
The upper triangular of the distance matrix. The result of
``pdist`` is returned in this form.
Returns
-------
Z : ndarray
The linkage matrix.
See Also
--------
linkage: for advanced creation of hierarchical clusterings.
scipy.spatial.distance.pdist : pairwise distance metrics
"""
return linkage(y, method='single', metric='euclidean')
def complete(y):
"""
Performs complete/max/farthest point linkage on a condensed distance matrix
Parameters
----------
y : ndarray
The upper triangular of the distance matrix. The result of
``pdist`` is returned in this form.
Returns
-------
Z : ndarray
A linkage matrix containing the hierarchical clustering. See
the `linkage` function documentation for more information
on its structure.
See Also
--------
linkage: for advanced creation of hierarchical clusterings.
scipy.spatial.distance.pdist : pairwise distance metrics
"""
return linkage(y, method='complete', metric='euclidean')
def average(y):
"""
Performs average/UPGMA linkage on a condensed distance matrix
Parameters
----------
y : ndarray
The upper triangular of the distance matrix. The result of
``pdist`` is returned in this form.
Returns
-------
Z : ndarray
A linkage matrix containing the hierarchical clustering. See
the `linkage` function documentation for more information
on its structure.
See Also
--------
linkage: for advanced creation of hierarchical clusterings.
scipy.spatial.distance.pdist : pairwise distance metrics
"""
return linkage(y, method='average', metric='euclidean')
def weighted(y):
"""
Performs weighted/WPGMA linkage on the condensed distance matrix.
See ``linkage`` for more information on the return
structure and algorithm.
Parameters
----------
y : ndarray
The upper triangular of the distance matrix. The result of
``pdist`` is returned in this form.
Returns
-------
Z : ndarray
A linkage matrix containing the hierarchical clustering. See
the ``linkage`` function documentation for more information
on its structure.
See Also
--------
linkage : for advanced creation of hierarchical clusterings.
scipy.spatial.distance.pdist : pairwise distance metrics
"""
return linkage(y, method='weighted', metric='euclidean')
def centroid(y):
"""
Performs centroid/UPGMC linkage.
See ``linkage`` for more information on the input matrix,
return structure, and algorithm.
The following are common calling conventions:
1. ``Z = centroid(y)``
Performs centroid/UPGMC linkage on the condensed distance
matrix ``y``. See ``linkage`` for more information on the return
structure and algorithm.
2. ``Z = centroid(X)``
Performs centroid/UPGMC linkage on the observation matrix ``X``
using Euclidean distance as the distance metric. See `linkage`
for more information on the return structure and algorithm.
Parameters
----------
y : ndarray
A condensed distance matrix. A condensed
distance matrix is a flat array containing the upper
triangular of the distance matrix. This is the form that
``pdist`` returns. Alternatively, a collection of
m observation vectors in n dimensions may be passed as
a m by n array.
Returns
-------
Z : ndarray
A linkage matrix containing the hierarchical clustering. See
the `linkage` function documentation for more information
on its structure.
See Also
--------
linkage: for advanced creation of hierarchical clusterings.
"""
return linkage(y, method='centroid', metric='euclidean')
def median(y):
"""
Performs median/WPGMC linkage.
See ``linkage`` for more information on the return structure
and algorithm.
The following are common calling conventions:
1. ``Z = median(y)``
Performs median/WPGMC linkage on the condensed distance matrix
``y``. See ``linkage`` for more information on the return
structure and algorithm.
2. ``Z = median(X)``
Performs median/WPGMC linkage on the observation matrix ``X``
using Euclidean distance as the distance metric. See `linkage`
for more information on the return structure and algorithm.
Parameters
----------
y : ndarray
A condensed distance matrix. A condensed
distance matrix is a flat array containing the upper
triangular of the distance matrix. This is the form that
``pdist`` returns. Alternatively, a collection of
m observation vectors in n dimensions may be passed as
a m by n array.
Returns
-------
Z : ndarray
The hierarchical clustering encoded as a linkage matrix.
See Also
--------
linkage: for advanced creation of hierarchical clusterings.
scipy.spatial.distance.pdist : pairwise distance metrics
"""
return linkage(y, method='median', metric='euclidean')
def ward(y):
"""
Performs Ward's linkage on a condensed distance matrix.
See linkage for more information on the return structure
and algorithm.
The following are common calling conventions:
1. ``Z = ward(y)``
Performs Ward's linkage on the condensed distance matrix ``Z``. See
linkage for more information on the return structure and
algorithm.
2. ``Z = ward(X)``
Performs Ward's linkage on the observation matrix ``X`` using
Euclidean distance as the distance metric. See linkage for more
information on the return structure and algorithm.
Parameters
----------
y : ndarray
A condensed distance matrix. A condensed
distance matrix is a flat array containing the upper
triangular of the distance matrix. This is the form that
``pdist`` returns. Alternatively, a collection of
m observation vectors in n dimensions may be passed as
a m by n array.
Returns
-------
Z : ndarray
The hierarchical clustering encoded as a linkage matrix.
See Also
--------
linkage: for advanced creation of hierarchical clusterings.
scipy.spatial.distance.pdist : pairwise distance metrics
"""
return linkage(y, method='ward', metric='euclidean')
def linkage(y, method='single', metric='euclidean'):
"""
Performs hierarchical/agglomerative clustering.
The input y may be either a 1d compressed distance matrix
or a 2d array of observation vectors.
If y is a 1d compressed distance matrix,
then y must be a :math:`{n \\choose 2}` sized
vector where n is the number of original observations paired
in the distance matrix. The behavior of this function is very
similar to the MATLAB linkage function.
A :math:`(n-1)` by 4 matrix ``Z`` is returned. At the
:math:`i`-th iteration, clusters with indices ``Z[i, 0]`` and
``Z[i, 1]`` are combined to form cluster :math:`n + i`. A
cluster with an index less than :math:`n` corresponds to one of
the :math:`n` original observations. The distance between
clusters ``Z[i, 0]`` and ``Z[i, 1]`` is given by ``Z[i, 2]``. The
fourth value ``Z[i, 3]`` represents the number of original
observations in the newly formed cluster.
The following linkage methods are used to compute the distance
:math:`d(s, t)` between two clusters :math:`s` and
:math:`t`. The algorithm begins with a forest of clusters that
have yet to be used in the hierarchy being formed. When two
clusters :math:`s` and :math:`t` from this forest are combined
into a single cluster :math:`u`, :math:`s` and :math:`t` are
removed from the forest, and :math:`u` is added to the
forest. When only one cluster remains in the forest, the algorithm
stops, and this cluster becomes the root.
A distance matrix is maintained at each iteration. The ``d[i,j]``
entry corresponds to the distance between cluster :math:`i` and
:math:`j` in the original forest.
At each iteration, the algorithm must update the distance matrix
to reflect the distance of the newly formed cluster u with the
remaining clusters in the forest.
Suppose there are :math:`|u|` original observations
:math:`u[0], \\ldots, u[|u|-1]` in cluster :math:`u` and
:math:`|v|` original objects :math:`v[0], \\ldots, v[|v|-1]` in
cluster :math:`v`. Recall :math:`s` and :math:`t` are
combined to form cluster :math:`u`. Let :math:`v` be any
remaining cluster in the forest that is not :math:`u`.
The following are methods for calculating the distance between the
newly formed cluster :math:`u` and each :math:`v`.
* method='single' assigns
.. math::
d(u,v) = \\min(dist(u[i],v[j]))
for all points :math:`i` in cluster :math:`u` and
:math:`j` in cluster :math:`v`. This is also known as the
Nearest Point Algorithm.
* method='complete' assigns
.. math::
d(u, v) = \\max(dist(u[i],v[j]))
for all points :math:`i` in cluster u and :math:`j` in
cluster :math:`v`. This is also known by the Farthest Point
Algorithm or Voor Hees Algorithm.
* method='average' assigns
.. math::
d(u,v) = \\sum_{ij} \\frac{d(u[i], v[j])}
{(|u|*|v|)}
for all points :math:`i` and :math:`j` where :math:`|u|`
and :math:`|v|` are the cardinalities of clusters :math:`u`
and :math:`v`, respectively. This is also called the UPGMA
algorithm.
* method='weighted' assigns
.. math::
d(u,v) = (dist(s,v) + dist(t,v))/2
where cluster u was formed with cluster s and t and v
is a remaining cluster in the forest. (also called WPGMA)
* method='centroid' assigns
.. math::
dist(s,t) = ||c_s-c_t||_2
where :math:`c_s` and :math:`c_t` are the centroids of
clusters :math:`s` and :math:`t`, respectively. When two
clusters :math:`s` and :math:`t` are combined into a new
cluster :math:`u`, the new centroid is computed over all the
original objects in clusters :math:`s` and :math:`t`. The
distance then becomes the Euclidean distance between the
centroid of :math:`u` and the centroid of a remaining cluster
:math:`v` in the forest. This is also known as the UPGMC
algorithm.
* method='median' assigns :math:`d(s,t)` like the ``centroid``
method. When two clusters :math:`s` and :math:`t` are combined
into a new cluster :math:`u`, the average of centroids s and t
give the new centroid :math:`u`. This is also known as the
WPGMC algorithm.
* method='ward' uses the Ward variance minimization algorithm.
The new entry :math:`d(u,v)` is computed as follows,
.. math::
d(u,v) = \\sqrt{\\frac{|v|+|s|}
{T}d(v,s)^2
+ \\frac{|v|+|t|}
{T}d(v,t)^2
- \\frac{|v|}
{T}d(s,t)^2}
where :math:`u` is the newly joined cluster consisting of
clusters :math:`s` and :math:`t`, :math:`v` is an unused
cluster in the forest, :math:`T=|v|+|s|+|t|`, and
:math:`|*|` is the cardinality of its argument. This is also
known as the incremental algorithm.
Warning: When the minimum distance pair in the forest is chosen, there
may be two or more pairs with the same minimum distance. This
implementation may chose a different minimum than the MATLAB
version.
Parameters
----------
y : ndarray
A condensed distance matrix. A condensed distance matrix
is a flat array containing the upper triangular of the distance matrix.
This is the form that ``pdist`` returns. Alternatively, a collection of
:math:`m` observation vectors in n dimensions may be passed as an
:math:`m` by :math:`n` array. All elements of `y` must be finite,
i.e. no NaNs or infs.
method : str, optional
The linkage algorithm to use. See the ``Linkage Methods`` section below
for full descriptions.
metric : str or function, optional
The distance metric to use in the case that y is a collection of
observation vectors; ignored otherwise. See the ``pdist``
function for a list of valid distance metrics. A custom distance
function can also be used.
Returns
-------
Z : ndarray
The hierarchical clustering encoded as a linkage matrix.
Notes
-----
1. For method 'single' an optimized algorithm based on minimum spanning
tree is implemented. It has time complexity :math:`O(n^2)`.
For methods 'complete', 'average', 'weighted' and 'ward' an algorithm
called nearest-neighbors chain is implemented. It also has time
complexity :math:`O(n^2)`.
For other methods a naive algorithm is implemented with :math:`O(n^3)`
time complexity.
All algorithms use :math:`O(n^2)` memory.
Refer to [1]_ for details about the algorithms.
2. Methods 'centroid', 'median' and 'ward' are correctly defined only if
Euclidean pairwise metric is used. If `y` is passed as precomputed
pairwise distances, then it is a user responsibility to assure that
these distances are in fact Euclidean, otherwise the produced result
will be incorrect.
See Also
--------
scipy.spatial.distance.pdist : pairwise distance metrics
References
----------
.. [1] Daniel Mullner, "Modern hierarchical, agglomerative clustering
algorithms", :arXiv:`1109.2378v1`.
"""
if method not in _LINKAGE_METHODS:
raise ValueError("Invalid method: {0}".format(method))
y = _convert_to_double(np.asarray(y, order='c'))
if y.ndim == 1:
distance.is_valid_y(y, throw=True, name='y')
[y] = _copy_arrays_if_base_present([y])
elif y.ndim == 2:
if method in _EUCLIDEAN_METHODS and metric != 'euclidean':
raise ValueError("Method '{0}' requires the distance metric "
"to be Euclidean".format(method))
if y.shape[0] == y.shape[1] and np.allclose(np.diag(y), 0):
if np.all(y >= 0) and np.allclose(y, y.T):
_warning('The symmetric non-negative hollow observation '
'matrix looks suspiciously like an uncondensed '
'distance matrix')
y = distance.pdist(y, metric)
else:
raise ValueError("`y` must be 1 or 2 dimensional.")
if not np.all(np.isfinite(y)):
raise ValueError("`y` must contain only finite values.")
n = int(distance.num_obs_y(y))
method_code = _LINKAGE_METHODS[method]
if method == 'single':
return _hierarchy.mst_single_linkage(y, n)
elif method in ['complete', 'average', 'weighted', 'ward']:
return _hierarchy.nn_chain(y, n, method_code)
else:
return _hierarchy.linkage(y, n, method_code)
class ClusterNode:
"""
A tree node class for representing a cluster.
Leaf nodes correspond to original observations, while non-leaf nodes
correspond to non-singleton clusters.
The `to_tree` function converts a matrix returned by the linkage
function into an easy-to-use tree representation.
All parameter names are also attributes.
Parameters
----------
id : int
The node id.
left : ClusterNode instance, optional
The left child tree node.
right : ClusterNode instance, optional
The right child tree node.
dist : float, optional
Distance for this cluster in the linkage matrix.
count : int, optional
The number of samples in this cluster.
See Also
--------
to_tree : for converting a linkage matrix ``Z`` into a tree object.
"""
def __init__(self, id, left=None, right=None, dist=0, count=1):
if id < 0:
raise ValueError('The id must be non-negative.')
if dist < 0:
raise ValueError('The distance must be non-negative.')
if (left is None and right is not None) or \
(left is not None and right is None):
raise ValueError('Only full or proper binary trees are permitted.'
' This node has one child.')
if count < 1:
raise ValueError('A cluster must contain at least one original '
'observation.')
self.id = id
self.left = left
self.right = right
self.dist = dist
if self.left is None:
self.count = count
else:
self.count = left.count + right.count
def __lt__(self, node):
if not isinstance(node, ClusterNode):
raise ValueError("Can't compare ClusterNode "
"to type {}".format(type(node)))
return self.dist < node.dist
def __gt__(self, node):
if not isinstance(node, ClusterNode):
raise ValueError("Can't compare ClusterNode "
"to type {}".format(type(node)))
return self.dist > node.dist
def __eq__(self, node):
if not isinstance(node, ClusterNode):
raise ValueError("Can't compare ClusterNode "
"to type {}".format(type(node)))
return self.dist == node.dist
def get_id(self):
"""
The identifier of the target node.
For ``0 <= i < n``, `i` corresponds to original observation i.
For ``n <= i < 2n-1``, `i` corresponds to non-singleton cluster formed
at iteration ``i-n``.
Returns
-------
id : int
The identifier of the target node.
"""
return self.id
def get_count(self):
"""
The number of leaf nodes (original observations) belonging to
the cluster node nd. If the target node is a leaf, 1 is
returned.
Returns
-------
get_count : int
The number of leaf nodes below the target node.
"""
return self.count
def get_left(self):
"""
Return a reference to the left child tree object.
Returns
-------
left : ClusterNode
The left child of the target node. If the node is a leaf,
None is returned.
"""
return self.left
def get_right(self):
"""
Returns a reference to the right child tree object.
Returns
-------
right : ClusterNode
The left child of the target node. If the node is a leaf,
None is returned.
"""
return self.right
def is_leaf(self):
"""
Returns True if the target node is a leaf.
Returns
-------
leafness : bool
True if the target node is a leaf node.
"""
return self.left is None
def pre_order(self, func=(lambda x: x.id)):
"""
Performs pre-order traversal without recursive function calls.
When a leaf node is first encountered, ``func`` is called with
the leaf node as its argument, and its result is appended to
the list.
For example, the statement::
ids = root.pre_order(lambda x: x.id)
returns a list of the node ids corresponding to the leaf nodes
of the tree as they appear from left to right.
Parameters
----------
func : function
Applied to each leaf ClusterNode object in the pre-order traversal.
Given the ``i``-th leaf node in the pre-order traversal ``n[i]``, the
result of ``func(n[i])`` is stored in ``L[i]``. If not provided,
the index of the original observation to which the node
corresponds is used.
Returns
-------
L : list
The pre-order traversal.
"""
# Do a preorder traversal, caching the result. To avoid having to do
# recursion, we'll store the previous index we've visited in a vector.
n = self.count
curNode = [None] * (2 * n)
lvisited = set()
rvisited = set()
curNode[0] = self
k = 0
preorder = []
while k >= 0:
nd = curNode[k]
ndid = nd.id
if nd.is_leaf():
preorder.append(func(nd))
k = k - 1
else:
if ndid not in lvisited:
curNode[k + 1] = nd.left
lvisited.add(ndid)
k = k + 1
elif ndid not in rvisited:
curNode[k + 1] = nd.right
rvisited.add(ndid)
k = k + 1
# If we've visited the left and right of this non-leaf
# node already, go up in the tree.
else:
k = k - 1
return preorder
_cnode_bare = ClusterNode(0)
_cnode_type = type(ClusterNode)
def _order_cluster_tree(Z):
"""
Returns clustering nodes in bottom-up order by distance.
Parameters
----------
Z : scipy.cluster.linkage array
The linkage matrix.
Returns
-------
nodes : list
A list of ClusterNode objects.
"""
q = deque()
tree = to_tree(Z)
q.append(tree)
nodes = []
while q:
node = q.popleft()
if not node.is_leaf():
bisect.insort_left(nodes, node)
q.append(node.get_right())
q.append(node.get_left())
return nodes
def cut_tree(Z, n_clusters=None, height=None):
"""
Given a linkage matrix Z, return the cut tree.
Parameters
----------
Z : scipy.cluster.linkage array
The linkage matrix.
n_clusters : array_like, optional
Number of clusters in the tree at the cut point.
height : array_like, optional
The height at which to cut the tree. Only possible for ultrametric
trees.
Returns
-------
cutree : array
An array indicating group membership at each agglomeration step. I.e.,
for a full cut tree, in the first column each data point is in its own
cluster. At the next step, two nodes are merged. Finally all singleton
and non-singleton clusters are in one group. If `n_clusters` or
`height` is given, the columns correspond to the columns of `n_clusters` or
`height`.
Examples
--------
>>> from scipy import cluster
>>> np.random.seed(23)
>>> X = np.random.randn(50, 4)
>>> Z = cluster.hierarchy.ward(X)
>>> cutree = cluster.hierarchy.cut_tree(Z, n_clusters=[5, 10])
>>> cutree[:10]
array([[0, 0],
[1, 1],
[2, 2],
[3, 3],
[3, 4],
[2, 2],
[0, 0],
[1, 5],
[3, 6],
[4, 7]])
"""
nobs = num_obs_linkage(Z)
nodes = _order_cluster_tree(Z)
if height is not None and n_clusters is not None:
raise ValueError("At least one of either height or n_clusters "
"must be None")
elif height is None and n_clusters is None: # return the full cut tree
cols_idx = np.arange(nobs)
elif height is not None:
heights = np.array([x.dist for x in nodes])
cols_idx = np.searchsorted(heights, height)
else:
cols_idx = nobs - np.searchsorted(np.arange(nobs), n_clusters)
try:
n_cols = len(cols_idx)
except TypeError: # scalar
n_cols = 1
cols_idx = np.array([cols_idx])
groups = np.zeros((n_cols, nobs), dtype=int)
last_group = np.arange(nobs)
if 0 in cols_idx:
groups[0] = last_group
for i, node in enumerate(nodes):
idx = node.pre_order()
this_group = last_group.copy()
this_group[idx] = last_group[idx].min()
this_group[this_group > last_group[idx].max()] -= 1
if i + 1 in cols_idx:
groups[np.where(i + 1 == cols_idx)[0]] = this_group
last_group = this_group
return groups.T
def to_tree(Z, rd=False):
"""
Converts a linkage matrix into an easy-to-use tree object.
The reference to the root `ClusterNode` object is returned (by default).
Each `ClusterNode` object has a ``left``, ``right``, ``dist``, ``id``,
and ``count`` attribute. The left and right attributes point to
ClusterNode objects that were combined to generate the cluster.
If both are None then the `ClusterNode` object is a leaf node, its count
must be 1, and its distance is meaningless but set to 0.
*Note: This function is provided for the convenience of the library
user. ClusterNodes are not used as input to any of the functions in this
library.*
Parameters
----------
Z : ndarray
The linkage matrix in proper form (see the `linkage`
function documentation).
rd : bool, optional
When False (default), a reference to the root `ClusterNode` object is
returned. Otherwise, a tuple ``(r, d)`` is returned. ``r`` is a
reference to the root node while ``d`` is a list of `ClusterNode`
objects - one per original entry in the linkage matrix plus entries
for all clustering steps. If a cluster id is
less than the number of samples ``n`` in the data that the linkage
matrix describes, then it corresponds to a singleton cluster (leaf
node).
See `linkage` for more information on the assignment of cluster ids
to clusters.
Returns
-------
tree : ClusterNode or tuple (ClusterNode, list of ClusterNode)
If ``rd`` is False, a `ClusterNode`.
If ``rd`` is True, a list of length ``2*n - 1``, with ``n`` the number
of samples. See the description of `rd` above for more details.
See Also
--------
linkage, is_valid_linkage, ClusterNode
Examples
--------
>>> from scipy.cluster import hierarchy
>>> x = np.random.rand(10).reshape(5, 2)
>>> Z = hierarchy.linkage(x)
>>> hierarchy.to_tree(Z)
<scipy.cluster.hierarchy.ClusterNode object at ...
>>> rootnode, nodelist = hierarchy.to_tree(Z, rd=True)
>>> rootnode
<scipy.cluster.hierarchy.ClusterNode object at ...
>>> len(nodelist)
9
"""
Z = np.asarray(Z, order='c')
is_valid_linkage(Z, throw=True, name='Z')
# Number of original objects is equal to the number of rows minus 1.
n = Z.shape[0] + 1
# Create a list full of None's to store the node objects
d = [None] * (n * 2 - 1)
# Create the nodes corresponding to the n original objects.
for i in xrange(0, n):
d[i] = ClusterNode(i)
nd = None
for i in xrange(0, n - 1):
fi = int(Z[i, 0])
fj = int(Z[i, 1])
if fi > i + n:
raise ValueError(('Corrupt matrix Z. Index to derivative cluster '
'is used before it is formed. See row %d, '
'column 0') % fi)
if fj > i + n:
raise ValueError(('Corrupt matrix Z. Index to derivative cluster '
'is used before it is formed. See row %d, '
'column 1') % fj)
nd = ClusterNode(i + n, d[fi], d[fj], Z[i, 2])
# ^ id ^ left ^ right ^ dist
if Z[i, 3] != nd.count:
raise ValueError(('Corrupt matrix Z. The count Z[%d,3] is '
'incorrect.') % i)
d[n + i] = nd
if rd:
return (nd, d)
else:
return nd
def _convert_to_bool(X):
if X.dtype != bool:
X = X.astype(bool)
if not X.flags.contiguous:
X = X.copy()
return X
def _convert_to_double(X):
if X.dtype != np.double:
X = X.astype(np.double)
if not X.flags.contiguous:
X = X.copy()
return X
def cophenet(Z, Y=None):
"""
Calculates the cophenetic distances between each observation in
the hierarchical clustering defined by the linkage ``Z``.
Suppose ``p`` and ``q`` are original observations in
disjoint clusters ``s`` and ``t``, respectively and
``s`` and ``t`` are joined by a direct parent cluster
``u``. The cophenetic distance between observations
``i`` and ``j`` is simply the distance between
clusters ``s`` and ``t``.
Parameters
----------
Z : ndarray
The hierarchical clustering encoded as an array
(see `linkage` function).
Y : ndarray (optional)
Calculates the cophenetic correlation coefficient ``c`` of a
hierarchical clustering defined by the linkage matrix `Z`
of a set of :math:`n` observations in :math:`m`
dimensions. `Y` is the condensed distance matrix from which
`Z` was generated.
Returns
-------
c : ndarray
The cophentic correlation distance (if ``Y`` is passed).
d : ndarray
The cophenetic distance matrix in condensed form. The
:math:`ij` th entry is the cophenetic distance between
original observations :math:`i` and :math:`j`.
"""
Z = np.asarray(Z, order='c')
is_valid_linkage(Z, throw=True, name='Z')
Zs = Z.shape
n = Zs[0] + 1
zz = np.zeros((n * (n-1)) // 2, dtype=np.double)
# Since the C code does not support striding using strides.
# The dimensions are used instead.
Z = _convert_to_double(Z)
_hierarchy.cophenetic_distances(Z, zz, int(n))
if Y is None:
return zz
Y = np.asarray(Y, order='c')
distance.is_valid_y(Y, throw=True, name='Y')
z = zz.mean()
y = Y.mean()
Yy = Y - y
Zz = zz - z
numerator = (Yy * Zz)
denomA = Yy**2
denomB = Zz**2
c = numerator.sum() / np.sqrt((denomA.sum() * denomB.sum()))
return (c, zz)
def inconsistent(Z, d=2):
r"""
Calculates inconsistency statistics on a linkage matrix.
Parameters
----------
Z : ndarray
The :math:`(n-1)` by 4 matrix encoding the linkage (hierarchical
clustering). See `linkage` documentation for more information on its
form.
d : int, optional
The number of links up to `d` levels below each non-singleton cluster.
Returns
-------
R : ndarray
A :math:`(n-1)` by 5 matrix where the ``i``'th row contains the link
statistics for the non-singleton cluster ``i``. The link statistics are
computed over the link heights for links :math:`d` levels below the
cluster ``i``. ``R[i,0]`` and ``R[i,1]`` are the mean and standard
deviation of the link heights, respectively; ``R[i,2]`` is the number
of links included in the calculation; and ``R[i,3]`` is the
inconsistency coefficient,
.. math:: \frac{\mathtt{Z[i,2]} - \mathtt{R[i,0]}} {R[i,1]}
Notes
-----
This function behaves similarly to the MATLAB(TM) ``inconsistent``
function.
"""
Z = np.asarray(Z, order='c')
Zs = Z.shape
is_valid_linkage(Z, throw=True, name='Z')
if (not d == np.floor(d)) or d < 0:
raise ValueError('The second argument d must be a nonnegative '
'integer value.')
# Since the C code does not support striding using strides.
# The dimensions are used instead.
[Z] = _copy_arrays_if_base_present([Z])
n = Zs[0] + 1
R = np.zeros((n - 1, 4), dtype=np.double)
_hierarchy.inconsistent(Z, R, int(n), int(d))
return R
def from_mlab_linkage(Z):
"""
Converts a linkage matrix generated by MATLAB(TM) to a new
linkage matrix compatible with this module.
The conversion does two things:
* the indices are converted from ``1..N`` to ``0..(N-1)`` form,
and
* a fourth column ``Z[:,3]`` is added where ``Z[i,3]`` represents the
number of original observations (leaves) in the non-singleton
cluster ``i``.
This function is useful when loading in linkages from legacy data
files generated by MATLAB.
Parameters
----------
Z : ndarray
A linkage matrix generated by MATLAB(TM).
Returns
-------
ZS : ndarray
A linkage matrix compatible with ``scipy.cluster.hierarchy``.
"""
Z = np.asarray(Z, dtype=np.double, order='c')
Zs = Z.shape
# If it's empty, return it.
if len(Zs) == 0 or (len(Zs) == 1 and Zs[0] == 0):
return Z.copy()
if len(Zs) != 2:
raise ValueError("The linkage array must be rectangular.")
# If it contains no rows, return it.
if Zs[0] == 0:
return Z.copy()
Zpart = Z.copy()
if Zpart[:, 0:2].min() != 1.0 and Zpart[:, 0:2].max() != 2 * Zs[0]:
raise ValueError('The format of the indices is not 1..N')
Zpart[:, 0:2] -= 1.0
CS = np.zeros((Zs[0],), dtype=np.double)
_hierarchy.calculate_cluster_sizes(Zpart, CS, int(Zs[0]) + 1)
return np.hstack([Zpart, CS.reshape(Zs[0], 1)])
def to_mlab_linkage(Z):
"""
Converts a linkage matrix to a MATLAB(TM) compatible one.
Converts a linkage matrix ``Z`` generated by the linkage function
of this module to a MATLAB(TM) compatible one. The return linkage
matrix has the last column removed and the cluster indices are
converted to ``1..N`` indexing.
Parameters
----------
Z : ndarray
A linkage matrix generated by ``scipy.cluster.hierarchy``.
Returns
-------
to_mlab_linkage : ndarray
A linkage matrix compatible with MATLAB(TM)'s hierarchical
clustering functions.
The return linkage matrix has the last column removed
and the cluster indices are converted to ``1..N`` indexing.
"""
Z = np.asarray(Z, order='c', dtype=np.double)
Zs = Z.shape
if len(Zs) == 0 or (len(Zs) == 1 and Zs[0] == 0):
return Z.copy()
is_valid_linkage(Z, throw=True, name='Z')
ZP = Z[:, 0:3].copy()
ZP[:, 0:2] += 1.0
return ZP
def is_monotonic(Z):
"""
Returns True if the linkage passed is monotonic.
The linkage is monotonic if for every cluster :math:`s` and :math:`t`
joined, the distance between them is no less than the distance
between any previously joined clusters.
Parameters
----------
Z : ndarray
The linkage matrix to check for monotonicity.
Returns
-------
b : bool
A boolean indicating whether the linkage is monotonic.
"""
Z = np.asarray(Z, order='c')
is_valid_linkage(Z, throw=True, name='Z')
# We expect the i'th value to be greater than its successor.
return (Z[1:, 2] >= Z[:-1, 2]).all()
def is_valid_im(R, warning=False, throw=False, name=None):
"""Returns True if the inconsistency matrix passed is valid.
It must be a :math:`n` by 4 array of doubles. The standard
deviations ``R[:,1]`` must be nonnegative. The link counts
``R[:,2]`` must be positive and no greater than :math:`n-1`.
Parameters
----------
R : ndarray
The inconsistency matrix to check for validity.
warning : bool, optional
When True, issues a Python warning if the linkage
matrix passed is invalid.
throw : bool, optional
When True, throws a Python exception if the linkage
matrix passed is invalid.
name : str, optional
This string refers to the variable name of the invalid
linkage matrix.
Returns
-------
b : bool
True if the inconsistency matrix is valid.
"""
R = np.asarray(R, order='c')
valid = True
name_str = "%r " % name if name else ''
try:
if type(R) != np.ndarray:
raise TypeError('Variable %spassed as inconsistency matrix is not '
'a numpy array.' % name_str)
if R.dtype != np.double:
raise TypeError('Inconsistency matrix %smust contain doubles '
'(double).' % name_str)
if len(R.shape) != 2:
raise ValueError('Inconsistency matrix %smust have shape=2 (i.e. '
'be two-dimensional).' % name_str)
if R.shape[1] != 4:
raise ValueError('Inconsistency matrix %smust have 4 columns.' %
name_str)
if R.shape[0] < 1:
raise ValueError('Inconsistency matrix %smust have at least one '
'row.' % name_str)
if (R[:, 0] < 0).any():
raise ValueError('Inconsistency matrix %scontains negative link '
'height means.' % name_str)
if (R[:, 1] < 0).any():
raise ValueError('Inconsistency matrix %scontains negative link '
'height standard deviations.' % name_str)
if (R[:, 2] < 0).any():
raise ValueError('Inconsistency matrix %scontains negative link '
'counts.' % name_str)
except Exception as e:
if throw:
raise
if warning:
_warning(str(e))
valid = False
return valid
def is_valid_linkage(Z, warning=False, throw=False, name=None):
"""
Checks the validity of a linkage matrix.
A linkage matrix is valid if it is a two dimensional array (type double)
with :math:`n` rows and 4 columns. The first two columns must contain
indices between 0 and :math:`2n-1`. For a given row ``i``, the following
two expressions have to hold:
.. math::
0 \\leq \\mathtt{Z[i,0]} \\leq i+n-1
0 \\leq Z[i,1] \\leq i+n-1
I.e. a cluster cannot join another cluster unless the cluster being joined
has been generated.
Parameters
----------
Z : array_like
Linkage matrix.
warning : bool, optional
When True, issues a Python warning if the linkage
matrix passed is invalid.
throw : bool, optional
When True, throws a Python exception if the linkage
matrix passed is invalid.
name : str, optional
This string refers to the variable name of the invalid
linkage matrix.
Returns
-------
b : bool
True if the inconsistency matrix is valid.
"""
Z = np.asarray(Z, order='c')
valid = True
name_str = "%r " % name if name else ''
try:
if type(Z) != np.ndarray:
raise TypeError('Passed linkage argument %sis not a valid array.' %
name_str)
if Z.dtype != np.double:
raise TypeError('Linkage matrix %smust contain doubles.' % name_str)
if len(Z.shape) != 2:
raise ValueError('Linkage matrix %smust have shape=2 (i.e. be '
'two-dimensional).' % name_str)
if Z.shape[1] != 4:
raise ValueError('Linkage matrix %smust have 4 columns.' % name_str)
if Z.shape[0] == 0:
raise ValueError('Linkage must be computed on at least two '
'observations.')
n = Z.shape[0]
if n > 1:
if ((Z[:, 0] < 0).any() or (Z[:, 1] < 0).any()):
raise ValueError('Linkage %scontains negative indices.' %
name_str)
if (Z[:, 2] < 0).any():
raise ValueError('Linkage %scontains negative distances.' %
name_str)
if (Z[:, 3] < 0).any():
raise ValueError('Linkage %scontains negative counts.' %
name_str)
if _check_hierarchy_uses_cluster_before_formed(Z):
raise ValueError('Linkage %suses non-singleton cluster before '
'it is formed.' % name_str)
if _check_hierarchy_uses_cluster_more_than_once(Z):
raise ValueError('Linkage %suses the same cluster more than once.'
% name_str)
except Exception as e:
if throw:
raise
if warning:
_warning(str(e))
valid = False
return valid
def _check_hierarchy_uses_cluster_before_formed(Z):
n = Z.shape[0] + 1
for i in xrange(0, n - 1):
if Z[i, 0] >= n + i or Z[i, 1] >= n + i:
return True
return False
def _check_hierarchy_uses_cluster_more_than_once(Z):
n = Z.shape[0] + 1
chosen = set([])
for i in xrange(0, n - 1):
if (Z[i, 0] in chosen) or (Z[i, 1] in chosen) or Z[i, 0] == Z[i, 1]:
return True
chosen.add(Z[i, 0])
chosen.add(Z[i, 1])
return False
def _check_hierarchy_not_all_clusters_used(Z):
n = Z.shape[0] + 1
chosen = set([])
for i in xrange(0, n - 1):
chosen.add(int(Z[i, 0]))
chosen.add(int(Z[i, 1]))
must_chosen = set(range(0, 2 * n - 2))
return len(must_chosen.difference(chosen)) > 0
def num_obs_linkage(Z):
"""
Returns the number of original observations of the linkage matrix
passed.
Parameters
----------
Z : ndarray
The linkage matrix on which to perform the operation.
Returns
-------
n : int
The number of original observations in the linkage.
"""
Z = np.asarray(Z, order='c')
is_valid_linkage(Z, throw=True, name='Z')
return (Z.shape[0] + 1)
def correspond(Z, Y):
"""
Checks for correspondence between linkage and condensed distance matrices
They must have the same number of original observations for
the check to succeed.
This function is useful as a sanity check in algorithms that make
extensive use of linkage and distance matrices that must
correspond to the same set of original observations.
Parameters
----------
Z : array_like
The linkage matrix to check for correspondence.
Y : array_like
The condensed distance matrix to check for correspondence.
Returns
-------
b : bool
A boolean indicating whether the linkage matrix and distance
matrix could possibly correspond to one another.
"""
is_valid_linkage(Z, throw=True)
distance.is_valid_y(Y, throw=True)
Z = np.asarray(Z, order='c')
Y = np.asarray(Y, order='c')
return distance.num_obs_y(Y) == num_obs_linkage(Z)
def fcluster(Z, t, criterion='inconsistent', depth=2, R=None, monocrit=None):
"""
Forms flat clusters from the hierarchical clustering defined by
the given linkage matrix.
Parameters
----------
Z : ndarray
The hierarchical clustering encoded with the matrix returned
by the `linkage` function.
t : float
The threshold to apply when forming flat clusters.
criterion : str, optional
The criterion to use in forming flat clusters. This can
be any of the following values:
``inconsistent`` : If a cluster node and all its
descendants have an inconsistent value less than or equal
to `t` then all its leaf descendants belong to the
same flat cluster. When no non-singleton cluster meets
this criterion, every node is assigned to its own
cluster. (Default)
``distance`` : Forms flat clusters so that the original
observations in each flat cluster have no greater a
cophenetic distance than `t`.
``maxclust`` : Finds a minimum threshold ``r`` so that
the cophenetic distance between any two original
observations in the same flat cluster is no more than
``r`` and no more than `t` flat clusters are formed.
``monocrit`` : Forms a flat cluster from a cluster node c
with index i when ``monocrit[j] <= t``.
For example, to threshold on the maximum mean distance
as computed in the inconsistency matrix R with a
threshold of 0.8 do::
MR = maxRstat(Z, R, 3)
cluster(Z, t=0.8, criterion='monocrit', monocrit=MR)
``maxclust_monocrit`` : Forms a flat cluster from a
non-singleton cluster node ``c`` when ``monocrit[i] <=
r`` for all cluster indices ``i`` below and including
``c``. ``r`` is minimized such that no more than ``t``
flat clusters are formed. monocrit must be
monotonic. For example, to minimize the threshold t on
maximum inconsistency values so that no more than 3 flat
clusters are formed, do::
MI = maxinconsts(Z, R)
cluster(Z, t=3, criterion='maxclust_monocrit', monocrit=MI)
depth : int, optional
The maximum depth to perform the inconsistency calculation.
It has no meaning for the other criteria. Default is 2.
R : ndarray, optional
The inconsistency matrix to use for the 'inconsistent'
criterion. This matrix is computed if not provided.
monocrit : ndarray, optional
An array of length n-1. `monocrit[i]` is the
statistics upon which non-singleton i is thresholded. The
monocrit vector must be monotonic, i.e. given a node c with
index i, for all node indices j corresponding to nodes
below c, ``monocrit[i] >= monocrit[j]``.
Returns
-------
fcluster : ndarray
An array of length ``n``. ``T[i]`` is the flat cluster number to
which original observation ``i`` belongs.
"""
Z = np.asarray(Z, order='c')
is_valid_linkage(Z, throw=True, name='Z')
n = Z.shape[0] + 1
T = np.zeros((n,), dtype='i')
# Since the C code does not support striding using strides.
# The dimensions are used instead.
[Z] = _copy_arrays_if_base_present([Z])
if criterion == 'inconsistent':
if R is None:
R = inconsistent(Z, depth)
else:
R = np.asarray(R, order='c')
is_valid_im(R, throw=True, name='R')
# Since the C code does not support striding using strides.
# The dimensions are used instead.
[R] = _copy_arrays_if_base_present([R])
_hierarchy.cluster_in(Z, R, T, float(t), int(n))
elif criterion == 'distance':
_hierarchy.cluster_dist(Z, T, float(t), int(n))
elif criterion == 'maxclust':
_hierarchy.cluster_maxclust_dist(Z, T, int(n), int(t))
elif criterion == 'monocrit':
[monocrit] = _copy_arrays_if_base_present([monocrit])
_hierarchy.cluster_monocrit(Z, monocrit, T, float(t), int(n))
elif criterion == 'maxclust_monocrit':
[monocrit] = _copy_arrays_if_base_present([monocrit])
_hierarchy.cluster_maxclust_monocrit(Z, monocrit, T, int(n), int(t))
else:
raise ValueError('Invalid cluster formation criterion: %s'
% str(criterion))
return T
def fclusterdata(X, t, criterion='inconsistent',
metric='euclidean', depth=2, method='single', R=None):
"""
Cluster observation data using a given metric.
Clusters the original observations in the n-by-m data
matrix X (n observations in m dimensions), using the euclidean
distance metric to calculate distances between original observations,
performs hierarchical clustering using the single linkage algorithm,
and forms flat clusters using the inconsistency method with `t` as the
cut-off threshold.
A one-dimensional array ``T`` of length ``n`` is returned. ``T[i]`` is
the index of the flat cluster to which the original observation ``i``
belongs.
Parameters
----------
X : (N, M) ndarray
N by M data matrix with N observations in M dimensions.
t : float
The threshold to apply when forming flat clusters.
criterion : str, optional
Specifies the criterion for forming flat clusters. Valid
values are 'inconsistent' (default), 'distance', or 'maxclust'
cluster formation algorithms. See `fcluster` for descriptions.
metric : str, optional
The distance metric for calculating pairwise distances. See
``distance.pdist`` for descriptions and linkage to verify
compatibility with the linkage method.
depth : int, optional
The maximum depth for the inconsistency calculation. See
`inconsistent` for more information.
method : str, optional
The linkage method to use (single, complete, average,
weighted, median centroid, ward). See `linkage` for more
information. Default is "single".
R : ndarray, optional
The inconsistency matrix. It will be computed if necessary
if it is not passed.
Returns
-------
fclusterdata : ndarray
A vector of length n. T[i] is the flat cluster number to
which original observation i belongs.
See Also
--------
scipy.spatial.distance.pdist : pairwise distance metrics
Notes
-----
This function is similar to the MATLAB function ``clusterdata``.
"""
X = np.asarray(X, order='c', dtype=np.double)
if type(X) != np.ndarray or len(X.shape) != 2:
raise TypeError('The observation matrix X must be an n by m numpy '
'array.')
Y = distance.pdist(X, metric=metric)
Z = linkage(Y, method=method)
if R is None:
R = inconsistent(Z, d=depth)
else:
R = np.asarray(R, order='c')
T = fcluster(Z, criterion=criterion, depth=depth, R=R, t=t)
return T
def leaves_list(Z):
"""
Returns a list of leaf node ids
The return corresponds to the observation vector index as it appears
in the tree from left to right. Z is a linkage matrix.
Parameters
----------
Z : ndarray
The hierarchical clustering encoded as a matrix. `Z` is
a linkage matrix. See `linkage` for more information.
Returns
-------
leaves_list : ndarray
The list of leaf node ids.
"""
Z = np.asarray(Z, order='c')
is_valid_linkage(Z, throw=True, name='Z')
n = Z.shape[0] + 1
ML = np.zeros((n,), dtype='i')
[Z] = _copy_arrays_if_base_present([Z])
_hierarchy.prelist(Z, ML, int(n))
return ML
# Maps number of leaves to text size.
#
# p <= 20, size="12"
# 20 < p <= 30, size="10"
# 30 < p <= 50, size="8"
# 50 < p <= np.inf, size="6"
_dtextsizes = {20: 12, 30: 10, 50: 8, 85: 6, np.inf: 5}
_drotation = {20: 0, 40: 45, np.inf: 90}
_dtextsortedkeys = list(_dtextsizes.keys())
_dtextsortedkeys.sort()
_drotationsortedkeys = list(_drotation.keys())
_drotationsortedkeys.sort()
def _remove_dups(L):
"""
Removes duplicates AND preserves the original order of the elements.
The set class is not guaranteed to do this.
"""
seen_before = set([])
L2 = []
for i in L:
if i not in seen_before:
seen_before.add(i)
L2.append(i)
return L2
def _get_tick_text_size(p):
for k in _dtextsortedkeys:
if p <= k:
return _dtextsizes[k]
def _get_tick_rotation(p):
for k in _drotationsortedkeys:
if p <= k:
return _drotation[k]
def _plot_dendrogram(icoords, dcoords, ivl, p, n, mh, orientation,
no_labels, color_list, leaf_font_size=None,
leaf_rotation=None, contraction_marks=None,
ax=None, above_threshold_color='b'):
# Import matplotlib here so that it's not imported unless dendrograms
# are plotted. Raise an informative error if importing fails.
try:
# if an axis is provided, don't use pylab at all
if ax is None:
import matplotlib.pylab
import matplotlib.patches
import matplotlib.collections
except ImportError:
raise ImportError("You must install the matplotlib library to plot "
"the dendrogram. Use no_plot=True to calculate the "
"dendrogram without plotting.")
if ax is None:
ax = matplotlib.pylab.gca()
# if we're using pylab, we want to trigger a draw at the end
trigger_redraw = True
else:
trigger_redraw = False
# Independent variable plot width
ivw = len(ivl) * 10
# Dependent variable plot height
dvw = mh + mh * 0.05
iv_ticks = np.arange(5, len(ivl) * 10 + 5, 10)
if orientation in ('top', 'bottom'):
if orientation == 'top':
ax.set_ylim([0, dvw])
ax.set_xlim([0, ivw])
else:
ax.set_ylim([dvw, 0])
ax.set_xlim([0, ivw])
xlines = icoords
ylines = dcoords
if no_labels:
ax.set_xticks([])
ax.set_xticklabels([])
else:
ax.set_xticks(iv_ticks)
if orientation == 'top':
ax.xaxis.set_ticks_position('bottom')
else:
ax.xaxis.set_ticks_position('top')
# Make the tick marks invisible because they cover up the links
for line in ax.get_xticklines():
line.set_visible(False)
leaf_rot = float(_get_tick_rotation(len(ivl))) if (
leaf_rotation is None) else leaf_rotation
leaf_font = float(_get_tick_text_size(len(ivl))) if (
leaf_font_size is None) else leaf_font_size
ax.set_xticklabels(ivl, rotation=leaf_rot, size=leaf_font)
elif orientation in ('left', 'right'):
if orientation == 'left':
ax.set_xlim([dvw, 0])
ax.set_ylim([0, ivw])
else:
ax.set_xlim([0, dvw])
ax.set_ylim([0, ivw])
xlines = dcoords
ylines = icoords
if no_labels:
ax.set_yticks([])
ax.set_yticklabels([])
else:
ax.set_yticks(iv_ticks)
if orientation == 'left':
ax.yaxis.set_ticks_position('right')
else:
ax.yaxis.set_ticks_position('left')
# Make the tick marks invisible because they cover up the links
for line in ax.get_yticklines():
line.set_visible(False)
leaf_font = float(_get_tick_text_size(len(ivl))) if (
leaf_font_size is None) else leaf_font_size
if leaf_rotation is not None:
ax.set_yticklabels(ivl, rotation=leaf_rotation, size=leaf_font)
else:
ax.set_yticklabels(ivl, size=leaf_font)
# Let's use collections instead. This way there is a separate legend item
# for each tree grouping, rather than stupidly one for each line segment.
colors_used = _remove_dups(color_list)
color_to_lines = {}
for color in colors_used:
color_to_lines[color] = []
for (xline, yline, color) in zip(xlines, ylines, color_list):
color_to_lines[color].append(list(zip(xline, yline)))
colors_to_collections = {}
# Construct the collections.
for color in colors_used:
coll = matplotlib.collections.LineCollection(color_to_lines[color],
colors=(color,))
colors_to_collections[color] = coll
# Add all the groupings below the color threshold.
for color in colors_used:
if color != above_threshold_color:
ax.add_collection(colors_to_collections[color])
# If there's a grouping of links above the color threshold, it goes last.
if above_threshold_color in colors_to_collections:
ax.add_collection(colors_to_collections[above_threshold_color])
if contraction_marks is not None:
Ellipse = matplotlib.patches.Ellipse
for (x, y) in contraction_marks:
if orientation in ('left', 'right'):
e = Ellipse((y, x), width=dvw / 100, height=1.0)
else:
e = Ellipse((x, y), width=1.0, height=dvw / 100)
ax.add_artist(e)
e.set_clip_box(ax.bbox)
e.set_alpha(0.5)
e.set_facecolor('k')
if trigger_redraw:
matplotlib.pylab.draw_if_interactive()
_link_line_colors = ['g', 'r', 'c', 'm', 'y', 'k']
def set_link_color_palette(palette):
"""
Set list of matplotlib color codes for use by dendrogram.
Note that this palette is global (i.e. setting it once changes the colors
for all subsequent calls to `dendrogram`) and that it affects only the
the colors below ``color_threshold``.
Note that `dendrogram` also accepts a custom coloring function through its
``link_color_func`` keyword, which is more flexible and non-global.
Parameters
----------
palette : list of str or None
A list of matplotlib color codes. The order of the color codes is the
order in which the colors are cycled through when color thresholding in
the dendrogram.
If ``None``, resets the palette to its default (which is
``['g', 'r', 'c', 'm', 'y', 'k']``).
Returns
-------
None
See Also
--------
dendrogram
Notes
-----
Ability to reset the palette with ``None`` added in Scipy 0.17.0.
Examples
--------
>>> from scipy.cluster import hierarchy
>>> ytdist = np.array([662., 877., 255., 412., 996., 295., 468., 268., 400.,
... 754., 564., 138., 219., 869., 669.])
>>> Z = hierarchy.linkage(ytdist, 'single')
>>> dn = hierarchy.dendrogram(Z, no_plot=True)
>>> dn['color_list']
['g', 'b', 'b', 'b', 'b']
>>> hierarchy.set_link_color_palette(['c', 'm', 'y', 'k'])
>>> dn = hierarchy.dendrogram(Z, no_plot=True)
>>> dn['color_list']
['c', 'b', 'b', 'b', 'b']
>>> dn = hierarchy.dendrogram(Z, no_plot=True, color_threshold=267,
... above_threshold_color='k')
>>> dn['color_list']
['c', 'm', 'm', 'k', 'k']
Now reset the color palette to its default:
>>> hierarchy.set_link_color_palette(None)
"""
if palette is None:
# reset to its default
palette = ['g', 'r', 'c', 'm', 'y', 'k']
elif type(palette) not in (list, tuple):
raise TypeError("palette must be a list or tuple")
_ptypes = [isinstance(p, string_types) for p in palette]
if False in _ptypes:
raise TypeError("all palette list elements must be color strings")
for i in list(_link_line_colors):
_link_line_colors.remove(i)
_link_line_colors.extend(list(palette))
def dendrogram(Z, p=30, truncate_mode=None, color_threshold=None,
get_leaves=True, orientation='top', labels=None,
count_sort=False, distance_sort=False, show_leaf_counts=True,
no_plot=False, no_labels=False, leaf_font_size=None,
leaf_rotation=None, leaf_label_func=None,
show_contracted=False, link_color_func=None, ax=None,
above_threshold_color='b'):
"""
Plots the hierarchical clustering as a dendrogram.
The dendrogram illustrates how each cluster is
composed by drawing a U-shaped link between a non-singleton
cluster and its children. The height of the top of the U-link is
the distance between its children clusters. It is also the
cophenetic distance between original observations in the two
children clusters. It is expected that the distances in Z[:,2] be
monotonic, otherwise crossings appear in the dendrogram.
Parameters
----------
Z : ndarray
The linkage matrix encoding the hierarchical clustering to
render as a dendrogram. See the ``linkage`` function for more
information on the format of ``Z``.
p : int, optional
The ``p`` parameter for ``truncate_mode``.
truncate_mode : str, optional
The dendrogram can be hard to read when the original
observation matrix from which the linkage is derived is
large. Truncation is used to condense the dendrogram. There
are several modes:
``None/'none'``
No truncation is performed (Default).
``'lastp'``
The last ``p`` non-singleton formed in the linkage are the only
non-leaf nodes in the linkage; they correspond to rows
``Z[n-p-2:end]`` in ``Z``. All other non-singleton clusters are
contracted into leaf nodes.
``'mlab'``
This corresponds to MATLAB(TM) behavior. (not implemented yet)
``'level'/'mtica'``
No more than ``p`` levels of the dendrogram tree are displayed.
This corresponds to Mathematica(TM) behavior.
color_threshold : double, optional
For brevity, let :math:`t` be the ``color_threshold``.
Colors all the descendent links below a cluster node
:math:`k` the same color if :math:`k` is the first node below
the cut threshold :math:`t`. All links connecting nodes with
distances greater than or equal to the threshold are colored
blue. If :math:`t` is less than or equal to zero, all nodes
are colored blue. If ``color_threshold`` is None or
'default', corresponding with MATLAB(TM) behavior, the
threshold is set to ``0.7*max(Z[:,2])``.
get_leaves : bool, optional
Includes a list ``R['leaves']=H`` in the result
dictionary. For each :math:`i`, ``H[i] == j``, cluster node
``j`` appears in position ``i`` in the left-to-right traversal
of the leaves, where :math:`j < 2n-1` and :math:`i < n`.
orientation : str, optional
The direction to plot the dendrogram, which can be any
of the following strings:
``'top'``
Plots the root at the top, and plot descendent links going downwards.
(default).
``'bottom'``
Plots the root at the bottom, and plot descendent links going
upwards.
``'left'``
Plots the root at the left, and plot descendent links going right.
``'right'``
Plots the root at the right, and plot descendent links going left.
labels : ndarray, optional
By default ``labels`` is None so the index of the original observation
is used to label the leaf nodes. Otherwise, this is an :math:`n`
-sized list (or tuple). The ``labels[i]`` value is the text to put
under the :math:`i` th leaf node only if it corresponds to an original
observation and not a non-singleton cluster.
count_sort : str or bool, optional
For each node n, the order (visually, from left-to-right) n's
two descendent links are plotted is determined by this
parameter, which can be any of the following values:
``False``
Nothing is done.
``'ascending'`` or ``True``
The child with the minimum number of original objects in its cluster
is plotted first.
``'descendent'``
The child with the maximum number of original objects in its cluster
is plotted first.
Note ``distance_sort`` and ``count_sort`` cannot both be True.
distance_sort : str or bool, optional
For each node n, the order (visually, from left-to-right) n's
two descendent links are plotted is determined by this
parameter, which can be any of the following values:
``False``
Nothing is done.
``'ascending'`` or ``True``
The child with the minimum distance between its direct descendents is
plotted first.
``'descending'``
The child with the maximum distance between its direct descendents is
plotted first.
Note ``distance_sort`` and ``count_sort`` cannot both be True.
show_leaf_counts : bool, optional
When True, leaf nodes representing :math:`k>1` original
observation are labeled with the number of observations they
contain in parentheses.
no_plot : bool, optional
When True, the final rendering is not performed. This is
useful if only the data structures computed for the rendering
are needed or if matplotlib is not available.
no_labels : bool, optional
When True, no labels appear next to the leaf nodes in the
rendering of the dendrogram.
leaf_rotation : double, optional
Specifies the angle (in degrees) to rotate the leaf
labels. When unspecified, the rotation is based on the number of
nodes in the dendrogram (default is 0).
leaf_font_size : int, optional
Specifies the font size (in points) of the leaf labels. When
unspecified, the size based on the number of nodes in the
dendrogram.
leaf_label_func : lambda or function, optional
When leaf_label_func is a callable function, for each
leaf with cluster index :math:`k < 2n-1`. The function
is expected to return a string with the label for the
leaf.
Indices :math:`k < n` correspond to original observations
while indices :math:`k \\geq n` correspond to non-singleton
clusters.
For example, to label singletons with their node id and
non-singletons with their id, count, and inconsistency
coefficient, simply do::
# First define the leaf label function.
def llf(id):
if id < n:
return str(id)
else:
return '[%d %d %1.2f]' % (id, count, R[n-id,3])
# The text for the leaf nodes is going to be big so force
# a rotation of 90 degrees.
dendrogram(Z, leaf_label_func=llf, leaf_rotation=90)
show_contracted : bool, optional
When True the heights of non-singleton nodes contracted
into a leaf node are plotted as crosses along the link
connecting that leaf node. This really is only useful when
truncation is used (see ``truncate_mode`` parameter).
link_color_func : callable, optional
If given, `link_color_function` is called with each non-singleton id
corresponding to each U-shaped link it will paint. The function is
expected to return the color to paint the link, encoded as a matplotlib
color string code. For example::
dendrogram(Z, link_color_func=lambda k: colors[k])
colors the direct links below each untruncated non-singleton node
``k`` using ``colors[k]``.
ax : matplotlib Axes instance, optional
If None and `no_plot` is not True, the dendrogram will be plotted
on the current axes. Otherwise if `no_plot` is not True the
dendrogram will be plotted on the given ``Axes`` instance. This can be
useful if the dendrogram is part of a more complex figure.
above_threshold_color : str, optional
This matplotlib color string sets the color of the links above the
color_threshold. The default is 'b'.
Returns
-------
R : dict
A dictionary of data structures computed to render the
dendrogram. Its has the following keys:
``'color_list'``
A list of color names. The k'th element represents the color of the
k'th link.
``'icoord'`` and ``'dcoord'``
Each of them is a list of lists. Let ``icoord = [I1, I2, ..., Ip]``
where ``Ik = [xk1, xk2, xk3, xk4]`` and ``dcoord = [D1, D2, ..., Dp]``
where ``Dk = [yk1, yk2, yk3, yk4]``, then the k'th link painted is
``(xk1, yk1)`` - ``(xk2, yk2)`` - ``(xk3, yk3)`` - ``(xk4, yk4)``.
``'ivl'``
A list of labels corresponding to the leaf nodes.
``'leaves'``
For each i, ``H[i] == j``, cluster node ``j`` appears in position
``i`` in the left-to-right traversal of the leaves, where
:math:`j < 2n-1` and :math:`i < n`. If ``j`` is less than ``n``, the
``i``-th leaf node corresponds to an original observation.
Otherwise, it corresponds to a non-singleton cluster.
See Also
--------
linkage, set_link_color_palette
Examples
--------
>>> from scipy.cluster import hierarchy
>>> import matplotlib.pyplot as plt
A very basic example:
>>> ytdist = np.array([662., 877., 255., 412., 996., 295., 468., 268.,
... 400., 754., 564., 138., 219., 869., 669.])
>>> Z = hierarchy.linkage(ytdist, 'single')
>>> plt.figure()
>>> dn = hierarchy.dendrogram(Z)
Now plot in given axes, improve the color scheme and use both vertical and
horizontal orientations:
>>> hierarchy.set_link_color_palette(['m', 'c', 'y', 'k'])
>>> fig, axes = plt.subplots(1, 2, figsize=(8, 3))
>>> dn1 = hierarchy.dendrogram(Z, ax=axes[0], above_threshold_color='y',
... orientation='top')
>>> dn2 = hierarchy.dendrogram(Z, ax=axes[1], above_threshold_color='#bcbddc',
... orientation='right')
>>> hierarchy.set_link_color_palette(None) # reset to default after use
>>> plt.show()
"""
# This feature was thought about but never implemented (still useful?):
#
# ... = dendrogram(..., leaves_order=None)
#
# Plots the leaves in the order specified by a vector of
# original observation indices. If the vector contains duplicates
# or results in a crossing, an exception will be thrown. Passing
# None orders leaf nodes based on the order they appear in the
# pre-order traversal.
Z = np.asarray(Z, order='c')
if orientation not in ["top", "left", "bottom", "right"]:
raise ValueError("orientation must be one of 'top', 'left', "
"'bottom', or 'right'")
is_valid_linkage(Z, throw=True, name='Z')
Zs = Z.shape
n = Zs[0] + 1
if type(p) in (int, float):
p = int(p)
else:
raise TypeError('The second argument must be a number')
if truncate_mode not in ('lastp', 'mlab', 'mtica', 'level', 'none', None):
raise ValueError('Invalid truncation mode.')
if truncate_mode == 'lastp' or truncate_mode == 'mlab':
if p > n or p == 0:
p = n
if truncate_mode == 'mtica' or truncate_mode == 'level':
if p <= 0:
p = np.inf
if get_leaves:
lvs = []
else:
lvs = None
icoord_list = []
dcoord_list = []
color_list = []
current_color = [0]
currently_below_threshold = [False]
ivl = [] # list of leaves
if color_threshold is None or (isinstance(color_threshold, string_types) and
color_threshold == 'default'):
color_threshold = max(Z[:, 2]) * 0.7
R = {'icoord': icoord_list, 'dcoord': dcoord_list, 'ivl': ivl,
'leaves': lvs, 'color_list': color_list}
# Empty list will be filled in _dendrogram_calculate_info
contraction_marks = [] if show_contracted else None
_dendrogram_calculate_info(
Z=Z, p=p,
truncate_mode=truncate_mode,
color_threshold=color_threshold,
get_leaves=get_leaves,
orientation=orientation,
labels=labels,
count_sort=count_sort,
distance_sort=distance_sort,
show_leaf_counts=show_leaf_counts,
i=2*n - 2,
iv=0.0,
ivl=ivl,
n=n,
icoord_list=icoord_list,
dcoord_list=dcoord_list,
lvs=lvs,
current_color=current_color,
color_list=color_list,
currently_below_threshold=currently_below_threshold,
leaf_label_func=leaf_label_func,
contraction_marks=contraction_marks,
link_color_func=link_color_func,
above_threshold_color=above_threshold_color)
if not no_plot:
mh = max(Z[:, 2])
_plot_dendrogram(icoord_list, dcoord_list, ivl, p, n, mh, orientation,
no_labels, color_list,
leaf_font_size=leaf_font_size,
leaf_rotation=leaf_rotation,
contraction_marks=contraction_marks,
ax=ax,
above_threshold_color=above_threshold_color)
return R
def _append_singleton_leaf_node(Z, p, n, level, lvs, ivl, leaf_label_func,
i, labels):
# If the leaf id structure is not None and is a list then the caller
# to dendrogram has indicated that cluster id's corresponding to the
# leaf nodes should be recorded.
if lvs is not None:
lvs.append(int(i))
# If leaf node labels are to be displayed...
if ivl is not None:
# If a leaf_label_func has been provided, the label comes from the
# string returned from the leaf_label_func, which is a function
# passed to dendrogram.
if leaf_label_func:
ivl.append(leaf_label_func(int(i)))
else:
# Otherwise, if the dendrogram caller has passed a labels list
# for the leaf nodes, use it.
if labels is not None:
ivl.append(labels[int(i - n)])
else:
# Otherwise, use the id as the label for the leaf.x
ivl.append(str(int(i)))
def _append_nonsingleton_leaf_node(Z, p, n, level, lvs, ivl, leaf_label_func,
i, labels, show_leaf_counts):
# If the leaf id structure is not None and is a list then the caller
# to dendrogram has indicated that cluster id's corresponding to the
# leaf nodes should be recorded.
if lvs is not None:
lvs.append(int(i))
if ivl is not None:
if leaf_label_func:
ivl.append(leaf_label_func(int(i)))
else:
if show_leaf_counts:
ivl.append("(" + str(int(Z[i - n, 3])) + ")")
else:
ivl.append("")
def _append_contraction_marks(Z, iv, i, n, contraction_marks):
_append_contraction_marks_sub(Z, iv, int(Z[i - n, 0]), n, contraction_marks)
_append_contraction_marks_sub(Z, iv, int(Z[i - n, 1]), n, contraction_marks)
def _append_contraction_marks_sub(Z, iv, i, n, contraction_marks):
if i >= n:
contraction_marks.append((iv, Z[i - n, 2]))
_append_contraction_marks_sub(Z, iv, int(Z[i - n, 0]), n, contraction_marks)
_append_contraction_marks_sub(Z, iv, int(Z[i - n, 1]), n, contraction_marks)
def _dendrogram_calculate_info(Z, p, truncate_mode,
color_threshold=np.inf, get_leaves=True,
orientation='top', labels=None,
count_sort=False, distance_sort=False,
show_leaf_counts=False, i=-1, iv=0.0,
ivl=[], n=0, icoord_list=[], dcoord_list=[],
lvs=None, mhr=False,
current_color=[], color_list=[],
currently_below_threshold=[],
leaf_label_func=None, level=0,
contraction_marks=None,
link_color_func=None,
above_threshold_color='b'):
"""
Calculates the endpoints of the links as well as the labels for the
the dendrogram rooted at the node with index i. iv is the independent
variable value to plot the left-most leaf node below the root node i
(if orientation='top', this would be the left-most x value where the
plotting of this root node i and its descendents should begin).
ivl is a list to store the labels of the leaf nodes. The leaf_label_func
is called whenever ivl != None, labels == None, and
leaf_label_func != None. When ivl != None and labels != None, the
labels list is used only for labeling the leaf nodes. When
ivl == None, no labels are generated for leaf nodes.
When get_leaves==True, a list of leaves is built as they are visited
in the dendrogram.
Returns a tuple with l being the independent variable coordinate that
corresponds to the midpoint of cluster to the left of cluster i if
i is non-singleton, otherwise the independent coordinate of the leaf
node if i is a leaf node.
Returns
-------
A tuple (left, w, h, md), where:
* left is the independent variable coordinate of the center of the
the U of the subtree
* w is the amount of space used for the subtree (in independent
variable units)
* h is the height of the subtree in dependent variable units
* md is the ``max(Z[*,2]``) for all nodes ``*`` below and including
the target node.
"""
if n == 0:
raise ValueError("Invalid singleton cluster count n.")
if i == -1:
raise ValueError("Invalid root cluster index i.")
if truncate_mode == 'lastp':
# If the node is a leaf node but corresponds to a non-single cluster,
# its label is either the empty string or the number of original
# observations belonging to cluster i.
if 2 * n - p > i >= n:
d = Z[i - n, 2]
_append_nonsingleton_leaf_node(Z, p, n, level, lvs, ivl,
leaf_label_func, i, labels,
show_leaf_counts)
if contraction_marks is not None:
_append_contraction_marks(Z, iv + 5.0, i, n, contraction_marks)
return (iv + 5.0, 10.0, 0.0, d)
elif i < n:
_append_singleton_leaf_node(Z, p, n, level, lvs, ivl,
leaf_label_func, i, labels)
return (iv + 5.0, 10.0, 0.0, 0.0)
elif truncate_mode in ('mtica', 'level'):
if i > n and level > p:
d = Z[i - n, 2]
_append_nonsingleton_leaf_node(Z, p, n, level, lvs, ivl,
leaf_label_func, i, labels,
show_leaf_counts)
if contraction_marks is not None:
_append_contraction_marks(Z, iv + 5.0, i, n, contraction_marks)
return (iv + 5.0, 10.0, 0.0, d)
elif i < n:
_append_singleton_leaf_node(Z, p, n, level, lvs, ivl,
leaf_label_func, i, labels)
return (iv + 5.0, 10.0, 0.0, 0.0)
elif truncate_mode in ('mlab',):
pass
# Otherwise, only truncate if we have a leaf node.
#
# If the truncate_mode is mlab, the linkage has been modified
# with the truncated tree.
#
# Only place leaves if they correspond to original observations.
if i < n:
_append_singleton_leaf_node(Z, p, n, level, lvs, ivl,
leaf_label_func, i, labels)
return (iv + 5.0, 10.0, 0.0, 0.0)
# !!! Otherwise, we don't have a leaf node, so work on plotting a
# non-leaf node.
# Actual indices of a and b
aa = int(Z[i - n, 0])
ab = int(Z[i - n, 1])
if aa > n:
# The number of singletons below cluster a
na = Z[aa - n, 3]
# The distance between a's two direct children.
da = Z[aa - n, 2]
else:
na = 1
da = 0.0
if ab > n:
nb = Z[ab - n, 3]
db = Z[ab - n, 2]
else:
nb = 1
db = 0.0
if count_sort == 'ascending' or count_sort == True:
# If a has a count greater than b, it and its descendents should
# be drawn to the right. Otherwise, to the left.
if na > nb:
# The cluster index to draw to the left (ua) will be ab
# and the one to draw to the right (ub) will be aa
ua = ab
ub = aa
else:
ua = aa
ub = ab
elif count_sort == 'descending':
# If a has a count less than or equal to b, it and its
# descendents should be drawn to the left. Otherwise, to
# the right.
if na > nb:
ua = aa
ub = ab
else:
ua = ab
ub = aa
elif distance_sort == 'ascending' or distance_sort == True:
# If a has a distance greater than b, it and its descendents should
# be drawn to the right. Otherwise, to the left.
if da > db:
ua = ab
ub = aa
else:
ua = aa
ub = ab
elif distance_sort == 'descending':
# If a has a distance less than or equal to b, it and its
# descendents should be drawn to the left. Otherwise, to
# the right.
if da > db:
ua = aa
ub = ab
else:
ua = ab
ub = aa
else:
ua = aa
ub = ab
# Updated iv variable and the amount of space used.
(uiva, uwa, uah, uamd) = \
_dendrogram_calculate_info(
Z=Z, p=p,
truncate_mode=truncate_mode,
color_threshold=color_threshold,
get_leaves=get_leaves,
orientation=orientation,
labels=labels,
count_sort=count_sort,
distance_sort=distance_sort,
show_leaf_counts=show_leaf_counts,
i=ua, iv=iv, ivl=ivl, n=n,
icoord_list=icoord_list,
dcoord_list=dcoord_list, lvs=lvs,
current_color=current_color,
color_list=color_list,
currently_below_threshold=currently_below_threshold,
leaf_label_func=leaf_label_func,
level=level + 1, contraction_marks=contraction_marks,
link_color_func=link_color_func,
above_threshold_color=above_threshold_color)
h = Z[i - n, 2]
if h >= color_threshold or color_threshold <= 0:
c = above_threshold_color
if currently_below_threshold[0]:
current_color[0] = (current_color[0] + 1) % len(_link_line_colors)
currently_below_threshold[0] = False
else:
currently_below_threshold[0] = True
c = _link_line_colors[current_color[0]]
(uivb, uwb, ubh, ubmd) = \
_dendrogram_calculate_info(
Z=Z, p=p,
truncate_mode=truncate_mode,
color_threshold=color_threshold,
get_leaves=get_leaves,
orientation=orientation,
labels=labels,
count_sort=count_sort,
distance_sort=distance_sort,
show_leaf_counts=show_leaf_counts,
i=ub, iv=iv + uwa, ivl=ivl, n=n,
icoord_list=icoord_list,
dcoord_list=dcoord_list, lvs=lvs,
current_color=current_color,
color_list=color_list,
currently_below_threshold=currently_below_threshold,
leaf_label_func=leaf_label_func,
level=level + 1, contraction_marks=contraction_marks,
link_color_func=link_color_func,
above_threshold_color=above_threshold_color)
max_dist = max(uamd, ubmd, h)
icoord_list.append([uiva, uiva, uivb, uivb])
dcoord_list.append([uah, h, h, ubh])
if link_color_func is not None:
v = link_color_func(int(i))
if not isinstance(v, string_types):
raise TypeError("link_color_func must return a matplotlib "
"color string!")
color_list.append(v)
else:
color_list.append(c)
return (((uiva + uivb) / 2), uwa + uwb, h, max_dist)
def is_isomorphic(T1, T2):
"""
Determines if two different cluster assignments are equivalent.
Parameters
----------
T1 : array_like
An assignment of singleton cluster ids to flat cluster ids.
T2 : array_like
An assignment of singleton cluster ids to flat cluster ids.
Returns
-------
b : bool
Whether the flat cluster assignments `T1` and `T2` are
equivalent.
"""
T1 = np.asarray(T1, order='c')
T2 = np.asarray(T2, order='c')
if type(T1) != np.ndarray:
raise TypeError('T1 must be a numpy array.')
if type(T2) != np.ndarray:
raise TypeError('T2 must be a numpy array.')
T1S = T1.shape
T2S = T2.shape
if len(T1S) != 1:
raise ValueError('T1 must be one-dimensional.')
if len(T2S) != 1:
raise ValueError('T2 must be one-dimensional.')
if T1S[0] != T2S[0]:
raise ValueError('T1 and T2 must have the same number of elements.')
n = T1S[0]
d = {}
for i in xrange(0, n):
if T1[i] in d:
if d[T1[i]] != T2[i]:
return False
else:
d[T1[i]] = T2[i]
return True
def maxdists(Z):
"""
Returns the maximum distance between any non-singleton cluster.
Parameters
----------
Z : ndarray
The hierarchical clustering encoded as a matrix. See
``linkage`` for more information.
Returns
-------
maxdists : ndarray
A ``(n-1)`` sized numpy array of doubles; ``MD[i]`` represents
the maximum distance between any cluster (including
singletons) below and including the node with index i. More
specifically, ``MD[i] = Z[Q(i)-n, 2].max()`` where ``Q(i)`` is the
set of all node indices below and including node i.
"""
Z = np.asarray(Z, order='c', dtype=np.double)
is_valid_linkage(Z, throw=True, name='Z')
n = Z.shape[0] + 1
MD = np.zeros((n - 1,))
[Z] = _copy_arrays_if_base_present([Z])
_hierarchy.get_max_dist_for_each_cluster(Z, MD, int(n))
return MD
def maxinconsts(Z, R):
"""
Returns the maximum inconsistency coefficient for each
non-singleton cluster and its descendents.
Parameters
----------
Z : ndarray
The hierarchical clustering encoded as a matrix. See
`linkage` for more information.
R : ndarray
The inconsistency matrix.
Returns
-------
MI : ndarray
A monotonic ``(n-1)``-sized numpy array of doubles.
"""
Z = np.asarray(Z, order='c')
R = np.asarray(R, order='c')
is_valid_linkage(Z, throw=True, name='Z')
is_valid_im(R, throw=True, name='R')
n = Z.shape[0] + 1
if Z.shape[0] != R.shape[0]:
raise ValueError("The inconsistency matrix and linkage matrix each "
"have a different number of rows.")
MI = np.zeros((n - 1,))
[Z, R] = _copy_arrays_if_base_present([Z, R])
_hierarchy.get_max_Rfield_for_each_cluster(Z, R, MI, int(n), 3)
return MI
def maxRstat(Z, R, i):
"""
Returns the maximum statistic for each non-singleton cluster and
its descendents.
Parameters
----------
Z : array_like
The hierarchical clustering encoded as a matrix. See `linkage` for more
information.
R : array_like
The inconsistency matrix.
i : int
The column of `R` to use as the statistic.
Returns
-------
MR : ndarray
Calculates the maximum statistic for the i'th column of the
inconsistency matrix `R` for each non-singleton cluster
node. ``MR[j]`` is the maximum over ``R[Q(j)-n, i]`` where
``Q(j)`` the set of all node ids corresponding to nodes below
and including ``j``.
"""
Z = np.asarray(Z, order='c')
R = np.asarray(R, order='c')
is_valid_linkage(Z, throw=True, name='Z')
is_valid_im(R, throw=True, name='R')
if type(i) is not int:
raise TypeError('The third argument must be an integer.')
if i < 0 or i > 3:
raise ValueError('i must be an integer between 0 and 3 inclusive.')
if Z.shape[0] != R.shape[0]:
raise ValueError("The inconsistency matrix and linkage matrix each "
"have a different number of rows.")
n = Z.shape[0] + 1
MR = np.zeros((n - 1,))
[Z, R] = _copy_arrays_if_base_present([Z, R])
_hierarchy.get_max_Rfield_for_each_cluster(Z, R, MR, int(n), i)
return MR
def leaders(Z, T):
"""
Returns the root nodes in a hierarchical clustering.
Returns the root nodes in a hierarchical clustering corresponding
to a cut defined by a flat cluster assignment vector ``T``. See
the ``fcluster`` function for more information on the format of ``T``.
For each flat cluster :math:`j` of the :math:`k` flat clusters
represented in the n-sized flat cluster assignment vector ``T``,
this function finds the lowest cluster node :math:`i` in the linkage
tree Z such that:
* leaf descendents belong only to flat cluster j
(i.e. ``T[p]==j`` for all :math:`p` in :math:`S(i)` where
:math:`S(i)` is the set of leaf ids of leaf nodes descendent
with cluster node :math:`i`)
* there does not exist a leaf that is not descendent with
:math:`i` that also belongs to cluster :math:`j`
(i.e. ``T[q]!=j`` for all :math:`q` not in :math:`S(i)`). If
this condition is violated, ``T`` is not a valid cluster
assignment vector, and an exception will be thrown.
Parameters
----------
Z : ndarray
The hierarchical clustering encoded as a matrix. See
`linkage` for more information.
T : ndarray
The flat cluster assignment vector.
Returns
-------
L : ndarray
The leader linkage node id's stored as a k-element 1-D array
where ``k`` is the number of flat clusters found in ``T``.
``L[j]=i`` is the linkage cluster node id that is the
leader of flat cluster with id M[j]. If ``i < n``, ``i``
corresponds to an original observation, otherwise it
corresponds to a non-singleton cluster.
For example: if ``L[3]=2`` and ``M[3]=8``, the flat cluster with
id 8's leader is linkage node 2.
M : ndarray
The leader linkage node id's stored as a k-element 1-D array where
``k`` is the number of flat clusters found in ``T``. This allows the
set of flat cluster ids to be any arbitrary set of ``k`` integers.
"""
Z = np.asarray(Z, order='c')
T = np.asarray(T, order='c')
if type(T) != np.ndarray or T.dtype != 'i':
raise TypeError('T must be a one-dimensional numpy array of integers.')
is_valid_linkage(Z, throw=True, name='Z')
if len(T) != Z.shape[0] + 1:
raise ValueError('Mismatch: len(T)!=Z.shape[0] + 1.')
Cl = np.unique(T)
kk = len(Cl)
L = np.zeros((kk,), dtype='i')
M = np.zeros((kk,), dtype='i')
n = Z.shape[0] + 1
[Z, T] = _copy_arrays_if_base_present([Z, T])
s = _hierarchy.leaders(Z, T, L, M, int(kk), int(n))
if s >= 0:
raise ValueError(('T is not a valid assignment vector. Error found '
'when examining linkage node %d (< 2n-1).') % s)
return (L, M)
| bsd-3-clause |
nschaetti/nsNLP | features/BagOfCharactersTensor.py | 1 | 13672 | # -*- coding: utf-8 -*-
#
# File : core/downloader/PySpeechesConfig.py
# Description : .
# Date : 20th of February 2017
#
# This file is part of pySpeeches. pySpeeches is free software: you can
# redistribute it and/or modify it under the terms of the GNU General Public
# License as published by the Free Software Foundation, version 2.
#
# This program is distributed in the hope that it will be useful, but WITHOUT
# ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
# FOR A PARTICULAR PURPOSE. See the GNU General Public License for more
# details.
#
# You should have received a copy of the GNU General Public License along with
# this program; if not, write to the Free Software Foundation, Inc., 51
# Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
#
# Copyright Nils Schaetti, University of Neuchâtel <[email protected]>
# Import packages
import torch
import matplotlib.pyplot as plt
# Bag of characters tensor
class BagOfCharactersTensor(object):
"""
Bag of characters tensor
"""
# Constructor
def __init__(self, alphabet, uppercase=False, n_gram=1, multi=10, tokenizer=None, start_grams=False, end_grams=False):
"""
Constructor
:param n_gram:
"""
# Variables
self._alphabet = alphabet
self._uppercase = uppercase
self._n_gram = n_gram
self._n_chars = len(alphabet) + 1
self.chars = list()
self.char_count = dict()
self._multi = multi
self._start_grams = start_grams
self._end_grams = end_grams
self._tokenizer = tokenizer
# Char to index
self._char2index = dict()
for index, c in enumerate(alphabet):
self._char2index[c] = index
# end for
# end __init__
#########################################
# Override
#########################################
# Call
def __call__(self, text, normalize=True):
"""
Call
:return:
"""
# Size factor
size_factor = 1
# Start grams
if self._start_grams:
size_factor += 1
# end if
# End grams
if self._end_grams:
size_factor += 1
# end if
# Tensor
gram_tensor = None
if self._n_gram == 1:
gram_tensor = torch.zeros(1, self._n_chars * size_factor)
elif self._n_gram == 2:
gram_tensor = torch.zeros(1, self._n_chars * size_factor, self._n_chars + 1)
elif self._n_gram == 3:
gram_tensor = torch.zeros(1, self._n_chars * size_factor, self._n_chars + 1, self._n_chars + 1)
# end if
# Compute 1 gram
gram_tensor = self._compute_1gram(gram_tensor, text, normalize)
# Compute 2 gram
if self._n_gram >= 2:
gram_tensor = self._compute_2gram(gram_tensor, text, normalize)
# end if
# Compute 3 gram
if self._n_gram == 3:
gram_tensor = self._compute_3gram(gram_tensor, text, normalize)
# end if
# Start characters
if self._start_grams:
gram_tensor = self._compute_position_1gram(gram_tensor, text, 'start', self._n_chars, normalize)
# end if
# Start 2-grams
if self._start_grams and self._n_gram >= 2:
self._compute_position_2gram(gram_tensor, text, 'start', self._n_chars, normalize)
# end if
# End characters
if self._end_grams:
self._compute_position_1gram\
(
gram_tensor,
text,
'end',
self._n_chars if not self._start_grams else self._n_chars * 2,
normalize
)
# end if
# End grams
if self._end_grams and self._n_gram >= 2:
self._compute_position_2gram\
(
gram_tensor,
text,
'end',
self._n_chars if not self._start_grams else self._n_chars * 2,
normalize
)
# end if
return gram_tensor
# end __call__
#########################################
# Private
#########################################
# Compute 1-gram values
def _compute_1gram(self, tensor, text, normalize=True):
"""
Compute 1-gram values
:param tensor:
:param text:
:return:
"""
# Total
total = 0.0
# For each grams
for i in range(len(text)):
# Gram
gram = text[i]
# Index
char_index = self._get_char_index(gram)
# Not found
if char_index == -1:
char_index = self._n_chars - 1
# end if
# Set
if self._n_gram == 1:
tensor[0, char_index] += 1.0
elif self._n_gram == 2:
tensor[0, char_index, 0] += 1.0
elif self._n_gram == 3:
tensor[0, char_index, 0, 0] += 1.0
# end if
total += 1.0
# end for
# Normalize
if normalize:
if self._n_gram == 1:
tensor /= total
elif self._n_gram == 2:
tensor[0, :self._n_chars, 0] /= total
tensor[0, :self._n_chars, 0] /= tensor[0, :, 0].max()
tensor[0, :self._n_chars, 0] *= self._multi
elif self._n_gram == 3:
tensor[0, :self._n_chars, 0, 0] /= total
tensor[0, :self._n_chars, 0, 0] /= tensor[0, :, 0, 0].max()
tensor[0, :self._n_chars, 0, 0] *= self._multi
# end if
# end if
return tensor
# end _compute_1gram
# Compute 2-gram values
def _compute_2gram(self, tensor, text, normalize=True):
"""
Compute 2-gram values
:param tensor:
:param text:
:param normalize:
:return:
"""
# Total
total = 0.0
# For each grams
for i in range(len(text)-1):
# Gram
gram = text[i:i+2]
# Index
char_index1 = self._get_char_index(gram[0])
char_index2 = self._get_char_index(gram[1])
# Not found
if char_index1 == -1:
char_index1 = self._n_chars - 1
# end if
# Add
char_index2 = char_index2 + 1 if char_index2 != -1 else -1
# Set
if self._n_gram == 2:
tensor[0, char_index1, char_index2] += 1.0
elif self._n_gram == 3:
tensor[0, 0, char_index1, char_index2, 0] += 1.0
# end if
total += 1.0
# end for
# Normalize
if normalize:
if self._n_gram == 2:
tensor[0, :self._n_chars, 1:] /= total
tensor[0, :self._n_chars, 1:] /= tensor[0, :, 1:].max()
tensor[0, :self._n_chars, 1:] *= self._multi
elif self._n_gram == 3:
tensor[0, :self._n_chars, 1:, 0] /= total
tensor[0, :self._n_chars, 1:, 0] /= tensor[0, :, 1:, 0]
tensor[0, :self._n_chars, 1:, 0] *= self._multi
# end if
# end if
return tensor
# end _compute_2gram
# Compute 3-gram values
def _compute_3gram(self, tensor, text, normalize=True):
"""
Compute 3-gram values
:param tensor:
:param text:
:param normalize:
:return:
"""
# Total
total = 0.0
# For each grams
for i in range(len(text)-2):
# Gram
gram = text[i:i + 3]
# Index
char_index1 = self._get_char_index(gram[0])
char_index2 = self._get_char_index(gram[1])
char_index3 = self._get_char_index(gram[2])
# Not found
if char_index1 == -1:
char_index1 = self._n_chars - 1
# end if
# Add
char_index2 = char_index2 + 1 if char_index2 != -1 else -1
char_index3 = char_index3 + 1 if char_index3 != -1 else -1
# Set
tensor[0, char_index1, char_index2, char_index3] += 1.0
total += 1.0
# end for
# Normalize
if normalize:
tensor[0, :self._n_chars, 1:, 1:] /= total
tensor[0, :self._n_chars, 1:, 1:] /= tensor[0, :self._n_chars, 1:, 1:].max()
tensor[0, :self._n_chars, 1:, 1:] *= self._multi
# end if
return tensor
# end _compute_3gram
# Compute position grams
def _compute_position_1gram(self, tensor, text, gram_type, start_pos, normalize=True):
"""
Compute position 1grams
:param tensor:
:param text:
:param gram_type:
:param start_pos:
:param normalize:
:return:
"""
# Total
total = 0.0
# Check tokenizer
if self._tokenizer is None:
raise Exception(u"I need a tokenizer!")
# end if
# Gram position
gram_pos1 = 0
if gram_type == 'end':
gram_pos1 = -1
# end if
# For each token
for token in self._tokenizer(text):
# Length
if len(token) > 0:
# Index
char_index1 = self._get_char_index(token[gram_pos1])
# Not found
if char_index1 == -1:
char_index1 = self._n_chars - 1
# end if
# Set
if self._n_gram == 1:
tensor[0, start_pos + char_index1] += 1.0
elif self._n_gram == 2:
tensor[0, start_pos + char_index1, 0] += 1.0
else:
tensor[0, start_pos + char_index1, 0, 0] += 1.0
# end if
# Total
total += 1.0
# end if
# end for
# Normalize
if normalize:
if self._n_gram == 1:
tensor[0, start_pos:start_pos + self._n_chars] /= total
if self._n_gram == 2:
tensor[0, start_pos:start_pos + self._n_chars, 0] /= total
max = tensor[0, start_pos:start_pos + self._n_chars, 0].max()
tensor[0, start_pos:start_pos + self._n_chars, 0] /= max
tensor[0, start_pos:start_pos + self._n_chars, 0] *= self._multi
elif self._n_gram == 3:
tensor[0, start_pos:start_pos + self._n_chars, 0, 0] /= total
max = tensor[0, start_pos:start_pos + self._n_chars, 0, 0].max()
tensor[0, start_pos:start_pos + self._n_chars, 0, 0] /= max
tensor[0, start_pos:start_pos + self._n_chars, 0, 0] *= self._multi
# end if
# end if
return tensor
# end _compute_position_1gram
# Compute position grams
def _compute_position_2gram(self, tensor, text, gram_type, start_pos, normalize=True):
"""
Compute position grams
:param tensor:
:param text:
:param start_pos:
:param normalize:
:return:
"""
# Total
total = 0.0
# Check tokenizer
if self._tokenizer is None:
raise Exception(u"I need a tokenizer!")
# end if
# Gram positin
gram_pos1 = 0
gram_pos2 = 1
if gram_type == 'end':
gram_pos1 = -2
gram_pos2 = -1
# end if
# For each token
for token in self._tokenizer(text):
# Length
if len(token) > 1:
# Index
char_index1 = self._get_char_index(token[gram_pos1])
char_index2 = self._get_char_index(token[gram_pos2])
# Not found
if char_index1 == -1:
char_index1 = self._n_chars - 1
# end if
# Add
char_index2 = char_index2 + 1 if char_index2 != -1 else -1
# Set
if self._n_gram == 2:
tensor[0, start_pos + char_index1, char_index2] += 1.0
else:
tensor[0, start_pos + char_index1, char_index2, 0] += 1.0
# end if
# Total
total += 1.0
# end if
# end for
# Normalize
if normalize:
if self._n_gram == 2:
tensor[0, start_pos:start_pos+self._n_chars, 1:] /= total
max = tensor[0, start_pos:start_pos+self._n_chars, 1:].max()
tensor[0, start_pos:start_pos + self._n_chars, 1:] /= max
tensor[0, start_pos:start_pos + self._n_chars, 1:] *= self._multi
elif self._n_gram == 3:
tensor[0, start_pos:start_pos+self._n_chars, 1:, 0] /= total
max = tensor[0, start_pos:start_pos+self._n_chars, 1:, 0].max()
tensor[0, start_pos:start_pos + self._n_chars, 1:, 0] /= max
tensor[0, start_pos:start_pos + self._n_chars, 1:, 0] *= self._multi
# end if
# end if
return tensor
# end _compute_position_gram
# Get char index
def _get_char_index(self, c):
"""
Get char index
:param c:
:return:
"""
try:
return self._char2index[c]
except KeyError:
return -1
# end try
# end _get_char_index
# end BagOfCharactersTensor | gpl-3.0 |
chenyyx/scikit-learn-doc-zh | examples/zh/linear_model/plot_iris_logistic.py | 119 | 1679 | #!/usr/bin/python
# -*- coding: utf-8 -*-
"""
=========================================================
Logistic Regression 3-class Classifier
=========================================================
Show below is a logistic-regression classifiers decision boundaries on the
`iris <https://en.wikipedia.org/wiki/Iris_flower_data_set>`_ dataset. The
datapoints are colored according to their labels.
"""
print(__doc__)
# Code source: Gaël Varoquaux
# Modified for documentation by Jaques Grobler
# License: BSD 3 clause
import numpy as np
import matplotlib.pyplot as plt
from sklearn import linear_model, datasets
# import some data to play with
iris = datasets.load_iris()
X = iris.data[:, :2] # we only take the first two features.
Y = iris.target
h = .02 # step size in the mesh
logreg = linear_model.LogisticRegression(C=1e5)
# we create an instance of Neighbours Classifier and fit the data.
logreg.fit(X, Y)
# Plot the decision boundary. For that, we will assign a color to each
# point in the mesh [x_min, x_max]x[y_min, y_max].
x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5
y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5
xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))
Z = logreg.predict(np.c_[xx.ravel(), yy.ravel()])
# Put the result into a color plot
Z = Z.reshape(xx.shape)
plt.figure(1, figsize=(4, 3))
plt.pcolormesh(xx, yy, Z, cmap=plt.cm.Paired)
# Plot also the training points
plt.scatter(X[:, 0], X[:, 1], c=Y, edgecolors='k', cmap=plt.cm.Paired)
plt.xlabel('Sepal length')
plt.ylabel('Sepal width')
plt.xlim(xx.min(), xx.max())
plt.ylim(yy.min(), yy.max())
plt.xticks(())
plt.yticks(())
plt.show()
| gpl-3.0 |
jjx02230808/project0223 | examples/applications/plot_species_distribution_modeling.py | 254 | 7434 | """
=============================
Species distribution modeling
=============================
Modeling species' geographic distributions is an important
problem in conservation biology. In this example we
model the geographic distribution of two south american
mammals given past observations and 14 environmental
variables. Since we have only positive examples (there are
no unsuccessful observations), we cast this problem as a
density estimation problem and use the `OneClassSVM` provided
by the package `sklearn.svm` as our modeling tool.
The dataset is provided by Phillips et. al. (2006).
If available, the example uses
`basemap <http://matplotlib.sourceforge.net/basemap/doc/html/>`_
to plot the coast lines and national boundaries of South America.
The two species are:
- `"Bradypus variegatus"
<http://www.iucnredlist.org/apps/redlist/details/3038/0>`_ ,
the Brown-throated Sloth.
- `"Microryzomys minutus"
<http://www.iucnredlist.org/apps/redlist/details/13408/0>`_ ,
also known as the Forest Small Rice Rat, a rodent that lives in Peru,
Colombia, Ecuador, Peru, and Venezuela.
References
----------
* `"Maximum entropy modeling of species geographic distributions"
<http://www.cs.princeton.edu/~schapire/papers/ecolmod.pdf>`_
S. J. Phillips, R. P. Anderson, R. E. Schapire - Ecological Modelling,
190:231-259, 2006.
"""
# Authors: Peter Prettenhofer <[email protected]>
# Jake Vanderplas <[email protected]>
#
# License: BSD 3 clause
from __future__ import print_function
from time import time
import numpy as np
import matplotlib.pyplot as plt
from sklearn.datasets.base import Bunch
from sklearn.datasets import fetch_species_distributions
from sklearn.datasets.species_distributions import construct_grids
from sklearn import svm, metrics
# if basemap is available, we'll use it.
# otherwise, we'll improvise later...
try:
from mpl_toolkits.basemap import Basemap
basemap = True
except ImportError:
basemap = False
print(__doc__)
def create_species_bunch(species_name, train, test, coverages, xgrid, ygrid):
"""Create a bunch with information about a particular organism
This will use the test/train record arrays to extract the
data specific to the given species name.
"""
bunch = Bunch(name=' '.join(species_name.split("_")[:2]))
species_name = species_name.encode('ascii')
points = dict(test=test, train=train)
for label, pts in points.items():
# choose points associated with the desired species
pts = pts[pts['species'] == species_name]
bunch['pts_%s' % label] = pts
# determine coverage values for each of the training & testing points
ix = np.searchsorted(xgrid, pts['dd long'])
iy = np.searchsorted(ygrid, pts['dd lat'])
bunch['cov_%s' % label] = coverages[:, -iy, ix].T
return bunch
def plot_species_distribution(species=("bradypus_variegatus_0",
"microryzomys_minutus_0")):
"""
Plot the species distribution.
"""
if len(species) > 2:
print("Note: when more than two species are provided,"
" only the first two will be used")
t0 = time()
# Load the compressed data
data = fetch_species_distributions()
# Set up the data grid
xgrid, ygrid = construct_grids(data)
# The grid in x,y coordinates
X, Y = np.meshgrid(xgrid, ygrid[::-1])
# create a bunch for each species
BV_bunch = create_species_bunch(species[0],
data.train, data.test,
data.coverages, xgrid, ygrid)
MM_bunch = create_species_bunch(species[1],
data.train, data.test,
data.coverages, xgrid, ygrid)
# background points (grid coordinates) for evaluation
np.random.seed(13)
background_points = np.c_[np.random.randint(low=0, high=data.Ny,
size=10000),
np.random.randint(low=0, high=data.Nx,
size=10000)].T
# We'll make use of the fact that coverages[6] has measurements at all
# land points. This will help us decide between land and water.
land_reference = data.coverages[6]
# Fit, predict, and plot for each species.
for i, species in enumerate([BV_bunch, MM_bunch]):
print("_" * 80)
print("Modeling distribution of species '%s'" % species.name)
# Standardize features
mean = species.cov_train.mean(axis=0)
std = species.cov_train.std(axis=0)
train_cover_std = (species.cov_train - mean) / std
# Fit OneClassSVM
print(" - fit OneClassSVM ... ", end='')
clf = svm.OneClassSVM(nu=0.1, kernel="rbf", gamma=0.5)
clf.fit(train_cover_std)
print("done.")
# Plot map of South America
plt.subplot(1, 2, i + 1)
if basemap:
print(" - plot coastlines using basemap")
m = Basemap(projection='cyl', llcrnrlat=Y.min(),
urcrnrlat=Y.max(), llcrnrlon=X.min(),
urcrnrlon=X.max(), resolution='c')
m.drawcoastlines()
m.drawcountries()
else:
print(" - plot coastlines from coverage")
plt.contour(X, Y, land_reference,
levels=[-9999], colors="k",
linestyles="solid")
plt.xticks([])
plt.yticks([])
print(" - predict species distribution")
# Predict species distribution using the training data
Z = np.ones((data.Ny, data.Nx), dtype=np.float64)
# We'll predict only for the land points.
idx = np.where(land_reference > -9999)
coverages_land = data.coverages[:, idx[0], idx[1]].T
pred = clf.decision_function((coverages_land - mean) / std)[:, 0]
Z *= pred.min()
Z[idx[0], idx[1]] = pred
levels = np.linspace(Z.min(), Z.max(), 25)
Z[land_reference == -9999] = -9999
# plot contours of the prediction
plt.contourf(X, Y, Z, levels=levels, cmap=plt.cm.Reds)
plt.colorbar(format='%.2f')
# scatter training/testing points
plt.scatter(species.pts_train['dd long'], species.pts_train['dd lat'],
s=2 ** 2, c='black',
marker='^', label='train')
plt.scatter(species.pts_test['dd long'], species.pts_test['dd lat'],
s=2 ** 2, c='black',
marker='x', label='test')
plt.legend()
plt.title(species.name)
plt.axis('equal')
# Compute AUC with regards to background points
pred_background = Z[background_points[0], background_points[1]]
pred_test = clf.decision_function((species.cov_test - mean)
/ std)[:, 0]
scores = np.r_[pred_test, pred_background]
y = np.r_[np.ones(pred_test.shape), np.zeros(pred_background.shape)]
fpr, tpr, thresholds = metrics.roc_curve(y, scores)
roc_auc = metrics.auc(fpr, tpr)
plt.text(-35, -70, "AUC: %.3f" % roc_auc, ha="right")
print("\n Area under the ROC curve : %f" % roc_auc)
print("\ntime elapsed: %.2fs" % (time() - t0))
plot_species_distribution()
plt.show()
| bsd-3-clause |
Eric89GXL/mne-python | mne/io/tests/test_what.py | 10 | 1766 | # Authors: Eric Larson <[email protected]>
# License: BSD
import glob
import os.path as op
import numpy as np
import pytest
from mne import what, create_info
from mne.datasets import testing
from mne.io import RawArray
from mne.preprocessing import ICA
from mne.utils import run_tests_if_main, requires_sklearn
data_path = testing.data_path(download=False)
@pytest.mark.slowtest
@requires_sklearn
@testing.requires_testing_data
def test_what(tmpdir, verbose_debug):
"""Test mne.what."""
# ICA
ica = ICA(max_iter=1)
raw = RawArray(np.random.RandomState(0).randn(3, 10),
create_info(3, 1000., 'eeg'))
with pytest.warns(None): # convergence sometimes
ica.fit(raw)
fname = op.join(str(tmpdir), 'x-ica.fif')
ica.save(fname)
assert what(fname) == 'ica'
# test files
fnames = glob.glob(
op.join(data_path, 'MEG', 'sample', '*.fif'))
fnames += glob.glob(
op.join(data_path, 'subjects', 'sample', 'bem', '*.fif'))
fnames = sorted(fnames)
want_dict = dict(eve='events', ave='evoked', cov='cov', inv='inverse',
fwd='forward', trans='transform', proj='proj',
raw='raw', meg='raw', sol='bem solution',
bem='bem surfaces', src='src', dense='bem surfaces',
sparse='bem surfaces', head='bem surfaces',
fiducials='fiducials')
for fname in fnames:
kind = op.splitext(fname)[0].split('-')[-1]
if len(kind) > 5:
kind = kind.split('_')[-1]
this = what(fname)
assert this == want_dict[kind]
fname = op.join(data_path, 'MEG', 'sample', 'sample_audvis-ave_xfit.dip')
assert what(fname) == 'unknown'
run_tests_if_main()
| bsd-3-clause |
BBN-Q/QGL | QGL/drivers/APS3Pattern.py | 1 | 67288 | '''
Module for writing hdf5 APS2 files from sequences and patterns
Copyright 2014 Raytheon BBN Technologies
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
'''
import os
import logging
from warnings import warn
from copy import copy
from itertools import zip_longest
import pickle
import struct
import sys
import numpy as np
from QGL import Compiler, ControlFlow, BlockLabel, PatternUtils
from QGL import PulseSequencer
from QGL.PatternUtils import hash_pulse, flatten
from QGL import TdmInstructions
# Python 2/3 compatibility: use 'int' that subclasses 'long'
from builtins import int
logger = logging.getLogger(__name__)
#Some constants
SAMPLING_RATE = 2.5e9
ADDRESS_UNIT = 8 #everything is done in units of 4 timesteps
MIN_ENTRY_LENGTH = 8
MAX_WAVEFORM_PTS = 2**28 #maximum size of waveform memory
WAVEFORM_CACHE_SIZE = 2**17
MAX_WAVEFORM_VALUE = 2**15 - 1 #maximum waveform value i.e. 14bit DAC
MAX_NUM_INSTRUCTIONS = 2**26
MAX_REPEAT_COUNT = 2**16 - 1
MAX_TRIGGER_COUNT = 2**32 - 1
MODULATION_CLOCK = 312.5e6
# instruction encodings
WFM = 0x0
MARKER = 0x1
WAIT = 0x2
LOAD = 0x3
REPEAT = 0x4
CMP = 0x5
GOTO = 0x6
CALL = 0x7
RET = 0x8
SYNC = 0x9
MODULATION = 0xA
LOADCMP = 0xB
PREFETCH = 0xC
NOP = 0XF
# # APS3 prototype
CUSTOM = 0xD
INVALIDATE = 0xE # Invalidate and WriteAddr use the same opcode
WRITEADDR = 0xE
# WFM/MARKER op codes
PLAY = 0x0
WAIT_TRIG = 0x1
WAIT_SYNC = 0x2
WFM_PREFETCH = 0x3
WFM_OP_OFFSET = 46
TA_PAIR_BIT = 45
# CMP op encodings
EQUAL = 0x0
NOTEQUAL = 0x1
GREATERTHAN = 0x2
LESSTHAN = 0x3
CMPTABLE = {'==': EQUAL, '!=': NOTEQUAL, '>': GREATERTHAN, '<': LESSTHAN}
# custom OP_CODES
TDM_MAJORITY_VOTE = 0
TDM_MAJORITY_VOTE_SET_MASK = 1
TDM_TSM_SET_ROUNDS = 2
TDM_TSM = 3
APS_CUSTOM_DECODE = ["APS_RAND", "APS_CLIFFORD_RAND","APS_CLIFFORD_SET_SEED" ,"APS_CLIFFORD_SET_OFFSET"
, "APS_CLIFFORD_SET_SPACING"]
TDM_CUSTOM_DECODE = ["TDM_MAJORITY_VOTE", "TDM_MAJORITY_VOTE_SET_MASK", "TDM_TSM_SET_ROUNDS", "TDM_TSM"]
# Whether we use PHASE_OFFSET modulation commands or bake it into waveform
# Default to false as we usually don't have many variants
USE_PHASE_OFFSET_INSTRUCTION = False
# Whether to save the waveform offsets for partial compilation
SAVE_WF_OFFSETS = False
# Do we want a pulse file per instrument or per channel
SEQFILE_PER_CHANNEL = False
def get_empty_channel_set():
return {'ch1': {}, 'm1': {}}
def get_seq_file_extension():
return '.aps3'
def is_compatible_file(filename):
with open(filename, 'rb') as FID:
byte = FID.read(4)
if byte == b'APS3':
return True
return False
def create_wf_vector(wfLib, seqs):
'''
Helper function to create the wf vector and offsets into it.
'''
max_pts_needed = 0
for wf in wfLib.values():
if len(wf) == 1:
max_pts_needed += ADDRESS_UNIT
else:
max_pts_needed += len(wf)
#If we have more than fits in cache we'll need to align and prefetch
need_prefetch = max_pts_needed > WAVEFORM_CACHE_SIZE
idx = 0
if not need_prefetch:
offsets = [{}]
cache_lines = []
#if we can fit them all in just pack
wfVec = np.zeros(max_pts_needed, dtype=np.int16)
for key, wf in wfLib.items():
#Clip the wf
wf[wf > 1] = 1.0
wf[wf < -1] = -1.0
#TA pairs need to be repeated ADDRESS_UNIT times
if wf.size == 1:
wf = wf.repeat(ADDRESS_UNIT)
#Ensure the wf is an integer number of ADDRESS_UNIT's
trim = wf.size % ADDRESS_UNIT
if trim:
wf = wf[:-trim]
wfVec[idx:idx + wf.size] = np.int16(MAX_WAVEFORM_VALUE * wf)
offsets[-1][key] = idx
idx += wf.size
#Trim the waveform
wfVec.resize(idx)
else:
#otherwise fill in one cache line at a time
CACHE_LINE_LENGTH = int(np.round(WAVEFORM_CACHE_SIZE / 2)) - 1
wfVec = np.zeros(CACHE_LINE_LENGTH, dtype=np.int16)
offsets = [{}]
cache_lines = []
for seq in seqs:
#go through sequence and see what we need to add
pts_to_add = 0
for entry in seq:
if isinstance(entry, Compiler.Waveform):
sig = wf_sig(entry)
if sig not in offsets[-1]:
pts_to_add += entry.length
#If what we need to add spills over then add a line and start again
if (idx % CACHE_LINE_LENGTH) + pts_to_add > CACHE_LINE_LENGTH:
idx = int(CACHE_LINE_LENGTH * (
(idx + CACHE_LINE_LENGTH) // CACHE_LINE_LENGTH))
wfVec = np.append(wfVec,
np.zeros(int(CACHE_LINE_LENGTH),
dtype=np.int16))
offsets.append({})
for entry in seq:
if isinstance(entry, Compiler.Waveform):
sig = wf_sig(entry)
if sig not in offsets[-1]:
wf = wfLib[sig]
wf[wf > 1] = 1.0
wf[wf < -1] = -1.0
#TA pairs need to be repeated ADDRESS_UNIT times
if wf.size == 1:
wf = wf.repeat(ADDRESS_UNIT)
#Ensure the wf is an integer number of ADDRESS_UNIT's
trim = wf.size % ADDRESS_UNIT
if trim:
wf = wf[:-trim]
wfVec[idx:idx + wf.size] = np.int16(
MAX_WAVEFORM_VALUE * wf)
offsets[-1][sig] = idx
idx += wf.size
cache_lines.append(int(idx // CACHE_LINE_LENGTH))
return wfVec, offsets, cache_lines
class Instruction(object):
def __init__(self, header, payload, label=None, target=None, decode_as_tdm = False):
self.header = header
self.payload = int(payload)
self.label = label
self.target = target
self.decode_as_tdm = decode_as_tdm
@classmethod
def unflatten(cls, instr, decode_as_tdm = False):
return cls(header=(int(instr) >> 56) & 0xff,
payload=int(instr) & 0xffffffffffffff,
decode_as_tdm = decode_as_tdm)
def __repr__(self):
return self.__str__()
def __str__(self):
opCodes = ["WFM", "MARKER", "WAIT", "LOAD", "REPEAT", "CMP", "GOTO",
"CALL", "RET", "SYNC", "MODULATION", "LOADCMP", "PREFETCH",
"CUSTOM", "WRITEADDR", "NOP"]
# list of opCodes where the reprenstation will change
excludeList = ["WRITEADDR", "LOADCMP"]
out = "{0} ".format(self.label) if self.label else ""
instrOpCode = (self.header >> 4) & 0xf
opCodeStr = opCodes[instrOpCode]
if opCodeStr not in excludeList:
out += opCodeStr
if (instrOpCode == MARKER) or (instrOpCode == WFM) or (
instrOpCode == MODULATION):
if (instrOpCode == MARKER) or (instrOpCode == WFM):
out += "; engine={}, ".format((self.header >> 2) & 0x3)
else:
out += "; "
if self.header & 0x1:
out += "write=1 | "
else:
out += "write=0 | "
if self.target:
out += " {}".format(self.target)
if instrOpCode == WFM:
wfOpCode = (self.payload >> 46) & 0x3
wfOpCodes = ["PLAY", "TRIG", "SYNC", "PREFETCH"]
out += wfOpCodes[wfOpCode]
out += "; TA_bit={}".format((self.payload >> 45) & 0x1)
out += ", count={}".format((self.payload >> 24) & 2**21 - 1)
out += ", addr={}".format(self.payload & 2**24 - 1)
# # APS3/TDM modifier to use VRAM output
# if self.payload & (1 << 48):
# out += ", use_vram"
elif instrOpCode == MARKER:
mrkOpCode = (self.payload >> 46) & 0x3
mrkOpCodes = ["PLAY", "TRIG", "SYNC"]
out += mrkOpCodes[mrkOpCode]
out += "; state={}".format((self.payload >> 32) & 0x1)
out += ", count={}".format(self.payload & 2**32 - 1)
elif instrOpCode == MODULATION:
modulatorOpCode = (self.payload >> 45) & 0x7
modulatorOpCodes = ["MODULATE", "RESET_PHASE", "TRIG", "SET_FREQ",
"SYNC", "SET_PHASE", "", "UPDATE_FRAME"]
out += modulatorOpCodes[modulatorOpCode]
out += "; nco_select=0x{:x}".format((self.payload >> 40) & 0xf)
if modulatorOpCode == 0x0:
out += ", count={:d}".format(self.payload & 0xffffffff)
elif modulatorOpCode == 0x3:
out += ", increment=0x{:08x}".format(self.payload & 0xffffffff)
elif modulatorOpCode == 0x5:
out += ", phase=0x{:08x}".format(self.payload & 0xffffffff)
elif modulatorOpCode == 0x7:
out += ", frame_change=0x{:08x}".format(self.payload &
0xffffffff)
elif instrOpCode == CMP:
cmpCodes = ["EQUAL", "NOTEQUAL", "GREATERTHAN", "LESSTHAN"]
cmpCode = (self.payload >> 8) & 0x3
out += " | " + cmpCodes[cmpCode]
out += ", value={}".format(self.payload & 0xff)
elif any(
[instrOpCode == op for op in [GOTO, CALL, RET, REPEAT, PREFETCH]]):
out += " | target_addr={}".format(self.payload & 2**26 - 1)
elif instrOpCode == LOAD:
out += " | count={}".format(self.payload)
elif instrOpCode == CUSTOM:
store_addr = self.payload & 0xFFFF
load_addr = (self.payload >> 16) & 0xFFFF
instruction = (self.payload >> 32) & 0xFF
instructionAPS = TDM_CUSTOM_DECODE[instruction]
out += " | instruction={0} ({1}), load_addr=0x{2:0x}, store_addr=0x{3:0x}".format(instruction, instructionAPS, load_addr, store_addr)
elif instrOpCode == WRITEADDR:
addr = self.payload & 0xFFFF
value = (self.payload >> 16) & 0xFFFFFFFF
invalidate = not (self.header & 0x1)
mapTrigger = (self.header >> 2) & 0x1
writeCrossbar = (self.header >> 1) & 0x1
instrStr = "WRITEADDR "
valueType = "value"
if invalidate:
instrStr = "INVALIDATE"
valueType = "valid_mask"
if mapTrigger:
instrStr = "STOREMEAS"
valueType = "mapping"
if writeCrossbar:
instrStr = "WRITECB"
valuetype = "mapping"
addr = (self.payload >> 16) & 0xFFFF
value = (self.payload >> 32) & 0xFFFF
out += "{0} | addr=0x{1:0x}, {2}=0x{3:0x}".format(instrStr, addr, valueType, value)
elif instrOpCode == LOADCMP:
addr = self.payload & 0xFFFF
mask = (self.payload >> 16) & 0xFFFF7
use_ram = (self.payload >> 48) & 0x1
if self.decode_as_tdm and not use_ram:
out += "WAITMEAS"
else:
src = "EXT"
if use_ram:
src = "RAM"
out += "LOADCMP | source={0}, addr=0x{1:0x}, read_mask=0x{2:0x}".format(src, addr, mask)
return out
def __eq__(self, other):
return self.header == other.header and self.payload == other.payload and self.label == other.label
def __ne__(self, other):
return not self == other
def __hash__(self):
return hash((self.header, self.payload, self.label))
@property
def address(self):
return self.payload & 0xffffffff # bottom 32-bits of payload
@address.setter
def address(self, value):
self.payload |= value & 0xffffffff
@property
def writeFlag(self):
return self.header & 0x1
@writeFlag.setter
def writeFlag(self, value):
self.header |= value & 0x1
@property
def opcode(self):
return self.header >> 4
def flatten(self):
return int((self.header << 56) | (self.payload & 0xffffffffffffff))
def Waveform(addr, count, isTA, write=False, label=None):
header = (WFM << 4) | (0x3 << 2) | (write &
0x1) #broadcast to both engines
count = int(count)
count = ((count // ADDRESS_UNIT) - 1) & 0x000fffff # 20 bit count
addr = (addr // ADDRESS_UNIT) & 0x00ffffff # 24 bit addr
payload = (PLAY << WFM_OP_OFFSET) | ((int(isTA) & 0x1)
<< TA_PAIR_BIT) | (count << 24) | addr
return Instruction(header, payload, label)
def WaveformPrefetch(addr):
header = (WFM << 4) | (0x3 << 2) | (0x1)
payload = (WFM_PREFETCH << WFM_OP_OFFSET) | addr
return Instruction(header, payload, None)
def Marker(sel, state, count, write=False, label=None):
header = (MARKER << 4) | ((sel & 0x3) << 2) | (write & 0x1)
count = int(count)
four_count = ((count // ADDRESS_UNIT) - 1) & 0xffffffff # 32 bit count
count_rem = 0#count % ADDRESS_UNIT
if state == 0:
transitionWords = {0: 0b0000, 1: 0b1000, 2: 0b1100, 3: 0b1110}
transition = transitionWords[count_rem]
else:
transitionWords = {0: 0b1111, 1: 0b0111, 2: 0b0011, 3: 0b0001}
transition = transitionWords[count_rem]
payload = (PLAY << WFM_OP_OFFSET) | (transition << 33) | (
(state & 0x1) << 32) | four_count
return Instruction(header, payload, label)
def Command(cmd, payload, write=False, label=None):
header = (cmd << 4)
if isinstance(payload, int):
instr = Instruction(header, payload, label)
else:
instr = Instruction(header, 0, label, target=payload)
instr.writeFlag = write
return instr
def Sync(label=None):
return Command(SYNC, WAIT_SYNC << WFM_OP_OFFSET, write=True, label=label)
def Wait(label=None):
return Command(WAIT, WAIT_TRIG << WFM_OP_OFFSET, write=True, label=label)
def LoadCmp(label=None):
return Command(LOADCMP, 0, label=label)
def Cmp(op, value, label=None):
return Command(CMP, (op << 8) | (value & 0xff), label=label)
def Goto(addr, label=None):
return Command(GOTO, addr, label=label)
def Call(addr, label=None):
return Command(CALL, addr, label=label)
def Return(label=None):
return Command(RET, 0, label=label)
def Load(count, label=None):
return Command(LOAD, count, label=label)
def Repeat(addr, label=None):
return Command(REPEAT, addr, label=label)
def Prefetch(addr, label=None):
return Command(PREFETCH, addr)
def NoOp():
return Instruction.unflatten(0xffffffffffffffff)
# QGL instructions
def Invalidate(addr, mask, label=None):
header = WRITEADDR << 4
payload = (mask << 16) | addr
return Instruction(header, payload, label=label)
def WriteAddr(addr, value, label=None):
header = (WRITEADDR << 4) | 1
payload = (value << 16) | addr
return Instruction(header, payload, label=label)
def StoreMeas(addr, mapping, label=None):
header = (WRITEADDR << 4) | 5
payload = (mapping << 16) | addr
return Instruction(header, payload, label=label)
def CrossBar(addr, value, label=None):
header = (WRITEADDR << 4) | 3
payload = (value << 32) | (addr << 16)
return Instruction(header, payload, label=label)
def Custom(in_addr, out_addr, custom_op, label=None):
header = CUSTOM << 4
payload = (custom_op << 32) | (in_addr << 16) | out_addr
return Instruction(header, payload, label=label)
def MajorityVote(in_addr, out_addr, label=None):
return Custom(in_addr, out_addr, 0, label=label)
def MajorityVoteMask(in_addr, out_addr, label=None):
return Custom(in_addr, out_addr, 1, label=label)
def DecodeSetRounds(in_addr, out_addr, label=None):
return Custom(in_addr, out_addr, 2, label=label)
def Decode(in_addr, out_addr, label=None):
return Custom(in_addr, out_addr, 3, label=label)
def LoadCmpVram(addr, mask, label=None):
header = LOADCMP << 4
payload = (1 << 48) | (mask << 16) | addr
return Instruction(header, payload, label=label)
def preprocess(seqs, shapeLib):
seqs = PatternUtils.convert_lengths_to_samples(
seqs, SAMPLING_RATE, ADDRESS_UNIT, Compiler.Waveform)
wfLib = build_waveforms(seqs, shapeLib)
inject_modulation_cmds(seqs)
return seqs, wfLib
def wf_sig(wf):
'''
Compute a signature of a Compiler.Waveform that identifies the relevant properties for
two Waveforms to be considered "equal" in the waveform library. For example, we ignore
length of TA waveforms.
'''
if wf.isZero or wf.isTimeAmp: # 2nd condition necessary until we support RT SSB
if USE_PHASE_OFFSET_INSTRUCTION:
return (wf.amp)
else:
return (wf.amp, round(wf.phase * 2**13))
else:
#TODO: why do we need the length?
if USE_PHASE_OFFSET_INSTRUCTION:
return (wf.key, wf.amp, wf.length)
else:
return (wf.key, round(wf.phase * 2**13), wf.amp, wf.length)
class ModulationCommand(object):
"""docstring for ModulationCommand"""
def __init__(self,
instruction,
nco_select,
frequency=0,
phase=0,
length=0):
super(ModulationCommand, self).__init__()
self.instruction = instruction
self.nco_select = nco_select
self.frequency = frequency
self.phase = phase
self.length = length
def __str__(self):
out = "Modulation({}, nco_select=0x{:x}".format(self.instruction,
self.nco_select)
if self.instruction == "MODULATE":
out += ", length={})".format(self.length)
elif self.instruction == "SET_FREQ":
out += ", frequency={})".format(self.frequency)
elif self.instruction == "SET_PHASE" or self.instruction == "UPDATE_FRAME":
out += ", phase={})".format(self.phase)
else:
out += ")"
return out
def _repr_pretty_(self, p, cycle):
p.text(str(self))
def __repr__(self):
return str(self)
def to_instruction(self, write_flag=True, label=None):
#Modulator op codes
MODULATOR_OP_OFFSET = 44
NCO_SELECT_OP_OFFSET = 40
op_code_map = {"MODULATE": 0x0,
"RESET_PHASE": 0x2,
"SET_FREQ": 0x6,
"SET_PHASE": 0xa,
"UPDATE_FRAME": 0xe}
payload = (op_code_map[self.instruction] << MODULATOR_OP_OFFSET) | (
self.nco_select << NCO_SELECT_OP_OFFSET)
if self.instruction == "MODULATE":
#zero-indexed quad count
payload |= np.uint32(self.length / ADDRESS_UNIT - 1)
elif self.instruction == "SET_FREQ":
# frequencies can span -4 to 4 or 0 to 8 in unsigned
payload |= np.uint32(
(self.frequency / MODULATION_CLOCK if self.frequency > 0 else
self.frequency / MODULATION_CLOCK + 8) * 2**27)
elif (self.instruction == "SET_PHASE") | (
self.instruction == "UPDATE_FRAME"):
#phases can span -0.5 to 0.5 or 0 to 1 in unsigned
payload |= np.uint32(np.mod(self.phase / (2 * np.pi), 1) * 2**27)
instr = Instruction(MODULATION << 4, payload, label)
instr.writeFlag = write_flag
return instr
def inject_modulation_cmds(seqs):
"""
Inject modulation commands from phase, frequency and frameChange of waveforms
in an IQ waveform sequence. Assume up to 2 NCOs for now.
"""
cur_freq = 0
cur_phase = 0
for ct,seq in enumerate(seqs):
#check whether we have modulation commands
freqs = np.unique([entry.frequency for entry in filter(lambda s: isinstance(s,Compiler.Waveform), seq)])
if len(freqs) > 2:
raise Exception("Max 2 frequencies on the same channel allowed.")
no_freq_cmds = np.all(np.less(np.abs(freqs), 1e-8))
phases = [entry.phase for entry in filter(lambda s: isinstance(s,Compiler.Waveform), seq)]
no_phase_cmds = np.all(np.less(np.abs(phases), 1e-8))
frame_changes = [entry.frameChange for entry in filter(lambda s: isinstance(s,Compiler.Waveform), seq)]
no_frame_cmds = np.all(np.less(np.abs(frame_changes), 1e-8))
no_modulation_cmds = no_freq_cmds and no_phase_cmds and no_frame_cmds
if no_modulation_cmds:
continue
mod_seq = []
pending_frame_update = False
for entry in seq:
#copies to avoid same object having different timestamps later
#copy through BlockLabel
if isinstance(entry, BlockLabel.BlockLabel):
mod_seq.append(copy(entry))
#mostly copy through control-flow
elif isinstance(entry, ControlFlow.ControlInstruction) or isinstance(entry, TdmInstructions.LoadCmpVramInstruction) or isinstance(entry, TdmInstructions.WriteAddrInstruction):
#heuristic to insert phase reset before trigger if we have modulation commands
if isinstance(entry, ControlFlow.Wait):
if not ( no_modulation_cmds and (cur_freq == 0) and (cur_phase == 0)):
mod_seq.append(ModulationCommand("RESET_PHASE", 0x3))
for nco_ind, freq in enumerate(freqs):
mod_seq.append( ModulationCommand("SET_FREQ", nco_ind + 1, frequency = freq) )
elif isinstance(entry, ControlFlow.Return):
cur_freq = 0 #makes sure that the frequency is set in the first sequence after the definition of subroutines
mod_seq.append(copy(entry))
elif isinstance(entry, Compiler.Waveform):
if not no_modulation_cmds:
#select nco
nco_select = (list(freqs)).index(entry.frequency) + 1
cur_freq = entry.frequency
if USE_PHASE_OFFSET_INSTRUCTION and (entry.length > 0) and (cur_phase != entry.phase):
mod_seq.append( ModulationCommand("SET_PHASE", nco_select, phase=entry.phase) )
cur_phase = entry.phase
#now apply modulation for count command and waveform command, if non-zero length
if entry.length > 0:
mod_seq.append(entry)
# if we have a modulate waveform modulate pattern and there is no pending frame update we can append length to previous modulation command
if (len(mod_seq) > 1) and (isinstance(mod_seq[-1], Compiler.Waveform)) and (isinstance(mod_seq[-2], ModulationCommand)) and (mod_seq[-2].instruction == "MODULATE") \
and mod_seq[-1].frequency == freqs[mod_seq[-2].nco_select - 1] and not pending_frame_update:
mod_seq[-2].length += entry.length
else:
mod_seq.append( ModulationCommand("MODULATE", nco_select, length = entry.length))
pending_frame_update = False
#now apply non-zero frame changes after so it is applied at end
if entry.frameChange != 0:
pending_frame_update = True
#zero length frame changes (Z pulses) need to be combined with the previous frame change or injected where possible
if entry.length == 0:
#if the last is a frame change then we can add to the frame change
if isinstance(mod_seq[-1], ModulationCommand) and mod_seq[-1].instruction == "UPDATE_FRAME":
mod_seq[-1].phase += entry.frameChange
#if last entry was pulse without frame change we add frame change
elif (isinstance(mod_seq[-1], Compiler.Waveform)) or (mod_seq[-1].instruction == "MODULATE"):
mod_seq.append( ModulationCommand("UPDATE_FRAME", nco_select, phase=entry.frameChange) )
#if this is the first entry with a wait for trigger then we can inject a frame change
#before the wait for trigger but after the RESET_PHASE
elif isinstance(mod_seq[-1], ControlFlow.Wait):
mod_seq.insert(-1, ModulationCommand("UPDATE_FRAME", nco_select, phase=entry.frameChange) )
elif isinstance(mod_seq[-2], ControlFlow.Wait) and isinstance(mod_seq[-1], ModulationCommand) and mod_seq[-1].instruction == "SET_FREQ":
mod_seq.insert(-2, ModulationCommand("UPDATE_FRAME", nco_select, phase=entry.frameChange) )
#otherwise drop and error if frame has been defined
else:
raise Exception("Unable to implement zero time Z pulse")
else:
mod_seq.append( ModulationCommand("UPDATE_FRAME", nco_select, phase=entry.frameChange) )
seqs[ct] = mod_seq
def build_waveforms(seqs, shapeLib):
# apply amplitude (and optionally phase) and add the resulting waveforms to the library
wfLib = {}
for wf in flatten(seqs):
if isinstance(wf, Compiler.Waveform) and wf_sig(wf) not in wfLib:
shape = wf.amp * shapeLib[wf.key]
if not USE_PHASE_OFFSET_INSTRUCTION:
shape *= np.exp(1j * wf.phase)
wfLib[wf_sig(wf)] = shape
return wfLib
def timestamp_entries(seq):
t = 0
for ct in range(len(seq)):
seq[ct].startTime = t
t += seq[ct].length
def synchronize_clocks(seqs):
# Control-flow instructions (CFIs) must occur at the same time on all channels.
# Therefore, we need to "reset the clock" by synchronizing the accumulated
# time at each CFI to the largest value on any channel
syncInstructions = [list(filter(
lambda s: isinstance(s, ControlFlow.ControlInstruction), seq))
for seq in seqs if seq]
# Add length to control-flow instructions to make accumulated time match at end of CFI.
# Keep running tally of how much each channel has been shifted so far.
localShift = [0 for _ in syncInstructions]
for ct in range(len(syncInstructions[0])):
step = [seq[ct] for seq in syncInstructions]
endTime = max((s.startTime + shift
for s, shift in zip(step, localShift)))
for ct, s in enumerate(step):
s.length = endTime - (s.startTime + localShift[ct])
# localShift[ct] += endTime - (s.startTime + localShift[ct])
# the += and the last term cancel, therefore:
localShift[ct] = endTime - s.startTime
# re-timestamp to propagate changes across the sequences
for seq in seqs:
timestamp_entries(seq)
# then transfer the control flow "lengths" back into start times
for seq in syncInstructions:
for instr in seq:
instr.startTime += instr.length
instr.length = 0
def create_seq_instructions(seqs, offsets, label = None):
'''
Helper function to create instruction vector from an IR sequence and an offset dictionary
keyed on the wf keys.
Seqs is a list of lists containing waveform and marker data, e.g.
[wfSeq & modulationSeq, m1Seq, m2Seq, m3Seq, m4Seq]
We take the strategy of greedily grabbing the next instruction that occurs in time, accross
all waveform and marker channels.
'''
# timestamp all entries before filtering (where we lose time information on control flow)
for seq in seqs:
timestamp_entries(seq)
synchronize_clocks(seqs)
# create (seq, startTime) pairs over all sequences
timeTuples = []
for ct, seq in enumerate(seqs):
timeTuples += [(entry.startTime, ct) for entry in seq]
timeTuples.sort()
# keep track of where we are in each sequence
indexes = np.zeros(len(seqs), dtype=np.int64)
# always start with SYNC (stealing label from beginning of sequence)
# unless it is a subroutine (using last entry as return as tell)
instructions = []
for ct, seq in enumerate(seqs):
if len(seq):
first_non_empty = ct
break
if not isinstance(seqs[first_non_empty][-1], ControlFlow.Return):
if isinstance(seqs[first_non_empty][0], BlockLabel.BlockLabel):
if not label:
label = seqs[first_non_empty][0]
timeTuples.pop(0)
indexes[first_non_empty] += 1
instructions.append(Sync(label=label))
label = None
while len(timeTuples) > 0:
#pop off all entries that have the same time
entries = []
start_time = 0
while True:
start_time, seq_idx = timeTuples.pop(0)
entries.append((seqs[seq_idx][indexes[seq_idx]], seq_idx))
indexes[seq_idx] += 1
next_start_time = timeTuples[0][0] if len(timeTuples) > 0 else -1
if start_time != next_start_time:
break
write_flags = [True] * len(entries)
for ct, (entry, seq_idx) in enumerate(entries):
#use first non empty sequence for control flow
if seq_idx == first_non_empty and (
isinstance(entry, ControlFlow.ControlInstruction) or
isinstance(entry, BlockLabel.BlockLabel) or
isinstance(entry, TdmInstructions.CustomInstruction) or
isinstance(entry, TdmInstructions.WriteAddrInstruction) or
isinstance(entry, TdmInstructions.LoadCmpVramInstruction)):
if isinstance(entry, BlockLabel.BlockLabel):
# carry label forward to next entry
label = entry
continue
# control flow instructions
elif isinstance(entry, ControlFlow.Wait):
instructions.append(Wait(label=label))
elif isinstance(entry, ControlFlow.LoadCmp):
instructions.append(LoadCmp(label=label))
elif isinstance(entry, ControlFlow.Sync):
instructions.append(Sync(label=label))
elif isinstance(entry, ControlFlow.Return):
instructions.append(Return(label=label))
# target argument commands
elif isinstance(entry, ControlFlow.Goto):
instructions.append(Goto(entry.target, label=label))
elif isinstance(entry, ControlFlow.Call):
instructions.append(Call(entry.target, label=label))
elif isinstance(entry, ControlFlow.Repeat):
instructions.append(Repeat(entry.target, label=label))
# value argument commands
elif isinstance(entry, ControlFlow.LoadRepeat):
instructions.append(Load(entry.value - 1, label=label))
elif isinstance(entry, ControlFlow.ComparisonInstruction):
# TODO modify Cmp operator to load from specified address
instructions.append(Cmp(CMPTABLE[entry.operator],
entry.value,
label=label))
elif isinstance(entry, TdmInstructions.LoadCmpVramInstruction) and entry.tdm == False:
instructions.append(LoadCmpVram(entry.addr, entry.mask, label=label))
# some TDM instructions are ignored by the APS
elif isinstance(entry, TdmInstructions.CustomInstruction):
pass
elif isinstance(entry, TdmInstructions.WriteAddrInstruction):
if entry.instruction == 'INVALIDATE' and entry.tdm == False:
instructions.append(Invalidate(entry.addr, entry.value, label=label))
continue
if seq_idx == 0:
#analog - waveforms or modulation
if isinstance(entry, Compiler.Waveform):
if entry.length < MIN_ENTRY_LENGTH:
warn("Dropping Waveform entry of length %s!" % entry.length)
continue
instructions.append(Waveform(
offsets[wf_sig(entry)], entry.length,
entry.isTimeAmp or entry.isZero,
write=write_flags[ct], label=label))
elif isinstance(entry, ModulationCommand):
instructions.append(entry.to_instruction(
write_flag=write_flags[ct],
label=label))
else: # a marker engine
if isinstance(entry, Compiler.Waveform):
if entry.length < MIN_ENTRY_LENGTH:
warn("Dropping entry!")
continue
markerSel = seq_idx - 1
state = not entry.isZero
instructions.append(Marker(markerSel,
state,
entry.length ,
write=write_flags[ct],
label=label))
#clear label
if len(timeTuples)>0:
label = None
return instructions, label
def create_instr_data(seqs, offsets, cache_lines):
'''
Constructs the complete instruction data vector, and does basic checks for validity.
Subroutines will be placed at least 8 cache lines away from sequences and aligned to cache line
'''
logger = logging.getLogger(__name__)
logger.debug('')
seq_instrs = []
need_prefetch = len(cache_lines) > 0
num_cache_lines = len(set(cache_lines))
cache_line_changes = np.concatenate(
([0], np.where(np.diff(cache_lines))[0] + 1))
label = None
for ct, seq in enumerate(zip_longest(*seqs, fillvalue=[])):
new_instrs, label = create_seq_instructions(list(seq), offsets[cache_lines[ct]]
if need_prefetch else offsets[0], label = label)
seq_instrs.append(new_instrs)
#if we need wf prefetching and have moved waveform cache lines then inject prefetch for the next line
if need_prefetch and (ct in cache_line_changes):
next_cache_line = cache_lines[cache_line_changes[(np.where(
ct == cache_line_changes)[0][0] + 1) % len(
cache_line_changes)]]
seq_instrs[-1].insert(0, WaveformPrefetch(int(
next_cache_line * WAVEFORM_CACHE_SIZE / 2)))
#steal label if necessary
if not seq_instrs[-1][0].label:
seq_instrs[-1][0].label = seq_instrs[-1][1].label
seq_instrs[-1][1].label = None
#concatenate instructions
instructions = []
subroutines_start = -1
for ct, seq in enumerate(seq_instrs):
#Use last instruction being return as mark of start of subroutines
try:
if (seq[-1].header >> 4) == RET:
subroutines_start = ct
break
except:
pass
instructions += seq
#if we have any subroutines then group in cache lines
if subroutines_start >= 0:
subroutine_instrs = []
subroutine_cache_line = {}
CACHE_LINE_LENGTH = 128
offset = 0
for sub in seq_instrs[subroutines_start:]:
#TODO for now we don't properly handle prefetching mulitple cache lines
if len(sub) > CACHE_LINE_LENGTH:
warnings.warn(
"Subroutines longer than {} instructions may not be prefetched correctly")
#Don't unecessarily split across a cache line
if (len(sub) + offset > CACHE_LINE_LENGTH) and (
len(sub) < CACHE_LINE_LENGTH):
pad_instrs = 128 - ((offset + 128) % 128)
subroutine_instrs += [NoOp()] * pad_instrs
offset = 0
if offset == 0:
line_label = sub[0].label
subroutine_cache_line[sub[0].label] = line_label
subroutine_instrs += sub
offset += len(sub) % CACHE_LINE_LENGTH
logger.debug("Placed {} subroutines into {} cache lines".format(
len(seq_instrs[subroutines_start:]), len(subroutine_instrs) //
CACHE_LINE_LENGTH))
#inject prefetch commands before waits
wait_idx = [idx for idx, instr in enumerate(instructions)
if (instr.header >> 4) == WAIT] + [len(instructions)]
instructions_with_prefetch = instructions[:wait_idx[0]]
last_prefetch = None
for start, stop in zip(wait_idx[:-1], wait_idx[1:]):
call_targets = [instr.target for instr in instructions[start:stop]
if (instr.header >> 4) == CALL]
needed_lines = set()
for target in call_targets:
needed_lines.add(subroutine_cache_line[target])
if len(needed_lines) > 8:
raise RuntimeError(
"Unable to prefetch more than 8 cache lines")
for needed_line in needed_lines:
if needed_line != last_prefetch:
instructions_with_prefetch.append(Prefetch(needed_line))
last_prefetch = needed_line
instructions_with_prefetch += instructions[start:stop]
instructions = instructions_with_prefetch
#pad out instruction vector to ensure circular cache never loads a subroutine
pad_instrs = 7 * 128 + (128 - ((len(instructions) + 128) % 128))
instructions += [NoOp()] * pad_instrs
instructions += subroutine_instrs
#turn symbols into integers addresses
resolve_symbols(instructions)
assert len(instructions) < MAX_NUM_INSTRUCTIONS, \
'Oops! too many instructions: {0}'.format(len(instructions))
return np.fromiter((instr.flatten() for instr in instructions), np.uint64,
len(instructions))
def resolve_symbols(seq):
symbols = {}
# create symbol look-up table
for ct, entry in enumerate(seq):
if entry.label and entry.label not in symbols:
symbols[entry.label] = ct
# then update
for (ct, entry) in enumerate(seq):
if entry.target:
# find next available label. The TDM may miss some labels if branches only contain waveforms (which are ignored)
for k in range(len(seq)-ct):
temp = seq[ct+k]
if temp.target in symbols:
break
entry.address = symbols[temp.target]
def compress_marker(markerLL):
'''
Compresses adjacent entries of the same state into single entries
'''
for seq in markerLL:
idx = 0
while idx + 1 < len(seq):
if (isinstance(seq[idx], Compiler.Waveform) and
isinstance(seq[idx + 1], Compiler.Waveform) and
seq[idx].isZero == seq[idx + 1].isZero):
seq[idx].length += seq[idx + 1].length
del seq[idx + 1]
else:
idx += 1
def write_sequence_file(awgData, fileName):
'''
Main function to pack channel sequences into an APS2 h5 file.
'''
# Convert QGL IR into a representation that is closer to the hardware.
awgData['ch1']['linkList'], wfLib = preprocess(
awgData['ch1']['linkList'], awgData['ch1']['wfLib'])
# compress marker data
for field in ['m1']:
if 'linkList' in awgData[field].keys():
PatternUtils.convert_lengths_to_samples(awgData[field]['linkList'],
SAMPLING_RATE, 1,
Compiler.Waveform)
compress_marker(awgData[field]['linkList'])
else:
awgData[field]['linkList'] = []
#Create the waveform vectors
wfInfo = []
wfInfo.append(create_wf_vector({key: wf.real
for key, wf in wfLib.items()}, awgData[
'ch1']['linkList']))
wfInfo.append(create_wf_vector({key: wf.imag
for key, wf in wfLib.items()}, awgData[
'ch1']['linkList']))
if SAVE_WF_OFFSETS:
#create a set of all waveform signatures in offset dictionaries
#we could have multiple offsets for the same pulse becuase it could
#be repeated in multiple cache lines
wf_sigs = set()
for offset_dict in wfInfo[0][1]:
wf_sigs |= set(offset_dict.keys())
#create dictionary linking entry labels (that's what we'll have later) with offsets
offsets = {}
for seq in awgData['ch1']['linkList']:
for entry in seq:
if len(wf_sigs) == 0:
break
if isinstance(entry, Compiler.Waveform):
sig = wf_sig(entry)
if sig in wf_sigs:
#store offsets and wavefor lib length
#time ampltidue entries are clamped to ADDRESS_UNIT
wf_length = ADDRESS_UNIT if entry.isTimeAmp else entry.length
offsets[entry.label] = ([_[sig] for _ in wfInfo[0][1]],
wf_length)
wf_sigs.discard(sig)
#break out of outer loop too
if len(wf_sigs) == 0:
break
#now pickle the label=>offsets
with open(os.path.splitext(fileName)[0] + ".offsets", "wb") as FID:
pickle.dump(offsets, FID)
# build instruction vector
seq_data = [awgData[s]['linkList']
for s in ['ch1', 'm1']]
instructions = create_instr_data(seq_data, wfInfo[0][1], wfInfo[0][2])
#Open the binary file
if os.path.isfile(fileName):
os.remove(fileName)
with open(fileName, 'wb') as FID:
FID.write(b'APS3') # target hardware
FID.write(np.float32(4.0).tobytes()) # Version
FID.write(np.float32(4.0).tobytes()) # minimum firmware version
FID.write(np.uint16(2).tobytes()) # number of channels
# FID.write(np.uint16([1, 2]).tobytes()) # channelDataFor
FID.write(np.uint64(instructions.size).tobytes()) # instructions length
FID.write(instructions.tobytes()) # instructions in uint64 form
#Create the groups and datasets
for chanct in range(2):
#Write the waveformLib to file
if wfInfo[chanct][0].size == 0:
#If there are no waveforms, ensure that there is some element
#so that the waveform group gets written to file.
#TODO: Fix this in libaps2
data = np.array([0], dtype=np.int16)
else:
data = wfInfo[chanct][0]
FID.write(np.uint64(data.size).tobytes()) # waveform data length for channel
FID.write(data.tobytes())
def read_sequence_file(fileName):
"""
Reads a HDF5 sequence file and returns a dictionary of lists.
Dictionary keys are channel strings such as ch1, m1
Lists are or tuples of time-amplitude pairs (time, output)
"""
chanStrs = ['ch1', 'ch2', 'm1', 'mod_phase']
seqs = {ch: [] for ch in chanStrs}
def start_new_seq():
for ct, ch in enumerate(chanStrs):
if (ct < 2) or (ct == 6):
#analog or modulation channel
seqs[ch].append([])
else:
#marker channel
seqs[ch].append([])
with open(fileName, 'rb') as FID:
target_hw = FID.read(4).decode('utf-8')
file_version = struct.unpack('<f', FID.read(4))[0]
min_fw = struct.unpack('<f', FID.read(4))[0]
num_chans = struct.unpack('<H', FID.read(2))[0]
inst_len = struct.unpack('<Q', FID.read(8))[0]
instructions = np.frombuffer(FID.read(8*inst_len), dtype=np.uint64)
wf_lib = {}
for i in range(num_chans):
wf_len = struct.unpack('<Q', FID.read(8))[0]
wf_dat = np.frombuffer(FID.read(2*wf_len), dtype=np.int16)
wf_lib[f'ch{i+1}'] = ( 1.0 / MAX_WAVEFORM_VALUE) * wf_dat.flatten()
NUM_NCO = 2
freq = np.zeros(NUM_NCO) #radians per timestep
phase = np.zeros(NUM_NCO)
frame = np.zeros(NUM_NCO)
next_freq = np.zeros(NUM_NCO)
next_phase = np.zeros(NUM_NCO)
next_frame = np.zeros(NUM_NCO)
accumulated_phase = np.zeros(NUM_NCO)
reset_flag = [False]*NUM_NCO
for data in instructions:
instr = Instruction.unflatten(data)
modulator_opcode = instr.payload >> 44
#update phases at these boundaries
if (instr.opcode == WAIT) | (instr.opcode == SYNC) | (
(instr.opcode) == MODULATION and (modulator_opcode == 0x0)):
for ct in range(NUM_NCO):
if reset_flag[ct]:
#would expect this to be zero but this is first non-zero point
accumulated_phase[ct] = next_freq[ct] * ADDRESS_UNIT
reset_flag[ct] = False
freq[:] = next_freq[:]
phase[:] = next_phase[:]
frame[:] = next_frame[:]
#Assume new sequence at every WAIT
if instr.opcode == WAIT:
start_new_seq()
elif instr.opcode == WFM and ((
(instr.payload >> WFM_OP_OFFSET) & 0x3) == PLAY):
addr = (instr.payload & 0x00ffffff) * ADDRESS_UNIT
count = (instr.payload >> 24) & 0xfffff
count = (count + 1) * ADDRESS_UNIT
isTA = (instr.payload >> 45) & 0x1
chan_select_bits = ((instr.header >> 2) & 0x1,
(instr.header >> 3) & 0x1)
#On older firmware we broadcast by default whereas on newer we respect the engine select
for chan, select_bit in zip(('ch1', 'ch2'), chan_select_bits):
if (file_version < 4) or select_bit:
if isTA:
seqs[chan][-1].append((count, wf_lib[chan][addr]))
else:
for sample in wf_lib[chan][addr:addr + count]:
seqs[chan][-1].append((1, sample))
elif instr.opcode == MARKER:
chan = 'm' + str(((instr.header >> 2) & 0x3) + 1)
count = instr.payload & 0xffffffff
count = (count + 1) * ADDRESS_UNIT
state = (instr.payload >> 32) & 0x1
seqs[chan][-1].append((count, state))
elif instr.opcode == MODULATION:
# modulator_op_code_map = {"MODULATE":0x0, "RESET_PHASE":0x2, "SET_FREQ":0x6, "SET_PHASE":0xa, "UPDATE_FRAME":0xe}
nco_select_bits = (instr.payload >> 40) & 0xf
if modulator_opcode == 0x0:
#modulate
count = ((instr.payload & 0xffffffff) + 1) * ADDRESS_UNIT
nco_select = {0b0001: 0,
0b0010: 1,
0b0100: 2,
0b1000: 3}[nco_select_bits]
seqs['mod_phase'][-1] = np.append(
seqs['mod_phase'][-1], freq[nco_select] *
np.arange(count) + accumulated_phase[nco_select] +
phase[nco_select] + frame[nco_select])
accumulated_phase += count * freq
else:
phase_rad = 2 * np.pi * (instr.payload &
0xffffffff) / 2**27
for ct in range(NUM_NCO):
if (nco_select_bits >> ct) & 0x1:
if modulator_opcode == 0x2:
#reset
next_phase[ct] = 0
next_frame[ct] = 0
reset_flag[ct] = True
elif modulator_opcode == 0x6:
#set frequency
next_freq[ct] = phase_rad / ADDRESS_UNIT
elif modulator_opcode == 0xa:
#set phase
next_phase[ct] = phase_rad
elif modulator_opcode == 0xe:
#update frame
next_frame[ct] += phase_rad
#Apply modulation if we have any
for ct, (
ch1, ch2, mod_phase
) in enumerate(zip(seqs['ch1'], seqs['ch2'], seqs['mod_phase'])):
if len(mod_phase):
#only really works if ch1, ch2 are broadcast together
mod_ch1 = []
mod_ch2 = []
cum_time = 0
for ((time_ch1, amp_ch1),
(time_ch2, amp_ch2)) in zip(ch1, ch2):
if (amp_ch1 != 0) or (amp_ch2 != 0):
assert time_ch1 == time_ch2
if time_ch1 == 1:
#single timestep
modulated = np.exp(1j * mod_phase[cum_time]) * (
amp_ch1 + 1j * amp_ch2)
mod_ch1.append((1, modulated.real))
mod_ch2.append((1, modulated.imag))
else:
#expand TA
modulated = np.exp(
1j *
mod_phase[cum_time:cum_time + time_ch1]) * (
amp_ch1 + 1j * amp_ch2)
for val in modulated:
mod_ch1.append((1, val.real))
mod_ch2.append((1, val.imag))
else:
mod_ch1.append((time_ch1, amp_ch1))
mod_ch2.append((time_ch2, amp_ch2))
cum_time += time_ch1
seqs['ch1'][ct] = mod_ch1
seqs['ch2'][ct] = mod_ch2
del seqs['mod_phase']
return seqs
def update_wf_library(filename, pulses, offsets):
"""
Update a H5 waveform library in place give an iterable of (pulseName, pulse)
tuples and offsets into the waveform library.
"""
assert USE_PHASE_OFFSET_INSTRUCTION == False
#load the h5 file
with h5py.File(filename) as FID:
for label, pulse in pulses.items():
#create a new waveform
if pulse.isTimeAmp:
shape = np.repeat(pulse.amp * np.exp(1j * pulse.phase), 4)
else:
shape = pulse.amp * np.exp(1j * pulse.phase) * pulse.shape
try:
length = offsets[label][1]
except KeyError:
print("\t{} not found in offsets so skipping".format(pulse))
continue
for offset in offsets[label][0]:
print("\tUpdating {} at offset {}".format(pulse, offset))
FID['/chan_1/waveforms'][offset:offset + length] = np.int16(
MAX_WAVEFORM_VALUE * shape.real)
FID['/chan_2/waveforms'][offset:offset + length] = np.int16(
MAX_WAVEFORM_VALUE * shape.imag)
def tdm_instructions(seqs):
"""
Generate the TDM instructions for the given sequence.
This assumes that there is one instruction sequence, not
a list of them (as is generally the case elsewhere). FIXME
"""
instructions = list()
label2addr = dict() # the backpatch table for labels
label = seqs[0][0]
for seq in seqs:
seq = list(flatten(copy(seq)))
#add sync at the beginning of the sequence. FIXME: for now, ignore subroutines. Assume that the first entry is a label
instructions.append(Sync(label=label))
label = None
for s in seq:
if isinstance(s, BlockLabel.BlockLabel):
#label2addr[s.label] = len(instructions) #FIXME this convert a label (A, B, ...) to the instruction number, i.e. the address (typically)
# carry label forward to next entry
label = s
continue
if isinstance(s, ControlFlow.Wait):
instructions.append(Wait(label=label))
elif isinstance(s, ControlFlow.LoadCmp):
instructions.append(LoadCmp(label=label))
elif isinstance(s, TdmInstructions.WriteAddrInstruction) and s.tdm == True:
if s.instruction == 'INVALIDATE':
print('o INVALIDATE(channel=%s, addr=0x%x, mask=0x%x)' %
(str(s.channel), s.addr, s.value))
instructions.append(Invalidate(s.addr, s.value, label=label))
elif s.instruction == 'WRITEADDR':
print('o WRITEADDR(channel=%s, addr=0x%x, value=0x%x)' %
(str(s.channel), s.addr, s.value))
instructions.append(WriteAddr(s.addr, s.value, label=label))
elif s.instruction == 'STOREMEAS':
print('STOREMEAS(channel=%s, addr=0x%x, mapping=0x%x)' %
(str(s.channel), s.addr, s.value))
instructions.append(StoreMeas(s.addr, s.value, label=label))
else: # TODO: add CrossBar (no need for explicit QGL call for TDM)
print('UNSUPPORTED WriteAddr: %s(channel=%s, addr=0x%x, val=0x%x)' %
(s.instruction, str(s.channel),
s.addr, s.value))
continue
elif isinstance(s, TdmInstructions.CustomInstruction):
if s.instruction == 'MAJORITY':
print('MAJORITY(in_addr=%x, out_addr=%x)' %
(s.in_addr, s.out_addr))
instructions.append(
MajorityVote(s.in_addr, s.out_addr, label=label))
elif s.instruction == 'MAJORITYMASK':
print('MAJORITYMASK(in_addr=%x, out_addr=%x)' %
(s.in_addr, s.out_addr))
instructions.append(
MajorityVoteMask(s.in_addr, s.out_addr, label=label))
elif s.instruction == 'TSM':
print('DECODE(in_addr=%x, out_addr=%x)' %
(s.in_addr, s.out_addr))
instructions.append(
Decode(s.in_addr, s.out_addr, label=label))
elif s.instruction == 'TSM_SET_ROUNDS':
print('DECODESETROUNDS(in_addr=%x, out_addr=%x)' %
(s.in_addr, s.out_addr))
instructions.append(
DecodeSetRounds(s.in_addr, s.out_addr, label=label))
else: #TODO: add decoder
print('UNSUPPORTED CUSTOM: %s(in_addr=0x%x, out_addr=0x%x)' %
(s.instruction, s.in_addr, s.out_addr))
elif isinstance(s, ControlFlow.Goto):
instructions.append(Goto(s.target, label=label))
elif isinstance(s, ControlFlow.Repeat):
instructions.append(Repeat(s.target, label=label))
elif isinstance(s, ControlFlow.LoadRepeat):
instructions.append(Load(s.value - 1, label=label))
elif isinstance(s, TdmInstructions.LoadCmpVramInstruction):
if s.instruction == 'LOADCMPVRAM' and s.tdm == True:
instructions.append(
LoadCmpVram(s.addr, s.mask, label=label))
elif isinstance(s, PulseSequencer.Pulse):
if s.label == 'MEAS' and s.maddr != (-1, 0):
instructions.append(CrossBar(2**s.maddr[1], 0x1, label=label))
instructions.append(LoadCmp(label=label))
instructions.append(StoreMeas(s.maddr[0], 1 << 16, label=label))
elif isinstance(s, PulseSequencer.PulseBlock):
sim_meas = []
for k in s.pulses:
if s.pulses[k].label == 'MEAS' and s.pulses[k].maddr != (-1, 0):
sim_meas.append(s.pulses[k])
if sim_meas:
maddr = [m.maddr[0] for m in sim_meas]
if len(set(maddr))>1:
raise Exception('Storing simultaneous measurements on different addresses not supported.')
for n,m in enumerate(sim_meas):
instructions.append(CrossBar(2**m.maddr[1], 2**n))
instructions.append(LoadCmp(label=label))
instructions.append(StoreMeas(maddr[0], 1 << 16))
elif isinstance(s, list):
# FIXME:
# If this happens, we are confused.
print('FIXME: TDM GOT LIST: %s' % str(s))
elif isinstance(s, ControlFlow.ComparisonInstruction):
instructions.append(
Cmp(CMPTABLE[s.operator], s.value, label=label))
else:
pass
# use this for debugging purposes
#print('OOPS: unhandled [%s]' % str(type(s)))
resolve_symbols(instructions)
return np.fromiter((instr.flatten() for instr in instructions), np.uint64,
len(instructions))
def write_tdm_seq(seq, tdm_fileName):
#Open the HDF5 file
if os.path.isfile(tdm_fileName):
os.remove(tdm_fileName)
with h5py.File(tdm_fileName, 'w') as FID:
FID['/'].attrs['Version'] = 5.0
FID['/'].attrs['target hardware'] = 'APS3'
FID['/'].attrs['minimum firmware version'] = 5.0
FID['/'].attrs['channelDataFor'] = np.uint16([1, 2])
#Create the groups and datasets
for chanct in range(2):
chanStr = '/chan_{0}'.format(chanct + 1)
chanGroup = FID.create_group(chanStr)
FID.create_dataset(chanStr + '/waveforms', data=np.uint16([]))
#Write the instructions to channel 1
if np.mod(chanct, 2) == 0:
FID.create_dataset(chanStr + '/instructions', data=seq)
# Utility Functions for displaying programs
def raw_instructions(fileName):
with open(fileName, 'rb') as FID:
target_hw = FID.read(4).decode('utf-8')
file_version = struct.unpack('<f', FID.read(4))[0]
min_fw = struct.unpack('<f', FID.read(4))[0]
num_chans = struct.unpack('<H', FID.read(2))[0]
inst_len = struct.unpack('<Q', FID.read(8))[0]
instructions = np.frombuffer(FID.read(8*inst_len), dtype=np.uint64)
return instructions
def decompile_instructions(instructions, tdm = False):
return [Instruction.unflatten(x, decode_as_tdm = tdm) for x in instructions]
def read_instructions(filename):
raw_instrs = raw_instructions(filename)
return decompile_instructions(raw_instrs)
def read_waveforms(filename):
with open(filename, 'rb') as FID:
target_hw = FID.read(4).decode('utf-8')
file_version = struct.unpack('<f', FID.read(4))[0]
min_fw = struct.unpack('<f', FID.read(4))[0]
num_chans = struct.unpack('<H', FID.read(2))[0]
inst_len = struct.unpack('<Q', FID.read(8))[0]
instructions = np.frombuffer(FID.read(8*inst_len), dtype=np.uint64)
wf_dat = []
for i in range(num_chans):
wf_len = struct.unpack('<Q', FID.read(8))[0]
dat = ( 1.0 / MAX_WAVEFORM_VALUE) * np.frombuffer(FID.read(2*wf_len), dtype=np.int16).flatten()
wf_dat.append(dat)
return wf_dat
def replace_instructions(filename, instructions, channel = 1):
channelStr = get_channel_instructions_string(channel)
with h5py.File(filename, 'r+') as fid:
del fid[channelStr]
fid.create_dataset(channelStr, data=instructions)
def display_decompiled_file(filename, tdm = False):
raw = raw_instructions(filename)
display_decompiled_instructions(raw, tdm)
def display_decompiled_instructions(raw, tdm = False, display_op_codes = True):
cmds = decompile_instructions(raw, tdm)
opcodeStr = ''
for i,a in enumerate(zip(raw,cmds)):
x,y = a
if display_op_codes:
opcodeStr = "0x{:016x} - ".format(x)
print("{:5}: {}{}".format(i, opcodeStr,y))
def display_raw_instructions(raw):
for x in raw:
print("0x{:016x}".format(x))
def display_raw_file(filename):
raw = raw_instructions(filename)
display_raw_instructions(raw)
if __name__ == '__main__':
if len(sys.argv) == 2:
from PyQt5.QtWidgets import QMainWindow, QApplication, QWidget, QTableWidget, QTableWidgetItem, QVBoxLayout, QAbstractItemView, QPushButton
from PyQt5.QtGui import QIcon, QColor, QFont
from matplotlib.backends.backend_qt4agg import FigureCanvasQTAgg
from matplotlib.figure import Figure
table_font = QFont("Arial", weight=QFont.Bold)
colors = {"WFM": QColor(0,200,0),
"GOTO": QColor(0,100,100),
"MARKER": QColor(150,150,200)}
class MatplotlibWidget(QWidget):
def __init__(self, I, Q, parent=None):
super(MatplotlibWidget, self).__init__(parent)
self.title = 'Waveform'
self.left = 100
self.top = 100
self.width = 800
self.height = 600
self.setWindowTitle(self.title)
self.setGeometry(self.left, self.top, self.width, self.height)
self.figure = Figure()
self.canvas = FigureCanvasQTAgg(self.figure)
self.axis = self.figure.add_subplot(111)
self.axis.plot(I)
self.axis.plot(Q)
self.layout = QVBoxLayout(self)
self.layout.addWidget(self.canvas)
self.setLayout(self.layout)
self.canvas.draw()
self.show()
class App(QWidget):
COLUMN_COUNT = 7
def __init__(self, instructions, waveforms):
super().__init__()
self.title = 'APS3 Disassembled Instructions'
self.left = 100
self.top = 100
self.width = 1000
self.height = 1200
self.instructions = instructions
self.waveforms = waveforms
print(self.waveforms)
self.initUI()
self.plotters = []
def initUI(self):
self.setWindowTitle(self.title)
self.setGeometry(self.left, self.top, self.width, self.height)
self.createTable()
self.layout = QVBoxLayout()
self.layout.addWidget(self.tableWidget)
self.setLayout(self.layout)
# Show widget
self.show()
def createTable(self):
# Create table
self.tableWidget = QTableWidget()
self.tableWidget.setRowCount(len(self.instructions))
self.tableWidget.setColumnCount(7)
for k, instr in enumerate(self.instructions):
fields = str(instr).replace(',','').replace(';', '').split(" ")
if "|" in fields:
fields.remove("|")
if fields[0] in colors:
color = colors[fields[0]]
else:
color = None
for l, f in enumerate(fields):
text = fields[l]
if text == "GOTO":
btn = QPushButton(self.tableWidget)
btn.setText('GOTO')
target_row = int(fields[1].split("=")[1])
def scroll_to_goto_target(row=target_row, tab=self.tableWidget):
tab.scrollToItem(tab.item(row, 0))
btn.clicked.connect(scroll_to_goto_target)
self.tableWidget.setCellWidget(k, l, btn)
if text == "WFM" and int(fields[4].split("=")[1])==0:
# Not a TA pair
btn = QPushButton(self.tableWidget)
btn.setText('WFM')
addr = int(fields[6].split("=")[1])
count = int(fields[5].split("=")[1])
def open_plotter(addr=None, I=self.waveforms[0][addr:addr+count], Q=self.waveforms[1][addr:addr+count]):
w = MatplotlibWidget(I,Q)
self.plotters.append(w)
btn.clicked.connect(open_plotter)
self.tableWidget.setCellWidget(k, l, btn)
else:
item = QTableWidgetItem(text)
item.setFont(table_font)
if color:
item.setBackground(color)
self.tableWidget.setItem(k, l, item)
if l < self.COLUMN_COUNT-1:
for j in range(l+1, self.COLUMN_COUNT):
item = QTableWidgetItem("")
if color:
item.setBackground(color)
self.tableWidget.setItem(k, j, item)
self.tableWidget.move(0,0)
self.tableWidget.setSelectionBehavior(QAbstractItemView.SelectRows)
app = QApplication(sys.argv[:1])
ex = App(read_instructions(sys.argv[1]), read_waveforms(sys.argv[1]))
sys.exit(app.exec_())
| apache-2.0 |
frank-tancf/scikit-learn | sklearn/ensemble/__init__.py | 153 | 1382 | """
The :mod:`sklearn.ensemble` module includes ensemble-based methods for
classification, regression and anomaly detection.
"""
from .base import BaseEnsemble
from .forest import RandomForestClassifier
from .forest import RandomForestRegressor
from .forest import RandomTreesEmbedding
from .forest import ExtraTreesClassifier
from .forest import ExtraTreesRegressor
from .bagging import BaggingClassifier
from .bagging import BaggingRegressor
from .iforest import IsolationForest
from .weight_boosting import AdaBoostClassifier
from .weight_boosting import AdaBoostRegressor
from .gradient_boosting import GradientBoostingClassifier
from .gradient_boosting import GradientBoostingRegressor
from .voting_classifier import VotingClassifier
from . import bagging
from . import forest
from . import weight_boosting
from . import gradient_boosting
from . import partial_dependence
__all__ = ["BaseEnsemble",
"RandomForestClassifier", "RandomForestRegressor",
"RandomTreesEmbedding", "ExtraTreesClassifier",
"ExtraTreesRegressor", "BaggingClassifier",
"BaggingRegressor", "IsolationForest", "GradientBoostingClassifier",
"GradientBoostingRegressor", "AdaBoostClassifier",
"AdaBoostRegressor", "VotingClassifier",
"bagging", "forest", "gradient_boosting",
"partial_dependence", "weight_boosting"]
| bsd-3-clause |
Leminen/project_template_deeplearning | src/models/logreg_example.py | 1 | 7774 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Oct 10 16:43:52 2017
@author: leminen
"""
import os
import tensorflow as tf
import matplotlib.pyplot as plt
import datetime
import argparse
import shlex
import src.utils as utils
import src.data.util_data as util_data
def hparams_parser_train(hparams_string):
parser = argparse.ArgumentParser()
parser.add_argument('--epoch_max',
type=int, default='100',
help='Max number of epochs to run')
parser.add_argument('--batch_size',
type=int, default='64',
help='Number of samples in each batch')
## add more model parameters to enable configuration from terminal
return parser.parse_args(shlex.split(hparams_string))
def hparams_parser_evaluate(hparams_string):
parser = argparse.ArgumentParser()
parser.add_argument('--epoch_no',
type=int,
default=None,
help='Epoch no to reload')
## add more model parameters to enable configuration from terminal
return parser.parse_args(shlex.split(hparams_string))
class logreg_example(object):
def __init__(self, dataset, id):
self.model = 'logreg_example'
if id != None:
self.model = self.model + '_' + id
self.dir_base = 'models/' + self.model
self.dir_logs = self.dir_base + '/logs'
self.dir_checkpoints = self.dir_base + '/checkpoints'
self.dir_results = self.dir_base + '/results'
utils.checkfolder(self.dir_checkpoints)
utils.checkfolder(self.dir_logs)
utils.checkfolder(self.dir_results)
# Specify valid dataset for model
if dataset == 'MNIST':
self.dateset_filenames = ['data/processed/MNIST/train.tfrecord']
self.lbl_dim = 10
else:
raise ValueError('Selected Dataset is not supported by model: logreg_example')
def _create_inference(self, inputs):
""" Define the inference model for the network
Args:
Returns:
"""
X = tf.reshape(inputs,[-1,784])
w = tf.Variable(tf.random_normal(shape=[784, 10], stddev=0.01), name='weights')
b = tf.Variable(tf.zeros([1, 10]), name="bias")
outputs = tf.matmul(X, w) + b
return outputs
def _create_losses(self, outputs, labels):
""" Define loss function[s] for the network
Args:
Returns:
"""
entropy = tf.nn.softmax_cross_entropy_with_logits(logits=outputs, labels=labels, name='loss')
loss = tf.reduce_mean(entropy) # computes the mean over all the examples in the batch
return loss
def _create_optimizer(self, loss):
""" Create optimizer for the network
Args:
Returns:
"""
optimizer = tf.train.AdamOptimizer(learning_rate = 0.01)
optimizer_op = optimizer.minimize(loss)
return optimizer_op
def _create_summaries(self, loss):
""" Create summaries for the network
Args:
Returns:
"""
### Add summaries
with tf.name_scope("summaries"):
tf.summary.scalar('model_loss', loss) # placeholder summary
summary_op = tf.summary.merge_all()
return summary_op
def train(self, hparams_string):
""" Run training of the network
Args:
Returns:
"""
args_train = hparams_parser_train(hparams_string)
batch_size = args_train.batch_size
epoch_max = args_train.epoch_max
utils.save_model_configuration(args_train, self.dir_base)
# Use dataset for loading in datasamples from .tfrecord (https://www.tensorflow.org/programmers_guide/datasets#consuming_tfrecord_data)
# The iterator will get a new batch from the dataset each time a sess.run() is executed on the graph.
dataset = tf.data.TFRecordDataset(self.dateset_filenames)
dataset = dataset.map(util_data.decode_image) # decoding the tfrecord
dataset = dataset.map(self._preProcessData) # potential local preprocessing of data
dataset = dataset.shuffle(buffer_size = 10000, seed = None)
dataset = dataset.batch(batch_size = batch_size)
iterator = dataset.make_initializable_iterator()
inputs = iterator.get_next()
# depends on self._preProcessData
[in_image, in_label] = inputs
# show network architecture
utils.show_all_variables()
# define model, loss, optimizer and summaries.
outputs = self._create_inference(in_image)
loss = self._create_losses(outputs, in_label)
optimizer_op = self._create_optimizer(loss)
summary_op = self._create_summaries(loss)
with tf.Session() as sess:
# Initialize all model Variables.
sess.run(tf.global_variables_initializer())
# Create Saver object for loading and storing checkpoints
saver = tf.train.Saver()
# Create Writer object for storing graph and summaries for TensorBoard
writer = tf.summary.FileWriter(self.dir_logs, sess.graph)
# Reload Tensor values from latest checkpoint
ckpt = tf.train.get_checkpoint_state(self.dir_checkpoints)
epoch_start = 0
if ckpt and ckpt.model_checkpoint_path:
saver.restore(sess, ckpt.model_checkpoint_path)
ckpt_name = os.path.basename(ckpt.model_checkpoint_path)
epoch_start = int(ckpt_name.split('-')[-1])
interationCnt = 0
# Do training loops
for epoch_n in range(epoch_start, epoch_max):
# Initiate or Re-initiate iterator
sess.run(iterator.initializer)
# Test model output before any training
if epoch_n == 0:
summary = sess.run(summary_op)
writer.add_summary(summary, global_step=-1)
utils.show_message('Running training epoch no: {0}'.format(epoch_n))
while True:
try:
_, summary = sess.run([optimizer_op, summary_op])
writer.add_summary(summary, global_step=interationCnt)
counter =+ 1
except tf.errors.OutOfRangeError:
# Do some evaluation after each Epoch
break
if epoch_n % 1 == 0:
saver.save(sess,os.path.join(self.dir_checkpoints, self.model + '.model'), global_step=epoch_n)
def evaluate(self, hparams_string):
""" Run prediction of the network
Args:
Returns:
"""
args_evaluate = hparams_parser_evaluate(hparams_string)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
def _preProcessData(self, image_proto, lbl_proto, class_proto, height_proto, width_proto, channels_proto, origin_proto):
""" Local preprocessing of data from dataset
also used to select which elements to parse onto the model
Args:
all outputs of util_data.decode_image
Returns:
"""
image = image_proto
label = tf.one_hot(lbl_proto, self.lbl_dim)
return image, label | mit |
skudriashev/incubator-airflow | airflow/hooks/presto_hook.py | 15 | 3549 | # -*- coding: utf-8 -*-
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from builtins import str
from pyhive import presto
from pyhive.exc import DatabaseError
from airflow.hooks.dbapi_hook import DbApiHook
class PrestoException(Exception):
pass
class PrestoHook(DbApiHook):
"""
Interact with Presto through PyHive!
>>> ph = PrestoHook()
>>> sql = "SELECT count(1) AS num FROM airflow.static_babynames"
>>> ph.get_records(sql)
[[340698]]
"""
conn_name_attr = 'presto_conn_id'
default_conn_name = 'presto_default'
def get_conn(self):
"""Returns a connection object"""
db = self.get_connection(self.presto_conn_id)
return presto.connect(
host=db.host,
port=db.port,
username=db.login,
catalog=db.extra_dejson.get('catalog', 'hive'),
schema=db.schema)
@staticmethod
def _strip_sql(sql):
return sql.strip().rstrip(';')
def _get_pretty_exception_message(self, e):
"""
Parses some DatabaseError to provide a better error message
"""
if (hasattr(e, 'message')
and 'errorName' in e.message
and 'message' in e.message):
return ('{name}: {message}'.format(
name=e.message['errorName'],
message=e.message['message']))
else:
return str(e)
def get_records(self, hql, parameters=None):
"""
Get a set of records from Presto
"""
try:
return super(PrestoHook, self).get_records(
self._strip_sql(hql), parameters)
except DatabaseError as e:
raise PrestoException(self._parse_exception_message(e))
def get_first(self, hql, parameters=None):
"""
Returns only the first row, regardless of how many rows the query
returns.
"""
try:
return super(PrestoHook, self).get_first(
self._strip_sql(hql), parameters)
except DatabaseError as e:
raise PrestoException(self._parse_exception_message(e))
def get_pandas_df(self, hql, parameters=None):
"""
Get a pandas dataframe from a sql query.
"""
import pandas
cursor = self.get_cursor()
try:
cursor.execute(self._strip_sql(hql), parameters)
data = cursor.fetchall()
except DatabaseError as e:
raise PrestoException(self._parse_exception_message(e))
column_descriptions = cursor.description
if data:
df = pandas.DataFrame(data)
df.columns = [c[0] for c in column_descriptions]
else:
df = pandas.DataFrame()
return df
def run(self, hql, parameters=None):
"""
Execute the statement against Presto. Can be used to create views.
"""
return super(PrestoHook, self).run(self._strip_sql(hql), parameters)
def insert_rows(self):
raise NotImplementedError()
| apache-2.0 |
rsutormin/narrative | src/notebook/ipython_profiles/profile_narrative_local/ipython_notebook_config.py | 4 | 19256 | import os
import inspect
import biokbase.narrative.monkeypatch as monkeypatch
# Configuration file for ipython-notebook.
c = get_config()
#------------------------------------------------------------------------------
# NotebookApp configuration
#------------------------------------------------------------------------------
# NotebookApp will inherit config from: BaseIPythonApplication, Application
# The IPython profile to use.
# c.NotebookApp.profile = u'default'
# The url for MathJax.js.
# c.NotebookApp.mathjax_url = ''
# The IP address the notebook server will listen on.
# c.NotebookApp.ip = '127.0.0.1'
# The base URL for the notebook server
# c.NotebookApp.base_project_url = '/'
# Create a massive crash report when IPython encounters what may be an internal
# error. The default is to append a short message to the usual traceback
# c.NotebookApp.verbose_crash = False
# The number of additional ports to try if the specified port is not available.
# c.NotebookApp.port_retries = 50
# Whether to install the default config files into the profile dir. If a new
# profile is being created, and IPython contains config files for that profile,
# then they will be staged into the new directory. Otherwise, default config
# files will be automatically generated.
# c.NotebookApp.copy_config_files = False
# The base URL for the kernel server
# c.NotebookApp.base_kernel_url = '/'
# The port the notebook server will listen on.
# c.NotebookApp.port = 8888
# Whether to overwrite existing config files when copying
# c.NotebookApp.overwrite = False
# Whether to prevent editing/execution of notebooks.
# c.NotebookApp.read_only = False
# Whether to enable MathJax for typesetting math/TeX
#
# MathJax is the javascript library IPython uses to render math/LaTeX. It is
# very large, so you may want to disable it if you have a slow internet
# connection, or for offline use of the notebook.
#
# When disabled, equations etc. will appear as their untransformed TeX source.
# c.NotebookApp.enable_mathjax = True
# Whether to open in a browser after starting. The specific browser used is
# platform dependent and determined by the python standard library `webbrowser`
# module, unless it is overridden using the --browser (NotebookApp.browser)
# configuration option.
# c.NotebookApp.open_browser = True
# The full path to an SSL/TLS certificate file.
# c.NotebookApp.certfile = u''
# The hostname for the websocket server.
# c.NotebookApp.websocket_host = ''
# The name of the IPython directory. This directory is used for logging
# configuration (through profiles), history storage, etc. The default is usually
# $HOME/.ipython. This options can also be specified through the environment
# variable IPYTHONDIR.
# c.NotebookApp.ipython_dir = u'/Users/sychan/.ipython'
# Set the log level by value or name.
# c.NotebookApp.log_level = 20
# Hashed password to use for web authentication.
#
# To generate, type in a python/IPython shell:
#
# from IPython.lib import passwd; passwd()
#
# The string should be of the form type:salt:hashed-password.
# c.NotebookApp.password = u''
# The Logging format template
# c.NotebookApp.log_format = '[%(name)s] %(message)s'
# The full path to a private key file for usage with SSL/TLS.
# c.NotebookApp.keyfile = u''
# Supply overrides for the tornado.web.Application that the IPython notebook
# uses.
# c.NotebookApp.webapp_settings = {}
# Supply overrides for the tornado.web.Application that the IPython notebook
# uses.
#c.NotebookApp.webapp_settings = { 'template_path': '/Users/wjriehl/Projects/kbase/narrative/src/ipythondir/profile_narrative/kbase_templates',
# 'static_path': '/Users/wjriehl/Projects/kbase/narrative/src/ipythondir/profile_narrative/kbase_templates/static' }
try:
myfile = __file__
except NameError:
myfile = os.path.abspath(inspect.getsourcefile(lambda _: None))
myfile = os.path.dirname( myfile)
c.NotebookApp.webapp_settings = { 'template_path': os.path.join(myfile,"kbase_templates"),
'static_path': os.path.join(myfile,"kbase_templates","static"),
'debug' : True }
c.NotebookApp.kbase_auth = True
# Specify what command to use to invoke a web browser when opening the notebook.
# If not specified, the default browser will be determined by the `webbrowser`
# standard library module, which allows setting of the BROWSER environment
# variable to override it.
# c.NotebookApp.browser = u''
#------------------------------------------------------------------------------
# IPKernelApp configuration
#------------------------------------------------------------------------------
# IPython: an enhanced interactive Python shell.
# IPKernelApp will inherit config from: KernelApp, BaseIPythonApplication,
# Application, InteractiveShellApp
# The importstring for the DisplayHook factory
# c.IPKernelApp.displayhook_class = 'IPython.zmq.displayhook.ZMQDisplayHook'
# Set the IP or interface on which the kernel will listen.
# c.IPKernelApp.ip = '127.0.0.1'
#
# c.IPKernelApp.parent_appname = u''
# Create a massive crash report when IPython encounters what may be an internal
# error. The default is to append a short message to the usual traceback
# c.IPKernelApp.verbose_crash = False
# Run the module as a script.
# c.IPKernelApp.module_to_run = ''
# set the shell (ROUTER) port [default: random]
# c.IPKernelApp.shell_port = 0
# Whether to overwrite existing config files when copying
# c.IPKernelApp.overwrite = False
# Execute the given command string.
# c.IPKernelApp.code_to_run = ''
# set the stdin (DEALER) port [default: random]
# c.IPKernelApp.stdin_port = 0
# Set the log level by value or name.
# c.IPKernelApp.log_level = 30
# lines of code to run at IPython startup.
c.IPKernelApp.exec_lines = [ 'import biokbase.narrative.magics']
# The importstring for the OutStream factory
# c.IPKernelApp.outstream_class = 'IPython.zmq.iostream.OutStream'
# Whether to create profile dir if it doesn't exist
# c.IPKernelApp.auto_create = False
# set the heartbeat port [default: random]
# c.IPKernelApp.hb_port = 0
# redirect stdout to the null device
# c.IPKernelApp.no_stdout = False
# dotted module name of an IPython extension to load.
# c.IPKernelApp.extra_extension = ''
# A file to be run
# c.IPKernelApp.file_to_run = ''
# The IPython profile to use.
# c.IPKernelApp.profile = u'default'
# Pre-load matplotlib and numpy for interactive use, selecting a particular
# matplotlib backend and loop integration.
# c.IPKernelApp.pylab = None
# kill this process if its parent dies. On Windows, the argument specifies the
# HANDLE of the parent process, otherwise it is simply boolean.
# c.IPKernelApp.parent = 0
# JSON file in which to store connection info [default: kernel-<pid>.json]
#
# This file will contain the IP, ports, and authentication key needed to connect
# clients to this kernel. By default, this file will be created in the security-
# dir of the current profile, but can be specified by absolute path.
# c.IPKernelApp.connection_file = ''
# If true, an 'import *' is done from numpy and pylab, when using pylab
# c.IPKernelApp.pylab_import_all = True
# The name of the IPython directory. This directory is used for logging
# configuration (through profiles), history storage, etc. The default is usually
# $HOME/.ipython. This options can also be specified through the environment
# variable IPYTHONDIR.
# c.IPKernelApp.ipython_dir = u'/Users/sychan/.ipython'
# ONLY USED ON WINDOWS Interrupt this process when the parent is signalled.
# c.IPKernelApp.interrupt = 0
# Whether to install the default config files into the profile dir. If a new
# profile is being created, and IPython contains config files for that profile,
# then they will be staged into the new directory. Otherwise, default config
# files will be automatically generated.
# c.IPKernelApp.copy_config_files = False
# List of files to run at IPython startup.
# c.IPKernelApp.exec_files = []
# Enable GUI event loop integration ('qt', 'wx', 'gtk', 'glut', 'pyglet',
# 'osx').
# c.IPKernelApp.gui = None
# A list of dotted module names of IPython extensions to load.
#c.IPKernelApp.extensions = []
# redirect stderr to the null device
# c.IPKernelApp.no_stderr = False
# The Logging format template
# c.IPKernelApp.log_format = '[%(name)s] %(message)s'
# set the iopub (PUB) port [default: random]
# c.IPKernelApp.iopub_port = 0
#------------------------------------------------------------------------------
# ZMQInteractiveShell configuration
#------------------------------------------------------------------------------
# A subclass of InteractiveShell for ZMQ.
# ZMQInteractiveShell will inherit config from: InteractiveShell
# Use colors for displaying information about objects. Because this information
# is passed through a pager (like 'less'), and some pagers get confused with
# color codes, this capability can be turned off.
# c.ZMQInteractiveShell.color_info = True
#
# c.ZMQInteractiveShell.history_length = 10000
# Don't call post-execute functions that have failed in the past.
# c.ZMQInteractiveShell.disable_failing_post_execute = False
# Show rewritten input, e.g. for autocall.
# c.ZMQInteractiveShell.show_rewritten_input = True
# Set the color scheme (NoColor, Linux, or LightBG).
# c.ZMQInteractiveShell.colors = 'LightBG'
#
# c.ZMQInteractiveShell.separate_in = '\n'
# Deprecated, use PromptManager.in2_template
# c.ZMQInteractiveShell.prompt_in2 = ' .\\D.: '
#
# c.ZMQInteractiveShell.separate_out = ''
# Deprecated, use PromptManager.in_template
# c.ZMQInteractiveShell.prompt_in1 = 'In [\\#]: '
# Enable deep (recursive) reloading by default. IPython can use the deep_reload
# module which reloads changes in modules recursively (it replaces the reload()
# function, so you don't need to change anything to use it). deep_reload()
# forces a full reload of modules whose code may have changed, which the default
# reload() function does not. When deep_reload is off, IPython will use the
# normal reload(), but deep_reload will still be available as dreload().
# c.ZMQInteractiveShell.deep_reload = False
# Make IPython automatically call any callable object even if you didn't type
# explicit parentheses. For example, 'str 43' becomes 'str(43)' automatically.
# The value can be '0' to disable the feature, '1' for 'smart' autocall, where
# it is not applied if there are no more arguments on the line, and '2' for
# 'full' autocall, where all callable objects are automatically called (even if
# no arguments are present).
# c.ZMQInteractiveShell.autocall = 0
#
# c.ZMQInteractiveShell.separate_out2 = ''
# Deprecated, use PromptManager.justify
# c.ZMQInteractiveShell.prompts_pad_left = True
#
# c.ZMQInteractiveShell.readline_parse_and_bind = ['tab: complete', '"\\C-l": clear-screen', 'set show-all-if-ambiguous on', '"\\C-o": tab-insert', '"\\C-r": reverse-search-history', '"\\C-s": forward-search-history', '"\\C-p": history-search-backward', '"\\C-n": history-search-forward', '"\\e[A": history-search-backward', '"\\e[B": history-search-forward', '"\\C-k": kill-line', '"\\C-u": unix-line-discard']
# Enable magic commands to be called without the leading %.
# c.ZMQInteractiveShell.automagic = True
#
# c.ZMQInteractiveShell.debug = False
#
# c.ZMQInteractiveShell.object_info_string_level = 0
#
# c.ZMQInteractiveShell.ipython_dir = ''
#
# c.ZMQInteractiveShell.readline_remove_delims = '-/~'
# Start logging to the default log file.
# c.ZMQInteractiveShell.logstart = False
# The name of the logfile to use.
# c.ZMQInteractiveShell.logfile = ''
#
# c.ZMQInteractiveShell.wildcards_case_sensitive = True
# Save multi-line entries as one entry in readline history
# c.ZMQInteractiveShell.multiline_history = True
# Start logging to the given file in append mode.
# c.ZMQInteractiveShell.logappend = ''
#
# c.ZMQInteractiveShell.xmode = 'Context'
#
# c.ZMQInteractiveShell.quiet = False
# Deprecated, use PromptManager.out_template
# c.ZMQInteractiveShell.prompt_out = 'Out[\\#]: '
# Set the size of the output cache. The default is 1000, you can change it
# permanently in your config file. Setting it to 0 completely disables the
# caching system, and the minimum value accepted is 20 (if you provide a value
# less than 20, it is reset to 0 and a warning is issued). This limit is
# defined because otherwise you'll spend more time re-flushing a too small cache
# than working
# c.ZMQInteractiveShell.cache_size = 1000
# 'all', 'last', 'last_expr' or 'none', specifying which nodes should be run
# interactively (displaying output from expressions).
# c.ZMQInteractiveShell.ast_node_interactivity = 'last_expr'
# Automatically call the pdb debugger after every exception.
# c.ZMQInteractiveShell.pdb = False
#------------------------------------------------------------------------------
# ProfileDir configuration
#------------------------------------------------------------------------------
# An object to manage the profile directory and its resources.
#
# The profile directory is used by all IPython applications, to manage
# configuration, logging and security.
#
# This object knows how to find, create and manage these directories. This
# should be used by any code that wants to handle profiles.
# Set the profile location directly. This overrides the logic used by the
# `profile` option.
# c.ProfileDir.location = u''
#------------------------------------------------------------------------------
# Session configuration
#------------------------------------------------------------------------------
# Object for handling serialization and sending of messages.
#
# The Session object handles building messages and sending them with ZMQ sockets
# or ZMQStream objects. Objects can communicate with each other over the
# network via Session objects, and only need to work with the dict-based IPython
# message spec. The Session will handle serialization/deserialization, security,
# and metadata.
#
# Sessions support configurable serialiization via packer/unpacker traits, and
# signing with HMAC digests via the key/keyfile traits.
#
# Parameters ----------
#
# debug : bool
# whether to trigger extra debugging statements
# packer/unpacker : str : 'json', 'pickle' or import_string
# importstrings for methods to serialize message parts. If just
# 'json' or 'pickle', predefined JSON and pickle packers will be used.
# Otherwise, the entire importstring must be used.
#
# The functions must accept at least valid JSON input, and output *bytes*.
#
# For example, to use msgpack:
# packer = 'msgpack.packb', unpacker='msgpack.unpackb'
# pack/unpack : callables
# You can also set the pack/unpack callables for serialization directly.
# session : bytes
# the ID of this Session object. The default is to generate a new UUID.
# username : unicode
# username added to message headers. The default is to ask the OS.
# key : bytes
# The key used to initialize an HMAC signature. If unset, messages
# will not be signed or checked.
# keyfile : filepath
# The file containing a key. If this is set, `key` will be initialized
# to the contents of the file.
# Username for the Session. Default is your system username.
# c.Session.username = 'sychan'
# The name of the packer for serializing messages. Should be one of 'json',
# 'pickle', or an import name for a custom callable serializer.
# c.Session.packer = 'json'
# The UUID identifying this session.
# c.Session.session = u''
# execution key, for extra authentication.
# c.Session.key = ''
# Debug output in the Session
# c.Session.debug = False
# The name of the unpacker for unserializing messages. Only used with custom
# functions for `packer`.
# c.Session.unpacker = 'json'
# path to file containing execution key.
# c.Session.keyfile = ''
#------------------------------------------------------------------------------
# InlineBackend configuration
#------------------------------------------------------------------------------
# An object to store configuration of the inline backend.
# The image format for figures with the inline backend.
# c.InlineBackend.figure_format = 'png'
# Close all figures at the end of each cell.
#
# When True, ensures that each cell starts with no active figures, but it also
# means that one must keep track of references in order to edit or redraw
# figures in subsequent cells. This mode is ideal for the notebook, where
# residual plots from other cells might be surprising.
#
# When False, one must call figure() to create new figures. This means that
# gcf() and getfigs() can reference figures created in other cells, and the
# active figure can continue to be edited with pylab/pyplot methods that
# reference the current active figure. This mode facilitates iterative editing
# of figures, and behaves most consistently with other matplotlib backends, but
# figure barriers between cells must be explicit.
# c.InlineBackend.close_figures = True
# Subset of matplotlib rcParams that should be different for the inline backend.
# c.InlineBackend.rc = {'font.size': 10, 'savefig.dpi': 72, 'figure.figsize': (6.0, 4.0), 'figure.subplot.bottom': 0.125}
#------------------------------------------------------------------------------
# MappingKernelManager configuration
#------------------------------------------------------------------------------
# A KernelManager that handles notebok mapping and HTTP error handling
# MappingKernelManager will inherit config from: MultiKernelManager
# The max raw message size accepted from the browser over a WebSocket
# connection.
# c.MappingKernelManager.max_msg_size = 65536
# Kernel heartbeat interval in seconds.
# c.MappingKernelManager.time_to_dead = 3.0
# The kernel manager class. This is configurable to allow subclassing of the
# KernelManager for customized behavior.
# c.MappingKernelManager.kernel_manager_class = 'IPython.zmq.blockingkernelmanager.BlockingKernelManager'
# Delay (in seconds) before sending first heartbeat.
# c.MappingKernelManager.first_beat = 5.0
#------------------------------------------------------------------------------
# NotebookManager configuration
#------------------------------------------------------------------------------
# Automatically create a Python script when saving the notebook.
#
# For easier use of import, %run and %load across notebooks, a <notebook-
# name>.py script will be created next to any <notebook-name>.ipynb on each
# save. This can also be set with the short `--script` flag.
# c.NotebookManager.save_script = False
# The directory to use for notebooks.
# c.NotebookManager.notebook_dir = u'/Users/sychan/src/kbase/dev_container/modules/narrative/src'
#----------------------
# Tweaks for running notebook
#----------------------
c.IPKernelApp.pylab = 'inline'
# Do the monkeypatching after we have declared our configs. The monkeypatching code should be
# checking for config directives that control what to patch. For example if
# NotebookApp.kbase_auth = True then the monkeypatch code should patch all the notebook app
# handlers to enforce and pass along kbase auth tokens
monkeypatch.do_patching(c)
| mit |
shahankhatch/scikit-learn | examples/ensemble/plot_voting_decision_regions.py | 230 | 2386 | """
==================================================
Plot the decision boundaries of a VotingClassifier
==================================================
Plot the decision boundaries of a `VotingClassifier` for
two features of the Iris dataset.
Plot the class probabilities of the first sample in a toy dataset
predicted by three different classifiers and averaged by the
`VotingClassifier`.
First, three examplary classifiers are initialized (`DecisionTreeClassifier`,
`KNeighborsClassifier`, and `SVC`) and used to initialize a
soft-voting `VotingClassifier` with weights `[2, 1, 2]`, which means that
the predicted probabilities of the `DecisionTreeClassifier` and `SVC`
count 5 times as much as the weights of the `KNeighborsClassifier` classifier
when the averaged probability is calculated.
"""
print(__doc__)
from itertools import product
import numpy as np
import matplotlib.pyplot as plt
from sklearn import datasets
from sklearn.tree import DecisionTreeClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.ensemble import VotingClassifier
# Loading some example data
iris = datasets.load_iris()
X = iris.data[:, [0, 2]]
y = iris.target
# Training classifiers
clf1 = DecisionTreeClassifier(max_depth=4)
clf2 = KNeighborsClassifier(n_neighbors=7)
clf3 = SVC(kernel='rbf', probability=True)
eclf = VotingClassifier(estimators=[('dt', clf1), ('knn', clf2),
('svc', clf3)],
voting='soft', weights=[2, 1, 2])
clf1.fit(X, y)
clf2.fit(X, y)
clf3.fit(X, y)
eclf.fit(X, y)
# Plotting decision regions
x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.1),
np.arange(y_min, y_max, 0.1))
f, axarr = plt.subplots(2, 2, sharex='col', sharey='row', figsize=(10, 8))
for idx, clf, tt in zip(product([0, 1], [0, 1]),
[clf1, clf2, clf3, eclf],
['Decision Tree (depth=4)', 'KNN (k=7)',
'Kernel SVM', 'Soft Voting']):
Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])
Z = Z.reshape(xx.shape)
axarr[idx[0], idx[1]].contourf(xx, yy, Z, alpha=0.4)
axarr[idx[0], idx[1]].scatter(X[:, 0], X[:, 1], c=y, alpha=0.8)
axarr[idx[0], idx[1]].set_title(tt)
plt.show()
| bsd-3-clause |
Sentient07/scikit-learn | sklearn/linear_model/tests/test_sag.py | 45 | 28228 | # Authors: Danny Sullivan <[email protected]>
# Tom Dupre la Tour <[email protected]>
#
# License: BSD 3 clause
import math
import numpy as np
import scipy.sparse as sp
from sklearn.linear_model.sag import get_auto_step_size
from sklearn.linear_model.sag_fast import _multinomial_grad_loss_all_samples
from sklearn.linear_model import LogisticRegression, Ridge
from sklearn.linear_model.base import make_dataset
from sklearn.linear_model.logistic import _multinomial_loss_grad
from sklearn.utils.extmath import logsumexp
from sklearn.utils.extmath import row_norms
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_greater
from sklearn.utils.testing import assert_raise_message
from sklearn.utils.testing import ignore_warnings
from sklearn.utils import compute_class_weight
from sklearn.utils import check_random_state
from sklearn.preprocessing import LabelEncoder, LabelBinarizer
from sklearn.datasets import make_blobs, load_iris
from sklearn.base import clone
iris = load_iris()
# this is used for sag classification
def log_dloss(p, y):
z = p * y
# approximately equal and saves the computation of the log
if z > 18.0:
return math.exp(-z) * -y
if z < -18.0:
return -y
return -y / (math.exp(z) + 1.0)
def log_loss(p, y):
return np.mean(np.log(1. + np.exp(-y * p)))
# this is used for sag regression
def squared_dloss(p, y):
return p - y
def squared_loss(p, y):
return np.mean(0.5 * (p - y) * (p - y))
# function for measuring the log loss
def get_pobj(w, alpha, myX, myy, loss):
w = w.ravel()
pred = np.dot(myX, w)
p = loss(pred, myy)
p += alpha * w.dot(w) / 2.
return p
def sag(X, y, step_size, alpha, n_iter=1, dloss=None, sparse=False,
sample_weight=None, fit_intercept=True):
n_samples, n_features = X.shape[0], X.shape[1]
weights = np.zeros(X.shape[1])
sum_gradient = np.zeros(X.shape[1])
gradient_memory = np.zeros((n_samples, n_features))
intercept = 0.0
intercept_sum_gradient = 0.0
intercept_gradient_memory = np.zeros(n_samples)
rng = np.random.RandomState(77)
decay = 1.0
seen = set()
# sparse data has a fixed decay of .01
if sparse:
decay = .01
for epoch in range(n_iter):
for k in range(n_samples):
idx = int(rng.rand(1) * n_samples)
# idx = k
entry = X[idx]
seen.add(idx)
p = np.dot(entry, weights) + intercept
gradient = dloss(p, y[idx])
if sample_weight is not None:
gradient *= sample_weight[idx]
update = entry * gradient + alpha * weights
sum_gradient += update - gradient_memory[idx]
gradient_memory[idx] = update
if fit_intercept:
intercept_sum_gradient += (gradient -
intercept_gradient_memory[idx])
intercept_gradient_memory[idx] = gradient
intercept -= (step_size * intercept_sum_gradient
/ len(seen) * decay)
weights -= step_size * sum_gradient / len(seen)
return weights, intercept
def sag_sparse(X, y, step_size, alpha, n_iter=1,
dloss=None, sample_weight=None, sparse=False,
fit_intercept=True):
if step_size * alpha == 1.:
raise ZeroDivisionError("Sparse sag does not handle the case "
"step_size * alpha == 1")
n_samples, n_features = X.shape[0], X.shape[1]
weights = np.zeros(n_features)
sum_gradient = np.zeros(n_features)
last_updated = np.zeros(n_features, dtype=np.int)
gradient_memory = np.zeros(n_samples)
rng = np.random.RandomState(77)
intercept = 0.0
intercept_sum_gradient = 0.0
wscale = 1.0
decay = 1.0
seen = set()
c_sum = np.zeros(n_iter * n_samples)
# sparse data has a fixed decay of .01
if sparse:
decay = .01
counter = 0
for epoch in range(n_iter):
for k in range(n_samples):
# idx = k
idx = int(rng.rand(1) * n_samples)
entry = X[idx]
seen.add(idx)
if counter >= 1:
for j in range(n_features):
if last_updated[j] == 0:
weights[j] -= c_sum[counter - 1] * sum_gradient[j]
else:
weights[j] -= ((c_sum[counter - 1] -
c_sum[last_updated[j] - 1]) *
sum_gradient[j])
last_updated[j] = counter
p = (wscale * np.dot(entry, weights)) + intercept
gradient = dloss(p, y[idx])
if sample_weight is not None:
gradient *= sample_weight[idx]
update = entry * gradient
sum_gradient += update - (gradient_memory[idx] * entry)
if fit_intercept:
intercept_sum_gradient += gradient - gradient_memory[idx]
intercept -= (step_size * intercept_sum_gradient
/ len(seen) * decay)
gradient_memory[idx] = gradient
wscale *= (1.0 - alpha * step_size)
if counter == 0:
c_sum[0] = step_size / (wscale * len(seen))
else:
c_sum[counter] = (c_sum[counter - 1] +
step_size / (wscale * len(seen)))
if counter >= 1 and wscale < 1e-9:
for j in range(n_features):
if last_updated[j] == 0:
weights[j] -= c_sum[counter] * sum_gradient[j]
else:
weights[j] -= ((c_sum[counter] -
c_sum[last_updated[j] - 1]) *
sum_gradient[j])
last_updated[j] = counter + 1
c_sum[counter] = 0
weights *= wscale
wscale = 1.0
counter += 1
for j in range(n_features):
if last_updated[j] == 0:
weights[j] -= c_sum[counter - 1] * sum_gradient[j]
else:
weights[j] -= ((c_sum[counter - 1] -
c_sum[last_updated[j] - 1]) *
sum_gradient[j])
weights *= wscale
return weights, intercept
def get_step_size(X, alpha, fit_intercept, classification=True):
if classification:
return (4.0 / (np.max(np.sum(X * X, axis=1))
+ fit_intercept + 4.0 * alpha))
else:
return 1.0 / (np.max(np.sum(X * X, axis=1)) + fit_intercept + alpha)
@ignore_warnings
def test_classifier_matching():
n_samples = 20
X, y = make_blobs(n_samples=n_samples, centers=2, random_state=0,
cluster_std=0.1)
y[y == 0] = -1
alpha = 1.1
n_iter = 80
fit_intercept = True
step_size = get_step_size(X, alpha, fit_intercept)
clf = LogisticRegression(solver="sag", fit_intercept=fit_intercept,
tol=1e-11, C=1. / alpha / n_samples,
max_iter=n_iter, random_state=10)
clf.fit(X, y)
weights, intercept = sag_sparse(X, y, step_size, alpha, n_iter=n_iter,
dloss=log_dloss,
fit_intercept=fit_intercept)
weights2, intercept2 = sag(X, y, step_size, alpha, n_iter=n_iter,
dloss=log_dloss,
fit_intercept=fit_intercept)
weights = np.atleast_2d(weights)
intercept = np.atleast_1d(intercept)
weights2 = np.atleast_2d(weights2)
intercept2 = np.atleast_1d(intercept2)
assert_array_almost_equal(weights, clf.coef_, decimal=10)
assert_array_almost_equal(intercept, clf.intercept_, decimal=10)
assert_array_almost_equal(weights2, clf.coef_, decimal=10)
assert_array_almost_equal(intercept2, clf.intercept_, decimal=10)
@ignore_warnings
def test_regressor_matching():
n_samples = 10
n_features = 5
rng = np.random.RandomState(10)
X = rng.normal(size=(n_samples, n_features))
true_w = rng.normal(size=n_features)
y = X.dot(true_w)
alpha = 1.
n_iter = 100
fit_intercept = True
step_size = get_step_size(X, alpha, fit_intercept, classification=False)
clf = Ridge(fit_intercept=fit_intercept, tol=.00000000001, solver='sag',
alpha=alpha * n_samples, max_iter=n_iter)
clf.fit(X, y)
weights1, intercept1 = sag_sparse(X, y, step_size, alpha, n_iter=n_iter,
dloss=squared_dloss,
fit_intercept=fit_intercept)
weights2, intercept2 = sag(X, y, step_size, alpha, n_iter=n_iter,
dloss=squared_dloss,
fit_intercept=fit_intercept)
assert_array_almost_equal(weights1, clf.coef_, decimal=10)
assert_array_almost_equal(intercept1, clf.intercept_, decimal=10)
assert_array_almost_equal(weights2, clf.coef_, decimal=10)
assert_array_almost_equal(intercept2, clf.intercept_, decimal=10)
@ignore_warnings
def test_sag_pobj_matches_logistic_regression():
"""tests if the sag pobj matches log reg"""
n_samples = 100
alpha = 1.0
max_iter = 20
X, y = make_blobs(n_samples=n_samples, centers=2, random_state=0,
cluster_std=0.1)
clf1 = LogisticRegression(solver='sag', fit_intercept=False, tol=.0000001,
C=1. / alpha / n_samples, max_iter=max_iter,
random_state=10)
clf2 = clone(clf1)
clf3 = LogisticRegression(fit_intercept=False, tol=.0000001,
C=1. / alpha / n_samples, max_iter=max_iter,
random_state=10)
clf1.fit(X, y)
clf2.fit(sp.csr_matrix(X), y)
clf3.fit(X, y)
pobj1 = get_pobj(clf1.coef_, alpha, X, y, log_loss)
pobj2 = get_pobj(clf2.coef_, alpha, X, y, log_loss)
pobj3 = get_pobj(clf3.coef_, alpha, X, y, log_loss)
assert_array_almost_equal(pobj1, pobj2, decimal=4)
assert_array_almost_equal(pobj2, pobj3, decimal=4)
assert_array_almost_equal(pobj3, pobj1, decimal=4)
@ignore_warnings
def test_sag_pobj_matches_ridge_regression():
"""tests if the sag pobj matches ridge reg"""
n_samples = 100
n_features = 10
alpha = 1.0
n_iter = 100
fit_intercept = False
rng = np.random.RandomState(10)
X = rng.normal(size=(n_samples, n_features))
true_w = rng.normal(size=n_features)
y = X.dot(true_w)
clf1 = Ridge(fit_intercept=fit_intercept, tol=.00000000001, solver='sag',
alpha=alpha, max_iter=n_iter, random_state=42)
clf2 = clone(clf1)
clf3 = Ridge(fit_intercept=fit_intercept, tol=.00001, solver='lsqr',
alpha=alpha, max_iter=n_iter, random_state=42)
clf1.fit(X, y)
clf2.fit(sp.csr_matrix(X), y)
clf3.fit(X, y)
pobj1 = get_pobj(clf1.coef_, alpha, X, y, squared_loss)
pobj2 = get_pobj(clf2.coef_, alpha, X, y, squared_loss)
pobj3 = get_pobj(clf3.coef_, alpha, X, y, squared_loss)
assert_array_almost_equal(pobj1, pobj2, decimal=4)
assert_array_almost_equal(pobj1, pobj3, decimal=4)
assert_array_almost_equal(pobj3, pobj2, decimal=4)
@ignore_warnings
def test_sag_regressor_computed_correctly():
"""tests if the sag regressor is computed correctly"""
alpha = .1
n_features = 10
n_samples = 40
max_iter = 50
tol = .000001
fit_intercept = True
rng = np.random.RandomState(0)
X = rng.normal(size=(n_samples, n_features))
w = rng.normal(size=n_features)
y = np.dot(X, w) + 2.
step_size = get_step_size(X, alpha, fit_intercept, classification=False)
clf1 = Ridge(fit_intercept=fit_intercept, tol=tol, solver='sag',
alpha=alpha * n_samples, max_iter=max_iter)
clf2 = clone(clf1)
clf1.fit(X, y)
clf2.fit(sp.csr_matrix(X), y)
spweights1, spintercept1 = sag_sparse(X, y, step_size, alpha,
n_iter=max_iter,
dloss=squared_dloss,
fit_intercept=fit_intercept)
spweights2, spintercept2 = sag_sparse(X, y, step_size, alpha,
n_iter=max_iter,
dloss=squared_dloss, sparse=True,
fit_intercept=fit_intercept)
assert_array_almost_equal(clf1.coef_.ravel(),
spweights1.ravel(),
decimal=3)
assert_almost_equal(clf1.intercept_, spintercept1, decimal=1)
# TODO: uncomment when sparse Ridge with intercept will be fixed (#4710)
#assert_array_almost_equal(clf2.coef_.ravel(),
# spweights2.ravel(),
# decimal=3)
#assert_almost_equal(clf2.intercept_, spintercept2, decimal=1)'''
@ignore_warnings
def test_get_auto_step_size():
X = np.array([[1, 2, 3], [2, 3, 4], [2, 3, 2]], dtype=np.float64)
alpha = 1.2
fit_intercept = False
# sum the squares of the second sample because that's the largest
max_squared_sum = 4 + 9 + 16
max_squared_sum_ = row_norms(X, squared=True).max()
assert_almost_equal(max_squared_sum, max_squared_sum_, decimal=4)
for fit_intercept in (True, False):
step_size_sqr = 1.0 / (max_squared_sum + alpha + int(fit_intercept))
step_size_log = 4.0 / (max_squared_sum + 4.0 * alpha +
int(fit_intercept))
step_size_sqr_ = get_auto_step_size(max_squared_sum_, alpha, "squared",
fit_intercept)
step_size_log_ = get_auto_step_size(max_squared_sum_, alpha, "log",
fit_intercept)
assert_almost_equal(step_size_sqr, step_size_sqr_, decimal=4)
assert_almost_equal(step_size_log, step_size_log_, decimal=4)
msg = 'Unknown loss function for SAG solver, got wrong instead of'
assert_raise_message(ValueError, msg, get_auto_step_size,
max_squared_sum_, alpha, "wrong", fit_intercept)
@ignore_warnings
def test_sag_regressor():
"""tests if the sag regressor performs well"""
xmin, xmax = -5, 5
n_samples = 20
tol = .001
max_iter = 20
alpha = 0.1
rng = np.random.RandomState(0)
X = np.linspace(xmin, xmax, n_samples).reshape(n_samples, 1)
# simple linear function without noise
y = 0.5 * X.ravel()
clf1 = Ridge(tol=tol, solver='sag', max_iter=max_iter,
alpha=alpha * n_samples)
clf2 = clone(clf1)
clf1.fit(X, y)
clf2.fit(sp.csr_matrix(X), y)
score1 = clf1.score(X, y)
score2 = clf2.score(X, y)
assert_greater(score1, 0.99)
assert_greater(score2, 0.99)
# simple linear function with noise
y = 0.5 * X.ravel() + rng.randn(n_samples, 1).ravel()
clf1 = Ridge(tol=tol, solver='sag', max_iter=max_iter,
alpha=alpha * n_samples)
clf2 = clone(clf1)
clf1.fit(X, y)
clf2.fit(sp.csr_matrix(X), y)
score1 = clf1.score(X, y)
score2 = clf2.score(X, y)
score2 = clf2.score(X, y)
assert_greater(score1, 0.5)
assert_greater(score2, 0.5)
@ignore_warnings
def test_sag_classifier_computed_correctly():
"""tests if the binary classifier is computed correctly"""
alpha = .1
n_samples = 50
n_iter = 50
tol = .00001
fit_intercept = True
X, y = make_blobs(n_samples=n_samples, centers=2, random_state=0,
cluster_std=0.1)
step_size = get_step_size(X, alpha, fit_intercept, classification=True)
classes = np.unique(y)
y_tmp = np.ones(n_samples)
y_tmp[y != classes[1]] = -1
y = y_tmp
clf1 = LogisticRegression(solver='sag', C=1. / alpha / n_samples,
max_iter=n_iter, tol=tol, random_state=77,
fit_intercept=fit_intercept)
clf2 = clone(clf1)
clf1.fit(X, y)
clf2.fit(sp.csr_matrix(X), y)
spweights, spintercept = sag_sparse(X, y, step_size, alpha, n_iter=n_iter,
dloss=log_dloss,
fit_intercept=fit_intercept)
spweights2, spintercept2 = sag_sparse(X, y, step_size, alpha,
n_iter=n_iter,
dloss=log_dloss, sparse=True,
fit_intercept=fit_intercept)
assert_array_almost_equal(clf1.coef_.ravel(),
spweights.ravel(),
decimal=2)
assert_almost_equal(clf1.intercept_, spintercept, decimal=1)
assert_array_almost_equal(clf2.coef_.ravel(),
spweights2.ravel(),
decimal=2)
assert_almost_equal(clf2.intercept_, spintercept2, decimal=1)
@ignore_warnings
def test_sag_multiclass_computed_correctly():
"""tests if the multiclass classifier is computed correctly"""
alpha = .1
n_samples = 20
tol = .00001
max_iter = 40
fit_intercept = True
X, y = make_blobs(n_samples=n_samples, centers=3, random_state=0,
cluster_std=0.1)
step_size = get_step_size(X, alpha, fit_intercept, classification=True)
classes = np.unique(y)
clf1 = LogisticRegression(solver='sag', C=1. / alpha / n_samples,
max_iter=max_iter, tol=tol, random_state=77,
fit_intercept=fit_intercept)
clf2 = clone(clf1)
clf1.fit(X, y)
clf2.fit(sp.csr_matrix(X), y)
coef1 = []
intercept1 = []
coef2 = []
intercept2 = []
for cl in classes:
y_encoded = np.ones(n_samples)
y_encoded[y != cl] = -1
spweights1, spintercept1 = sag_sparse(X, y_encoded, step_size, alpha,
dloss=log_dloss, n_iter=max_iter,
fit_intercept=fit_intercept)
spweights2, spintercept2 = sag_sparse(X, y_encoded, step_size, alpha,
dloss=log_dloss, n_iter=max_iter,
sparse=True,
fit_intercept=fit_intercept)
coef1.append(spweights1)
intercept1.append(spintercept1)
coef2.append(spweights2)
intercept2.append(spintercept2)
coef1 = np.vstack(coef1)
intercept1 = np.array(intercept1)
coef2 = np.vstack(coef2)
intercept2 = np.array(intercept2)
for i, cl in enumerate(classes):
assert_array_almost_equal(clf1.coef_[i].ravel(),
coef1[i].ravel(),
decimal=2)
assert_almost_equal(clf1.intercept_[i], intercept1[i], decimal=1)
assert_array_almost_equal(clf2.coef_[i].ravel(),
coef2[i].ravel(),
decimal=2)
assert_almost_equal(clf2.intercept_[i], intercept2[i], decimal=1)
@ignore_warnings
def test_classifier_results():
"""tests if classifier results match target"""
alpha = .1
n_features = 20
n_samples = 10
tol = .01
max_iter = 200
rng = np.random.RandomState(0)
X = rng.normal(size=(n_samples, n_features))
w = rng.normal(size=n_features)
y = np.dot(X, w)
y = np.sign(y)
clf1 = LogisticRegression(solver='sag', C=1. / alpha / n_samples,
max_iter=max_iter, tol=tol, random_state=77)
clf2 = clone(clf1)
clf1.fit(X, y)
clf2.fit(sp.csr_matrix(X), y)
pred1 = clf1.predict(X)
pred2 = clf2.predict(X)
assert_almost_equal(pred1, y, decimal=12)
assert_almost_equal(pred2, y, decimal=12)
@ignore_warnings
def test_binary_classifier_class_weight():
"""tests binary classifier with classweights for each class"""
alpha = .1
n_samples = 50
n_iter = 20
tol = .00001
fit_intercept = True
X, y = make_blobs(n_samples=n_samples, centers=2, random_state=10,
cluster_std=0.1)
step_size = get_step_size(X, alpha, fit_intercept, classification=True)
classes = np.unique(y)
y_tmp = np.ones(n_samples)
y_tmp[y != classes[1]] = -1
y = y_tmp
class_weight = {1: .45, -1: .55}
clf1 = LogisticRegression(solver='sag', C=1. / alpha / n_samples,
max_iter=n_iter, tol=tol, random_state=77,
fit_intercept=fit_intercept,
class_weight=class_weight)
clf2 = clone(clf1)
clf1.fit(X, y)
clf2.fit(sp.csr_matrix(X), y)
le = LabelEncoder()
class_weight_ = compute_class_weight(class_weight, np.unique(y), y)
sample_weight = class_weight_[le.fit_transform(y)]
spweights, spintercept = sag_sparse(X, y, step_size, alpha, n_iter=n_iter,
dloss=log_dloss,
sample_weight=sample_weight,
fit_intercept=fit_intercept)
spweights2, spintercept2 = sag_sparse(X, y, step_size, alpha,
n_iter=n_iter,
dloss=log_dloss, sparse=True,
sample_weight=sample_weight,
fit_intercept=fit_intercept)
assert_array_almost_equal(clf1.coef_.ravel(),
spweights.ravel(),
decimal=2)
assert_almost_equal(clf1.intercept_, spintercept, decimal=1)
assert_array_almost_equal(clf2.coef_.ravel(),
spweights2.ravel(),
decimal=2)
assert_almost_equal(clf2.intercept_, spintercept2, decimal=1)
@ignore_warnings
def test_multiclass_classifier_class_weight():
"""tests multiclass with classweights for each class"""
alpha = .1
n_samples = 20
tol = .00001
max_iter = 50
class_weight = {0: .45, 1: .55, 2: .75}
fit_intercept = True
X, y = make_blobs(n_samples=n_samples, centers=3, random_state=0,
cluster_std=0.1)
step_size = get_step_size(X, alpha, fit_intercept, classification=True)
classes = np.unique(y)
clf1 = LogisticRegression(solver='sag', C=1. / alpha / n_samples,
max_iter=max_iter, tol=tol, random_state=77,
fit_intercept=fit_intercept,
class_weight=class_weight)
clf2 = clone(clf1)
clf1.fit(X, y)
clf2.fit(sp.csr_matrix(X), y)
le = LabelEncoder()
class_weight_ = compute_class_weight(class_weight, np.unique(y), y)
sample_weight = class_weight_[le.fit_transform(y)]
coef1 = []
intercept1 = []
coef2 = []
intercept2 = []
for cl in classes:
y_encoded = np.ones(n_samples)
y_encoded[y != cl] = -1
spweights1, spintercept1 = sag_sparse(X, y_encoded, step_size, alpha,
n_iter=max_iter, dloss=log_dloss,
sample_weight=sample_weight)
spweights2, spintercept2 = sag_sparse(X, y_encoded, step_size, alpha,
n_iter=max_iter, dloss=log_dloss,
sample_weight=sample_weight,
sparse=True)
coef1.append(spweights1)
intercept1.append(spintercept1)
coef2.append(spweights2)
intercept2.append(spintercept2)
coef1 = np.vstack(coef1)
intercept1 = np.array(intercept1)
coef2 = np.vstack(coef2)
intercept2 = np.array(intercept2)
for i, cl in enumerate(classes):
assert_array_almost_equal(clf1.coef_[i].ravel(),
coef1[i].ravel(),
decimal=2)
assert_almost_equal(clf1.intercept_[i], intercept1[i], decimal=1)
assert_array_almost_equal(clf2.coef_[i].ravel(),
coef2[i].ravel(),
decimal=2)
assert_almost_equal(clf2.intercept_[i], intercept2[i], decimal=1)
def test_classifier_single_class():
"""tests if ValueError is thrown with only one class"""
X = [[1, 2], [3, 4]]
y = [1, 1]
assert_raise_message(ValueError,
"This solver needs samples of at least 2 classes "
"in the data",
LogisticRegression(solver='sag').fit,
X, y)
def test_step_size_alpha_error():
X = [[0, 0], [0, 0]]
y = [1, -1]
fit_intercept = False
alpha = 1.
msg = ("Current sag implementation does not handle the case"
" step_size * alpha_scaled == 1")
clf1 = LogisticRegression(solver='sag', C=1. / alpha,
fit_intercept=fit_intercept)
assert_raise_message(ZeroDivisionError, msg, clf1.fit, X, y)
clf2 = Ridge(fit_intercept=fit_intercept, solver='sag', alpha=alpha)
assert_raise_message(ZeroDivisionError, msg, clf2.fit, X, y)
def test_multinomial_loss():
# test if the multinomial loss and gradient computations are consistent
X, y = iris.data, iris.target.astype(np.float64)
n_samples, n_features = X.shape
n_classes = len(np.unique(y))
rng = check_random_state(42)
weights = rng.randn(n_features, n_classes)
intercept = rng.randn(n_classes)
sample_weights = rng.randn(n_samples)
np.abs(sample_weights, sample_weights)
# compute loss and gradient like in multinomial SAG
dataset, _ = make_dataset(X, y, sample_weights, random_state=42)
loss_1, grad_1 = _multinomial_grad_loss_all_samples(dataset, weights,
intercept, n_samples,
n_features, n_classes)
# compute loss and gradient like in multinomial LogisticRegression
lbin = LabelBinarizer()
Y_bin = lbin.fit_transform(y)
weights_intercept = np.vstack((weights, intercept)).T.ravel()
loss_2, grad_2, _ = _multinomial_loss_grad(weights_intercept, X, Y_bin,
0.0, sample_weights)
grad_2 = grad_2.reshape(n_classes, -1)
grad_2 = grad_2[:, :-1].T
# comparison
assert_array_almost_equal(grad_1, grad_2)
assert_almost_equal(loss_1, loss_2)
def test_multinomial_loss_ground_truth():
# n_samples, n_features, n_classes = 4, 2, 3
n_classes = 3
X = np.array([[1.1, 2.2], [2.2, -4.4], [3.3, -2.2], [1.1, 1.1]])
y = np.array([0, 1, 2, 0])
lbin = LabelBinarizer()
Y_bin = lbin.fit_transform(y)
weights = np.array([[0.1, 0.2, 0.3], [1.1, 1.2, -1.3]])
intercept = np.array([1., 0, -.2])
sample_weights = np.array([0.8, 1, 1, 0.8])
prediction = np.dot(X, weights) + intercept
logsumexp_prediction = logsumexp(prediction, axis=1)
p = prediction - logsumexp_prediction[:, np.newaxis]
loss_1 = -(sample_weights[:, np.newaxis] * p * Y_bin).sum()
diff = sample_weights[:, np.newaxis] * (np.exp(p) - Y_bin)
grad_1 = np.dot(X.T, diff)
weights_intercept = np.vstack((weights, intercept)).T.ravel()
loss_2, grad_2, _ = _multinomial_loss_grad(weights_intercept, X, Y_bin,
0.0, sample_weights)
grad_2 = grad_2.reshape(n_classes, -1)
grad_2 = grad_2[:, :-1].T
assert_almost_equal(loss_1, loss_2)
assert_array_almost_equal(grad_1, grad_2)
# ground truth
loss_gt = 11.680360354325961
grad_gt = np.array([[-0.557487, -1.619151, +2.176638],
[-0.903942, +5.258745, -4.354803]])
assert_almost_equal(loss_1, loss_gt)
assert_array_almost_equal(grad_1, grad_gt)
| bsd-3-clause |
dimatura/pydisp | pydisp/pydisp.py | 1 | 5469 | # -*- coding: utf-8 -*-
import cStringIO as StringIO
import base64
import json
import uuid
import os
from PIL import Image
import matplotlib as mpl
import matplotlib.cm as cm
import numpy as np
import requests
__all__ = ['image',
'dyplot',
'send',
'text',
'pylab',
'pane',
'b64_encode',
'is_valid_image_mime_type',
'CONFIG',
]
VALID_IMAGE_MIME_TYPES = {'png','gif','bmp','webp','jpeg'}
class CONFIG(object):
PORT = 8000
HOSTNAME = 'localhost'
@staticmethod
def load_config():
# TODO what is the right way (TM)
fname = os.path.join(os.environ['HOME'], '.display', 'config.json')
if os.path.exists(fname):
with open(fname, 'r') as f:
cfg = json.load(f)
CONFIG.PORT = int(cfg['port'])
CONFIG.HOSTNAME = cfg['hostname']
@staticmethod
def display_url():
return "http://{:s}:{:d}/events".format(CONFIG.HOSTNAME, CONFIG.PORT)
CONFIG.load_config()
def send(**command):
""" send command to server """
command = json.dumps(command)
headers = {'Content-Type': 'application/text'}
req = requests.post(CONFIG.display_url(), headers=headers, data=command.encode('ascii'))
resp = req.content
return resp is not None
def uid():
""" return a unique id for a pane """
return 'pane_{}'.format(uuid.uuid4())
def pane(panetype, win, title, content):
""" create a pane (formerly window) """
if win is None:
win = uid()
send(command='pane', type=panetype, id=win, title=title, content=content)
return win
def is_valid_image_mime_type(mt):
return mt in VALID_IMAGE_MIME_TYPES
def scalar_preprocess(img, **kwargs):
""" vmin, vmax, clip, cmap """
vmin = kwargs.get('vmin')
vmax = kwargs.get('vmax')
clip = kwargs.get('clip')
cmap = kwargs.get('cmap', 'jet')
# TODO customization
normalizer = mpl.colors.Normalize(vmin, vmax, clip)
nimg = normalizer(img)
cmap = cm.get_cmap(cmap)
cimg = cmap(nimg)[:, :, :3] # ignore alpha
simg = (255*cimg).astype(np.uint8)
return simg
def rgb_preprocess(img):
if np.issubdtype(img.dtype, np.float):
# assuming 0., 1. range
return (img*255).clip(0, 255).astype(np.uint8)
if not img.dtype == np.uint8:
raise ValueError('only uint8 or float for 3-channel images')
return img
def img_encode(img, encoding):
# ret, data = cv2.imencode('.'+encoding, img)
if encoding=='jpg':
encoding = 'jpeg'
buf = StringIO.StringIO()
Image.fromarray(img).save(buf, format=encoding)
data = buf.getvalue()
buf.close()
return data
def b64_encode(data, encoding):
b64data = ('data:image/{};base64,{}'
.format(encoding, base64.b64encode(data).decode('ascii')))
return b64data
def pylab(fig, **kwargs):
""" Display a matplotlib figure. """
# save figure to buffer
output = StringIO.StringIO()
fig.savefig(output, format='png')
data = output.getvalue()
output.close()
encoded = b64_encode(data, 'png')
pydisp.pane('image',
win=kwargs.get('win'),
title=kwargs.get('title'),
content={
'src': encoded,
'width': kwargs.get('width'),
})
return win
def image(img, **kwargs):
""" Display image encoded as an array.
image(img, [win, title, labels, width, kwargs])
to_bgr: swap blue and red channels (default False)
encoding: 'jpg' (default) or 'png'
kwargs is argument for scalar preprocessing
"""
to_bgr = kwargs.get('to_bgr', False)
if img.ndim not in (2, 3):
raise ValueError('image should be 2 (gray) or 3 (rgb) dimensional')
assert img.ndim == 2 or img.ndim == 3
if img.ndim == 3:
img = rgb_preprocess(img)
else:
img = scalar_preprocess(img, **kwargs)
if to_bgr:
img = img[...,[2, 1, 0]]
encoding = kwargs.get('encoding', 'jpg')
data = img_encode(img, encoding)
encoded = b64_encode(data, encoding)
return pane('image',
kwargs.get('win'),
kwargs.get('title'),
content={
'src': encoded,
'labels': kwargs.get('labels'),
'width': kwargs.get('width'),
})
def text(txt, **kwargs):
win = kwargs.get('win') or uid()
title = kwargs.get('title') or 'text'
return pane('text',
win,
title,
content=txt)
def dyplot(data, **kwargs):
""" Plot data as line chart with dygraph
Params:
data: either a 2-d numpy array or a list of lists.
win: pane id
labels: list of series names, first series is always the X-axis
see http://dygraphs.com/options.html for other supported options
"""
win = kwargs.get('win') or uid()
dataset = {}
if type(data).__module__ == np.__name__:
dataset = data.tolist()
else:
dataset = data
# clone kwargs into options
options = dict(kwargs)
options['file'] = dataset
if options.get('labels'):
options['xlabel'] = options['labels'][0]
# Don't pass our options to dygraphs.
options.pop('win', None)
return pane('plot', kwargs.get('win'), kwargs.get('title'), content=options)
| mit |
mtustin-handy/airflow | airflow/hooks/presto_hook.py | 37 | 2626 | from builtins import str
from pyhive import presto
from pyhive.exc import DatabaseError
from airflow.hooks.dbapi_hook import DbApiHook
import logging
logging.getLogger("pyhive").setLevel(logging.INFO)
class PrestoException(Exception):
pass
class PrestoHook(DbApiHook):
"""
Interact with Presto through PyHive!
>>> ph = PrestoHook()
>>> sql = "SELECT count(1) AS num FROM airflow.static_babynames"
>>> ph.get_records(sql)
[[340698]]
"""
conn_name_attr = 'presto_conn_id'
default_conn_name = 'presto_default'
def get_conn(self):
"""Returns a connection object"""
db = self.get_connection(self.presto_conn_id)
return presto.connect(
host=db.host,
port=db.port,
username=db.login,
catalog=db.extra_dejson.get('catalog', 'hive'),
schema=db.schema)
@staticmethod
def _strip_sql(sql):
return sql.strip().rstrip(';')
def get_records(self, hql, parameters=None):
"""
Get a set of records from Presto
"""
try:
return super(PrestoHook, self).get_records(
self._strip_sql(hql), parameters)
except DatabaseError as e:
obj = eval(str(e))
raise PrestoException(obj['message'])
def get_first(self, hql, parameters=None):
"""
Returns only the first row, regardless of how many rows the query
returns.
"""
try:
return super(PrestoHook, self).get_first(
self._strip_sql(hql), parameters)
except DatabaseError as e:
obj = eval(str(e))
raise PrestoException(obj['message'])
def get_pandas_df(self, hql, parameters=None):
"""
Get a pandas dataframe from a sql query.
"""
import pandas
cursor = self.get_cursor()
cursor.execute(self._strip_sql(hql), parameters)
try:
data = cursor.fetchall()
except DatabaseError as e:
obj = eval(str(e))
raise PrestoException(obj['message'])
column_descriptions = cursor.description
if data:
df = pandas.DataFrame(data)
df.columns = [c[0] for c in column_descriptions]
else:
df = pandas.DataFrame()
return df
def run(self, hql, parameters=None):
"""
Execute the statement against Presto. Can be used to create views.
"""
return super(PrestoHook, self).run(self._strip_sql(hql), parameters)
def insert_rows(self):
raise NotImplemented()
| apache-2.0 |
juanyaw/PTVS | Python/Product/Analyzer/BuiltinScraperTests.py | 18 | 18954 | # ############################################################################
#
# Copyright (c) Microsoft Corporation.
#
# This source code is subject to terms and conditions of the Apache License, Version 2.0. A
# copy of the license can be found in the License.html file at the root of this distribution. If
# you cannot locate the Apache License, Version 2.0, please send an email to
# [email protected]. By using this source code in any fashion, you are agreeing to be bound
# by the terms of the Apache License, Version 2.0.
#
# You must not remove this notice, or any other, from this software.
#
# ###########################################################################
import re
import unittest
from pprint import pformat
from BuiltinScraper import parse_doc_str, BUILTIN, __builtins__, get_overloads_from_doc_string, TOKENS_REGEX
try:
unicode
except NameError:
from BuiltinScraper import unicode
import sys
class Test_BuiltinScraperTests(unittest.TestCase):
def check_doc_str(self, doc, module_name, func_name, expected, mod=None, extra_args=[], obj_class=None):
r = parse_doc_str(doc, module_name, mod, func_name, extra_args, obj_class)
# Quick pass if everything matches
if r == expected:
return
msg = 'Expected:\n%s\nActual\n%s' % (pformat(expected), pformat(r))
self.assertEqual(len(r), len(expected), msg)
def check_dict(e, a, indent):
if e == a:
return
missing_keys = set(e.keys()) - set(a.keys())
extra_keys = set(a.keys()) - set(e.keys())
mismatched_keys = [k for k in set(a.keys()) & set(e.keys()) if a[k] != e[k]]
if missing_keys:
print('%sDid not received following keys: %s' % (indent, ', '.join(missing_keys)))
if extra_keys:
print('%sDid not expect following keys: %s' % (indent, ', '.join(extra_keys)))
for k in mismatched_keys:
if isinstance(e[k], dict) and isinstance(a[k], dict):
check_dict(e[k], a[k], indent + ' ')
elif (isinstance(e[k], tuple) and isinstance(a[k], tuple) or isinstance(e[k], list) and isinstance(a[k], list)):
check_seq(e[k], a[k], indent + ' ')
else:
print('%sExpected "%s": "%s"' % (indent, k, e[k]))
print('%sActual "%s": "%s"' % (indent, k, a[k]))
print('')
def check_seq(e, a, indent):
if e == a:
return
for i, (e2, a2) in enumerate(zip(e, a)):
if isinstance(e2, dict) and isinstance(a2, dict):
check_dict(e2, a2, indent + ' ')
elif (isinstance(e2, tuple) and isinstance(a2, tuple) or isinstance(e2, list) and isinstance(a2, list)):
check_seq(e2, a2, indent + ' ')
elif e1 != a1:
print('%sExpected "%s"' % (indent, e2))
print('%sActual "%s"' % (indent, a2))
print('')
for e1, a1 in zip(expected, r):
check_dict(e1, a1, '')
self.fail(msg)
def test_regex(self):
self.assertSequenceEqual(
[i.strip() for i in re.split(TOKENS_REGEX, 'f(\'\', \'a\', \'a\\\'b\', "", "a", "a\\\"b")') if i.strip()],
['f', '(', "''", ',', "'a'", ',', "'a\\'b'", ',', '""', ',', '"a"', ',', '"a\\"b"', ')']
)
self.assertSequenceEqual(
[i.strip() for i in re.split(TOKENS_REGEX, 'f(1, 1., -1, -1.)') if i.strip()],
['f', '(', '1', ',', '1.', ',', '-1', ',', '-1.', ')']
)
self.assertSequenceEqual(
[i.strip() for i in re.split(TOKENS_REGEX, 'f(a, *a, **a, ...)') if i.strip()],
['f', '(', 'a', ',', '*', 'a', ',', '**', 'a', ',', '...', ')']
)
self.assertSequenceEqual(
[i.strip() for i in re.split(TOKENS_REGEX, 'f(a:123, a=123) --> => ->') if i.strip()],
['f', '(', 'a', ':', '123', ',', 'a', '=', '123', ')', '-->', '=>', '->']
)
def test_numpy_1(self):
self.check_doc_str(
"""arange([start,] stop[, step,], dtype=None)
Returns
-------
out : ndarray""",
'numpy',
'arange',
[{
'doc': 'Returns\n -------\n out : ndarray',
'ret_type': [('', 'ndarray')],
'args': (
{'name': 'start', 'default_value':'None'},
{'name': 'stop'},
{'name': 'step', 'default_value': 'None'},
{'name': 'dtype', 'default_value':'None'},
)
}]
)
def test_numpy_2(self):
self.check_doc_str(
"""arange([start,] stop[, step,], dtype=None)
Return - out : ndarray""",
'numpy',
'arange',
[{
'doc': 'Return - out : ndarray',
'ret_type': [('', 'ndarray')],
'args': (
{'name': 'start', 'default_value':'None'},
{'name': 'stop'},
{'name': 'step', 'default_value': 'None'},
{'name': 'dtype', 'default_value':'None'},
)
}]
)
def test_reduce(self):
self.check_doc_str(
'reduce(function, sequence[, initial]) -> value',
BUILTIN,
'reduce',
mod=__builtins__,
expected = [{
'args': (
{'name': 'function'},
{'name': 'sequence'},
{'default_value': 'None', 'name': 'initial'}
),
'doc': '',
'ret_type': [('', 'value')]
}]
)
def test_pygame_draw_arc(self):
self.check_doc_str(
'pygame.draw.arc(Surface, color, Rect, start_angle, stop_angle, width=1): return Rect',
'draw',
'arc',
[{
'args': (
{'name': 'Surface'},
{'name': 'color'},
{'name': 'Rect'},
{'name': 'start_angle'},
{'name': 'stop_angle'},
{'default_value': '1', 'name': 'width'}
),
'doc': '',
'ret_type': [('', 'Rect')]
}]
)
def test_isdigit(self):
self.check_doc_str(
'''B.isdigit() -> bool
Return True if all characters in B are digits
and there is at least one character in B, False otherwise.''',
'bytes',
'isdigit',
[{
'args': (),
'doc': 'Return True if all characters in B are digits\nand there is at least one character in B, False otherwise.',
'ret_type': [(BUILTIN, 'bool')]
}]
)
def test_init(self):
self.check_doc_str(
'x.__init__(...) initializes x; see help(type(x)) for signature',
'str',
'__init__',
[{'args': ({'arg_format': '*', 'name': 'args'},), 'doc': 'initializes x; see help(type(x)) for signature'}]
)
def test_find(self):
self.check_doc_str(
'S.find(sub [,start [,end]]) -> int',
'str',
'find',
[{
'args': (
{'name': 'sub'},
{'default_value': 'None', 'name': 'start'},
{'default_value': 'None', 'name': 'end'}
),
'doc': '',
'ret_type': [(BUILTIN, 'int')]
}]
)
def test_format(self):
self.check_doc_str(
'S.format(*args, **kwargs) -> unicode',
'str',
'format',
[{
'args': (
{'arg_format': '*', 'name': 'args'},
{'arg_format': '**', 'name': 'kwargs'}
),
'doc': '',
'ret_type': [(BUILTIN, unicode.__name__)]
}]
)
def test_ascii(self):
self.check_doc_str(
"'ascii(object) -> string\n\nReturn the same as repr(). In Python 3.x, the repr() result will\\ncontain printable characters unescaped, while the ascii() result\\nwill have such characters backslash-escaped.'",
'future_builtins',
'ascii',
[{
'args': ({'name': 'object'},),
'doc': "Return the same as repr(). In Python 3.x, the repr() result will\\ncontain printable characters unescaped, while the ascii() result\\nwill have such characters backslash-escaped.'",
'ret_type': [(BUILTIN, 'str')]
}]
)
def test_preannotation(self):
self.check_doc_str(
'f(INT class_code) => SpaceID',
'fob',
'f',
[{
'args': ({'name': 'class_code', 'type': [(BUILTIN, 'int')]},),
'doc': '',
'ret_type': [('', 'SpaceID')]
}])
def test_compress(self):
self.check_doc_str(
'compress(data, selectors) --> iterator over selected data\n\nReturn data elements',
'itertools',
'compress',
[{
'args': ({'name': 'data'}, {'name': 'selectors'}),
'doc': 'Return data elements',
'ret_type': [('', 'iterator')]
}]
)
def test_isinstance(self):
self.check_doc_str(
'isinstance(object, class-or-type-or-tuple) -> bool\n\nReturn whether an object is an '
'instance of a class or of a subclass thereof.\nWith a type as second argument, '
'return whether that is the object\'s type.\nThe form using a tuple, isinstance(x, (A, B, ...)),'
' is a shortcut for\nisinstance(x, A) or isinstance(x, B) or ... (etc.).',
BUILTIN,
'isinstance',
[{
'args': ({'name': 'object'}, {'name': 'class-or-type-or-tuple'}),
'doc': "Return whether an object is an instance of a class or of a subclass thereof.\n"
"With a type as second argument, return whether that is the object's type.\n"
"The form using a tuple, isinstance(x, (A, B, ...)), is a shortcut for\n"
"isinstance(x, A) or isinstance(x, B) or ... (etc.).",
'ret_type': [(BUILTIN, 'bool')]
}]
)
def test_tuple_parameters(self):
self.check_doc_str(
'pygame.Rect(left, top, width, height): return Rect\n'
'pygame.Rect((left, top), (width, height)): return Rect\n'
'pygame.Rect(object): return Rect\n'
'pygame object for storing rectangular coordinates',
'pygame',
'Rect',
[{
'args': ({'name': 'left'}, {'name': 'top'}, {'name': 'width'}, {'name': 'height'}),
'doc': 'pygame object for storing rectangular coordinates',
'ret_type': [('', 'Rect')]
},
{
'args': ({'name': 'left, top'}, {'name': 'width, height'}),
'doc': 'pygame object for storing rectangular coordinates',
'ret_type': [('', 'Rect')]
},
{
'args': ({'name': 'object'},),
'doc': 'pygame object for storing rectangular coordinates',
'ret_type': [('', 'Rect')]
}]
)
def test_read(self):
self.check_doc_str(
'read([size]) -> read at most size bytes, returned as a string.\n\n'
'If the size argument is negative or omitted, read until EOF is reached.\n'
'Notice that when in non-blocking mode, less data than what was requested\n'
'may be returned, even if no size parameter was given.',
BUILTIN,
'read',
mod=__builtins__,
expected=[{
'args': ({'default_value': 'None', 'name': 'size'},),
'doc': 'read at most size bytes, returned as a string.\n\nIf the size argument is negative or omitted, read until EOF is reached.\nNotice that when in non-blocking mode, less data than what was requested\nmay be returned, even if no size parameter was given.',
'ret_type': [('', '')]
}]
)
r = get_overloads_from_doc_string(
'read([size]) -> read at most size bytes, returned as a string.\n\n'
'If the size argument is negative or omitted, read until EOF is reached.\n'
'Notice that when in non-blocking mode, less data than what was requested\n'
'may be returned, even if no size parameter was given.',
__builtins__,
None,
'read'
)
self.assertEqual(
r,
[{
'args': ({'default_value': 'None', 'name': 'size'},),
'doc': 'read at most size bytes, returned as a string.\n\nIf the size argument is negative or omitted, read until EOF is reached.\nNotice that when in non-blocking mode, less data than what was requested\nmay be returned, even if no size parameter was given.',
'ret_type': [('', '')]
}],
repr(r)
)
def test_new(self):
self.check_doc_str(
'T.__new__(S, ...) -> a new object with type S, a subtype of T',
'struct',
'__new__',
[{
'ret_type': [('', '')],
'doc': 'a new object with type S, a subtype of T',
'args': ({'name': 'S'}, {'arg_format': '*', 'name': 'args'})
}]
)
def test_C_prototype(self):
self.check_doc_str(
'GetDriverByName(char const * name) -> Driver',
'',
'GetDriverByName',
[{
'ret_type': [('', 'Driver')],
'doc': '',
'args': ({'name': 'name', 'type': [(BUILTIN, 'str')]},),
}]
)
def test_chmod(self):
self.check_doc_str(
'chmod(path, mode, *, dir_fd=None, follow_symlinks=True)',
'nt',
'chmod',
[{
'doc': '',
'args': (
{'name': 'path'},
{'name': 'mode'},
{'name': 'args', 'arg_format': '*'},
{'name': 'dir_fd', 'default_value': 'None'},
{'name': 'follow_symlinks', 'default_value': 'True'}
)
}]
)
def test_open(self):
if sys.version_info[0] >= 3:
expect_ret_type = ('_io', '_IOBase')
else:
expect_ret_type = (BUILTIN, 'file')
self.check_doc_str(
'open(file, mode=\'r\', buffering=-1, encoding=None,\n' +
' errors=None, newline=None, closefd=True, opener=None)' +
' -> file object\n\nOpen file',
BUILTIN,
'open',
[{
'doc': 'Open file',
'ret_type': [expect_ret_type],
'args': (
{'name': 'file'},
{'name': 'mode', 'default_value': "'r'"},
{'name': 'buffering', 'default_value': '-1'},
{'name': 'encoding', 'default_value': 'None'},
{'name': 'errors', 'default_value': 'None'},
{'name': 'newline', 'default_value': 'None'},
{'name': 'closefd', 'default_value': 'True'},
{'name': 'opener', 'default_value': 'None'},
)
}]
)
def test_optional_with_default(self):
self.check_doc_str(
'max(iterable[, key=func]) -> value',
BUILTIN,
'max',
[{
'doc': '',
'ret_type': [('', 'value')],
'args': (
{'name': 'iterable'},
{'name': 'key', 'default_value': 'func'}
)
}]
)
def test_pyplot_figure(self):
pyplot_doc = """
Creates a new figure.
Parameters
----------
num : integer or string, optional, default: none
If not provided, a new figure will be created, and a the figure number
will be increamted. The figure objects holds this number in a `number`
attribute.
If num is provided, and a figure with this id already exists, make
it active, and returns a reference to it. If this figure does not
exists, create it and returns it.
If num is a string, the window title will be set to this figure's
`num`.
figsize : tuple of integers, optional, default : None
width, height in inches. If not provided, defaults to rc
figure.figsize.
dpi : integer, optional, default ; None
resolution of the figure. If not provided, defaults to rc figure.dpi.
facecolor :
the background color; If not provided, defaults to rc figure.facecolor
edgecolor :
the border color. If not provided, defaults to rc figure.edgecolor
Returns
-------
figure : Figure
The Figure instance returned will also be passed to new_figure_manager
in the backends, which allows to hook custom Figure classes into the
pylab interface. Additional kwargs will be passed to the figure init
function.
Note
----
If you are creating many figures, make sure you explicitly call "close"
on the figures you are not using, because this will enable pylab
to properly clean up the memory.
rcParams defines the default values, which can be modified in the
matplotlibrc file
"""
self.check_doc_str(
pyplot_doc,
'matplotlib.pyplot',
'figure',
[{
'doc': pyplot_doc,
'ret_type': [('', 'Figure')],
'args': (
{'name': 'args', 'arg_format': '*'},
{'name': 'kwargs', 'arg_format': '**'}
)
}]
)
if __name__ == '__main__':
unittest.main()
| apache-2.0 |
davidrobles/mlnd-capstone-code | capstone/datasets/ucic4.py | 1 | 1524 | from __future__ import division, unicode_literals
import pandas as pd
from capstone.game.games import Connect4 as C4
def load_dataframe():
'''
Returns a Pandas Dataframe of the UCI Connect 4 dataset
https://archive.ics.uci.edu/ml/machine-learning-databases/connect-4/connect-4.names
'''
def column_name(i):
if i == 42:
return 'outcome'
row = chr(ord('a') + (i // C4.ROWS))
col = (i % C4.ROWS) + 1
return '{row}{col}'.format(row=row, col=col)
column_names = [column_name(i) for i in range(43)]
return pd.read_csv('datasets/uci_c4.csv', header=None, names=column_names)
def series_to_game(series):
'''Converts a Pandas Series to a Connect 4 game'''
cell_map = {'x': 'X', 'o': 'O', 'b': '-'}
board = [[' '] * C4.COLS for row in range(C4.ROWS)]
cells = series.iloc[:-1]
outcome = series.iloc[-1]
for ix, cell in enumerate(cells):
row = C4.ROWS - (ix % C4.ROWS) - 1
col = ix // C4.ROWS
board[row][col] = cell_map[cell]
return C4(board), outcome
_df = load_dataframe()
def get_random_game(outcome=None):
if outcome:
sample_df = _df.loc[_df['outcome'] == outcome].sample()
else:
sample_df = _df.sample()
row = sample_df.iloc[0]
game, _ = series_to_game(row)
return game
def get_random_win_game():
return get_random_game('win')
def get_random_loss_game():
return get_random_game('loss')
def get_random_draw_game():
return get_random_game('draw')
| mit |
XueqingLin/tensorflow | tensorflow/examples/skflow/mnist.py | 8 | 3167 | # Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""This showcases how simple it is to build image classification networks.
It follows description from this TensorFlow tutorial:
https://www.tensorflow.org/versions/master/tutorials/mnist/pros/index.html#deep-mnist-for-experts
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from sklearn import metrics
import tensorflow as tf
from tensorflow.contrib import learn
### Download and load MNIST data.
mnist = learn.datasets.load_dataset('mnist')
### Linear classifier.
feature_columns = learn.infer_real_valued_columns_from_input(mnist.train.images)
classifier = learn.LinearClassifier(
feature_columns=feature_columns, n_classes=10)
classifier.fit(mnist.train.images, mnist.train.labels, batch_size=100,
steps=1000)
score = metrics.accuracy_score(
mnist.test.labels, classifier.predict(mnist.test.images))
print('Accuracy: {0:f}'.format(score))
### Convolutional network
def max_pool_2x2(tensor_in):
return tf.nn.max_pool(
tensor_in, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
def conv_model(X, y):
# pylint: disable=invalid-name,missing-docstring
# reshape X to 4d tensor with 2nd and 3rd dimensions being image width and
# height final dimension being the number of color channels.
X = tf.reshape(X, [-1, 28, 28, 1])
# first conv layer will compute 32 features for each 5x5 patch
with tf.variable_scope('conv_layer1'):
h_conv1 = learn.ops.conv2d(X, n_filters=32, filter_shape=[5, 5],
bias=True, activation=tf.nn.relu)
h_pool1 = max_pool_2x2(h_conv1)
# second conv layer will compute 64 features for each 5x5 patch.
with tf.variable_scope('conv_layer2'):
h_conv2 = learn.ops.conv2d(h_pool1, n_filters=64, filter_shape=[5, 5],
bias=True, activation=tf.nn.relu)
h_pool2 = max_pool_2x2(h_conv2)
# reshape tensor into a batch of vectors
h_pool2_flat = tf.reshape(h_pool2, [-1, 7 * 7 * 64])
# densely connected layer with 1024 neurons.
h_fc1 = learn.ops.dnn(
h_pool2_flat, [1024], activation=tf.nn.relu, dropout=0.5)
return learn.models.logistic_regression(h_fc1, y)
# Training and predicting.
classifier = learn.TensorFlowEstimator(
model_fn=conv_model, n_classes=10, batch_size=100, steps=20000,
learning_rate=0.001)
classifier.fit(mnist.train.images, mnist.train.labels)
score = metrics.accuracy_score(
mnist.test.labels, classifier.predict(mnist.test.images))
print('Accuracy: {0:f}'.format(score))
| apache-2.0 |
MrNuggelz/sklearn-glvq | setup.py | 1 | 1432 | from __future__ import print_function
import sys
from setuptools import setup
with open('requirements.txt') as f:
INSTALL_REQUIRES = [l.strip() for l in f.readlines() if l]
try:
import numpy
except ImportError:
print('numpy is required during installation')
sys.exit(1)
try:
import scipy
except ImportError:
print('scipy is required during installation')
sys.exit(1)
CLASSIFIERS = [
'Programming Language :: Python',
'Programming Language :: Python :: 2.7',
'Programming Language :: Python :: 3.5',
'Intended Audience :: Science/Research',
'Natural Language :: English',
'Operating System :: POSIX',
'Operating System :: MacOS',
'Operating System :: Unix',
'Operating System :: Microsoft :: Windows',
]
version = '1.1.0'
setup(name='sklearn-lvq',
version=version,
description='sklearn compatible Generalized Learning Vector '
'Quantization and Robust Soft Learning Vector Quantization implementation',
author='Joris Jensen',
url='https://github.com/MrNuggelz/sklearn-lvq',
download_url='https://github.com/MrNuggelz/sklearn-lvq/releases/tag/{}'.format(version),
tests_require=['nose'],
platforms=['any'],
license='BSD-3-Clause',
packages=['sklearn_lvq'],
install_requires=INSTALL_REQUIRES,
author_email='[email protected]',
classifiers=CLASSIFIERS,
)
| bsd-3-clause |
musically-ut/numpy | numpy/core/tests/test_multiarray.py | 5 | 220057 | from __future__ import division, absolute_import, print_function
import collections
import tempfile
import sys
import os
import shutil
import warnings
import operator
import io
import itertools
if sys.version_info[0] >= 3:
import builtins
else:
import __builtin__ as builtins
from decimal import Decimal
import numpy as np
from nose import SkipTest
from numpy.core import *
from numpy.compat import asbytes, getexception, strchar, sixu
from test_print import in_foreign_locale
from numpy.core.multiarray_tests import (
test_neighborhood_iterator, test_neighborhood_iterator_oob,
test_pydatamem_seteventhook_start, test_pydatamem_seteventhook_end,
test_inplace_increment, get_buffer_info, test_as_c_array
)
from numpy.testing import (
TestCase, run_module_suite, assert_, assert_raises,
assert_equal, assert_almost_equal, assert_array_equal,
assert_array_almost_equal, assert_allclose,
assert_array_less, runstring, dec
)
# Need to test an object that does not fully implement math interface
from datetime import timedelta
if sys.version_info[:2] > (3, 2):
# In Python 3.3 the representation of empty shape, strides and suboffsets
# is an empty tuple instead of None.
# http://docs.python.org/dev/whatsnew/3.3.html#api-changes
EMPTY = ()
else:
EMPTY = None
class TestFlags(TestCase):
def setUp(self):
self.a = arange(10)
def test_writeable(self):
mydict = locals()
self.a.flags.writeable = False
self.assertRaises(ValueError, runstring, 'self.a[0] = 3', mydict)
self.assertRaises(ValueError, runstring, 'self.a[0:1].itemset(3)', mydict)
self.a.flags.writeable = True
self.a[0] = 5
self.a[0] = 0
def test_otherflags(self):
assert_equal(self.a.flags.carray, True)
assert_equal(self.a.flags.farray, False)
assert_equal(self.a.flags.behaved, True)
assert_equal(self.a.flags.fnc, False)
assert_equal(self.a.flags.forc, True)
assert_equal(self.a.flags.owndata, True)
assert_equal(self.a.flags.writeable, True)
assert_equal(self.a.flags.aligned, True)
assert_equal(self.a.flags.updateifcopy, False)
def test_string_align(self):
a = np.zeros(4, dtype=np.dtype('|S4'))
assert_(a.flags.aligned)
# not power of two are accessed bytewise and thus considered aligned
a = np.zeros(5, dtype=np.dtype('|S4'))
assert_(a.flags.aligned)
def test_void_align(self):
a = np.zeros(4, dtype=np.dtype([("a", "i4"), ("b", "i4")]))
assert_(a.flags.aligned)
class TestHash(TestCase):
# see #3793
def test_int(self):
for st, ut, s in [(np.int8, np.uint8, 8),
(np.int16, np.uint16, 16),
(np.int32, np.uint32, 32),
(np.int64, np.uint64, 64)]:
for i in range(1, s):
assert_equal(hash(st(-2**i)), hash(-2**i),
err_msg="%r: -2**%d" % (st, i))
assert_equal(hash(st(2**(i - 1))), hash(2**(i - 1)),
err_msg="%r: 2**%d" % (st, i - 1))
assert_equal(hash(st(2**i - 1)), hash(2**i - 1),
err_msg="%r: 2**%d - 1" % (st, i))
i = max(i - 1, 1)
assert_equal(hash(ut(2**(i - 1))), hash(2**(i - 1)),
err_msg="%r: 2**%d" % (ut, i - 1))
assert_equal(hash(ut(2**i - 1)), hash(2**i - 1),
err_msg="%r: 2**%d - 1" % (ut, i))
class TestAttributes(TestCase):
def setUp(self):
self.one = arange(10)
self.two = arange(20).reshape(4, 5)
self.three = arange(60, dtype=float64).reshape(2, 5, 6)
def test_attributes(self):
assert_equal(self.one.shape, (10,))
assert_equal(self.two.shape, (4, 5))
assert_equal(self.three.shape, (2, 5, 6))
self.three.shape = (10, 3, 2)
assert_equal(self.three.shape, (10, 3, 2))
self.three.shape = (2, 5, 6)
assert_equal(self.one.strides, (self.one.itemsize,))
num = self.two.itemsize
assert_equal(self.two.strides, (5*num, num))
num = self.three.itemsize
assert_equal(self.three.strides, (30*num, 6*num, num))
assert_equal(self.one.ndim, 1)
assert_equal(self.two.ndim, 2)
assert_equal(self.three.ndim, 3)
num = self.two.itemsize
assert_equal(self.two.size, 20)
assert_equal(self.two.nbytes, 20*num)
assert_equal(self.two.itemsize, self.two.dtype.itemsize)
assert_equal(self.two.base, arange(20))
def test_dtypeattr(self):
assert_equal(self.one.dtype, dtype(int_))
assert_equal(self.three.dtype, dtype(float_))
assert_equal(self.one.dtype.char, 'l')
assert_equal(self.three.dtype.char, 'd')
self.assertTrue(self.three.dtype.str[0] in '<>')
assert_equal(self.one.dtype.str[1], 'i')
assert_equal(self.three.dtype.str[1], 'f')
def test_int_subclassing(self):
# Regression test for https://github.com/numpy/numpy/pull/3526
numpy_int = np.int_(0)
if sys.version_info[0] >= 3:
# On Py3k int_ should not inherit from int, because it's not fixed-width anymore
assert_equal(isinstance(numpy_int, int), False)
else:
# Otherwise, it should inherit from int...
assert_equal(isinstance(numpy_int, int), True)
# ... and fast-path checks on C-API level should also work
from numpy.core.multiarray_tests import test_int_subclass
assert_equal(test_int_subclass(numpy_int), True)
def test_stridesattr(self):
x = self.one
def make_array(size, offset, strides):
return ndarray(size, buffer=x, dtype=int,
offset=offset*x.itemsize,
strides=strides*x.itemsize)
assert_equal(make_array(4, 4, -1), array([4, 3, 2, 1]))
self.assertRaises(ValueError, make_array, 4, 4, -2)
self.assertRaises(ValueError, make_array, 4, 2, -1)
self.assertRaises(ValueError, make_array, 8, 3, 1)
assert_equal(make_array(8, 3, 0), np.array([3]*8))
# Check behavior reported in gh-2503:
self.assertRaises(ValueError, make_array, (2, 3), 5, array([-2, -3]))
make_array(0, 0, 10)
def test_set_stridesattr(self):
x = self.one
def make_array(size, offset, strides):
try:
r = ndarray([size], dtype=int, buffer=x, offset=offset*x.itemsize)
except:
raise RuntimeError(getexception())
r.strides = strides=strides*x.itemsize
return r
assert_equal(make_array(4, 4, -1), array([4, 3, 2, 1]))
assert_equal(make_array(7, 3, 1), array([3, 4, 5, 6, 7, 8, 9]))
self.assertRaises(ValueError, make_array, 4, 4, -2)
self.assertRaises(ValueError, make_array, 4, 2, -1)
self.assertRaises(RuntimeError, make_array, 8, 3, 1)
# Check that the true extent of the array is used.
# Test relies on as_strided base not exposing a buffer.
x = np.lib.stride_tricks.as_strided(arange(1), (10, 10), (0, 0))
def set_strides(arr, strides):
arr.strides = strides
self.assertRaises(ValueError, set_strides, x, (10*x.itemsize, x.itemsize))
# Test for offset calculations:
x = np.lib.stride_tricks.as_strided(np.arange(10, dtype=np.int8)[-1],
shape=(10,), strides=(-1,))
self.assertRaises(ValueError, set_strides, x[::-1], -1)
a = x[::-1]
a.strides = 1
a[::2].strides = 2
def test_fill(self):
for t in "?bhilqpBHILQPfdgFDGO":
x = empty((3, 2, 1), t)
y = empty((3, 2, 1), t)
x.fill(1)
y[...] = 1
assert_equal(x, y)
def test_fill_max_uint64(self):
x = empty((3, 2, 1), dtype=uint64)
y = empty((3, 2, 1), dtype=uint64)
value = 2**64 - 1
y[...] = value
x.fill(value)
assert_array_equal(x, y)
def test_fill_struct_array(self):
# Filling from a scalar
x = array([(0, 0.0), (1, 1.0)], dtype='i4,f8')
x.fill(x[0])
assert_equal(x['f1'][1], x['f1'][0])
# Filling from a tuple that can be converted
# to a scalar
x = np.zeros(2, dtype=[('a', 'f8'), ('b', 'i4')])
x.fill((3.5, -2))
assert_array_equal(x['a'], [3.5, 3.5])
assert_array_equal(x['b'], [-2, -2])
class TestArrayConstruction(TestCase):
def test_array(self):
d = np.ones(6)
r = np.array([d, d])
assert_equal(r, np.ones((2, 6)))
d = np.ones(6)
tgt = np.ones((2, 6))
r = np.array([d, d])
assert_equal(r, tgt)
tgt[1] = 2
r = np.array([d, d + 1])
assert_equal(r, tgt)
d = np.ones(6)
r = np.array([[d, d]])
assert_equal(r, np.ones((1, 2, 6)))
d = np.ones(6)
r = np.array([[d, d], [d, d]])
assert_equal(r, np.ones((2, 2, 6)))
d = np.ones((6, 6))
r = np.array([d, d])
assert_equal(r, np.ones((2, 6, 6)))
d = np.ones((6, ))
r = np.array([[d, d + 1], d + 2])
assert_equal(len(r), 2)
assert_equal(r[0], [d, d + 1])
assert_equal(r[1], d + 2)
tgt = np.ones((2, 3), dtype=np.bool)
tgt[0, 2] = False
tgt[1, 0:2] = False
r = np.array([[True, True, False], [False, False, True]])
assert_equal(r, tgt)
r = np.array([[True, False], [True, False], [False, True]])
assert_equal(r, tgt.T)
def test_array_empty(self):
assert_raises(TypeError, np.array)
def test_array_copy_false(self):
d = np.array([1, 2, 3])
e = np.array(d, copy=False)
d[1] = 3
assert_array_equal(e, [1, 3, 3])
e = np.array(d, copy=False, order='F')
d[1] = 4
assert_array_equal(e, [1, 4, 3])
e[2] = 7
assert_array_equal(d, [1, 4, 7])
def test_array_copy_true(self):
d = np.array([[1,2,3], [1, 2, 3]])
e = np.array(d, copy=True)
d[0, 1] = 3
e[0, 2] = -7
assert_array_equal(e, [[1, 2, -7], [1, 2, 3]])
assert_array_equal(d, [[1, 3, 3], [1, 2, 3]])
e = np.array(d, copy=True, order='F')
d[0, 1] = 5
e[0, 2] = 7
assert_array_equal(e, [[1, 3, 7], [1, 2, 3]])
assert_array_equal(d, [[1, 5, 3], [1,2,3]])
def test_array_cont(self):
d = np.ones(10)[::2]
assert_(np.ascontiguousarray(d).flags.c_contiguous)
assert_(np.ascontiguousarray(d).flags.f_contiguous)
assert_(np.asfortranarray(d).flags.c_contiguous)
assert_(np.asfortranarray(d).flags.f_contiguous)
d = np.ones((10, 10))[::2,::2]
assert_(np.ascontiguousarray(d).flags.c_contiguous)
assert_(np.asfortranarray(d).flags.f_contiguous)
class TestAssignment(TestCase):
def test_assignment_broadcasting(self):
a = np.arange(6).reshape(2, 3)
# Broadcasting the input to the output
a[...] = np.arange(3)
assert_equal(a, [[0, 1, 2], [0, 1, 2]])
a[...] = np.arange(2).reshape(2, 1)
assert_equal(a, [[0, 0, 0], [1, 1, 1]])
# For compatibility with <= 1.5, a limited version of broadcasting
# the output to the input.
#
# This behavior is inconsistent with NumPy broadcasting
# in general, because it only uses one of the two broadcasting
# rules (adding a new "1" dimension to the left of the shape),
# applied to the output instead of an input. In NumPy 2.0, this kind
# of broadcasting assignment will likely be disallowed.
a[...] = np.arange(6)[::-1].reshape(1, 2, 3)
assert_equal(a, [[5, 4, 3], [2, 1, 0]])
# The other type of broadcasting would require a reduction operation.
def assign(a, b):
a[...] = b
assert_raises(ValueError, assign, a, np.arange(12).reshape(2, 2, 3))
def test_assignment_errors(self):
# Address issue #2276
class C:
pass
a = np.zeros(1)
def assign(v):
a[0] = v
assert_raises((AttributeError, TypeError), assign, C())
assert_raises(ValueError, assign, [1])
class TestDtypedescr(TestCase):
def test_construction(self):
d1 = dtype('i4')
assert_equal(d1, dtype(int32))
d2 = dtype('f8')
assert_equal(d2, dtype(float64))
def test_byteorders(self):
self.assertNotEqual(dtype('<i4'), dtype('>i4'))
self.assertNotEqual(dtype([('a', '<i4')]), dtype([('a', '>i4')]))
class TestZeroRank(TestCase):
def setUp(self):
self.d = array(0), array('x', object)
def test_ellipsis_subscript(self):
a, b = self.d
self.assertEqual(a[...], 0)
self.assertEqual(b[...], 'x')
self.assertTrue(a[...].base is a) # `a[...] is a` in numpy <1.9.
self.assertTrue(b[...].base is b) # `b[...] is b` in numpy <1.9.
def test_empty_subscript(self):
a, b = self.d
self.assertEqual(a[()], 0)
self.assertEqual(b[()], 'x')
self.assertTrue(type(a[()]) is a.dtype.type)
self.assertTrue(type(b[()]) is str)
def test_invalid_subscript(self):
a, b = self.d
self.assertRaises(IndexError, lambda x: x[0], a)
self.assertRaises(IndexError, lambda x: x[0], b)
self.assertRaises(IndexError, lambda x: x[array([], int)], a)
self.assertRaises(IndexError, lambda x: x[array([], int)], b)
def test_ellipsis_subscript_assignment(self):
a, b = self.d
a[...] = 42
self.assertEqual(a, 42)
b[...] = ''
self.assertEqual(b.item(), '')
def test_empty_subscript_assignment(self):
a, b = self.d
a[()] = 42
self.assertEqual(a, 42)
b[()] = ''
self.assertEqual(b.item(), '')
def test_invalid_subscript_assignment(self):
a, b = self.d
def assign(x, i, v):
x[i] = v
self.assertRaises(IndexError, assign, a, 0, 42)
self.assertRaises(IndexError, assign, b, 0, '')
self.assertRaises(ValueError, assign, a, (), '')
def test_newaxis(self):
a, b = self.d
self.assertEqual(a[newaxis].shape, (1,))
self.assertEqual(a[..., newaxis].shape, (1,))
self.assertEqual(a[newaxis, ...].shape, (1,))
self.assertEqual(a[..., newaxis].shape, (1,))
self.assertEqual(a[newaxis, ..., newaxis].shape, (1, 1))
self.assertEqual(a[..., newaxis, newaxis].shape, (1, 1))
self.assertEqual(a[newaxis, newaxis, ...].shape, (1, 1))
self.assertEqual(a[(newaxis,)*10].shape, (1,)*10)
def test_invalid_newaxis(self):
a, b = self.d
def subscript(x, i): x[i]
self.assertRaises(IndexError, subscript, a, (newaxis, 0))
self.assertRaises(IndexError, subscript, a, (newaxis,)*50)
def test_constructor(self):
x = ndarray(())
x[()] = 5
self.assertEqual(x[()], 5)
y = ndarray((), buffer=x)
y[()] = 6
self.assertEqual(x[()], 6)
def test_output(self):
x = array(2)
self.assertRaises(ValueError, add, x, [1], x)
class TestScalarIndexing(TestCase):
def setUp(self):
self.d = array([0, 1])[0]
def test_ellipsis_subscript(self):
a = self.d
self.assertEqual(a[...], 0)
self.assertEqual(a[...].shape, ())
def test_empty_subscript(self):
a = self.d
self.assertEqual(a[()], 0)
self.assertEqual(a[()].shape, ())
def test_invalid_subscript(self):
a = self.d
self.assertRaises(IndexError, lambda x: x[0], a)
self.assertRaises(IndexError, lambda x: x[array([], int)], a)
def test_invalid_subscript_assignment(self):
a = self.d
def assign(x, i, v):
x[i] = v
self.assertRaises(TypeError, assign, a, 0, 42)
def test_newaxis(self):
a = self.d
self.assertEqual(a[newaxis].shape, (1,))
self.assertEqual(a[..., newaxis].shape, (1,))
self.assertEqual(a[newaxis, ...].shape, (1,))
self.assertEqual(a[..., newaxis].shape, (1,))
self.assertEqual(a[newaxis, ..., newaxis].shape, (1, 1))
self.assertEqual(a[..., newaxis, newaxis].shape, (1, 1))
self.assertEqual(a[newaxis, newaxis, ...].shape, (1, 1))
self.assertEqual(a[(newaxis,)*10].shape, (1,)*10)
def test_invalid_newaxis(self):
a = self.d
def subscript(x, i): x[i]
self.assertRaises(IndexError, subscript, a, (newaxis, 0))
self.assertRaises(IndexError, subscript, a, (newaxis,)*50)
def test_overlapping_assignment(self):
# With positive strides
a = np.arange(4)
a[:-1] = a[1:]
assert_equal(a, [1, 2, 3, 3])
a = np.arange(4)
a[1:] = a[:-1]
assert_equal(a, [0, 0, 1, 2])
# With positive and negative strides
a = np.arange(4)
a[:] = a[::-1]
assert_equal(a, [3, 2, 1, 0])
a = np.arange(6).reshape(2, 3)
a[::-1,:] = a[:, ::-1]
assert_equal(a, [[5, 4, 3], [2, 1, 0]])
a = np.arange(6).reshape(2, 3)
a[::-1, ::-1] = a[:, ::-1]
assert_equal(a, [[3, 4, 5], [0, 1, 2]])
# With just one element overlapping
a = np.arange(5)
a[:3] = a[2:]
assert_equal(a, [2, 3, 4, 3, 4])
a = np.arange(5)
a[2:] = a[:3]
assert_equal(a, [0, 1, 0, 1, 2])
a = np.arange(5)
a[2::-1] = a[2:]
assert_equal(a, [4, 3, 2, 3, 4])
a = np.arange(5)
a[2:] = a[2::-1]
assert_equal(a, [0, 1, 2, 1, 0])
a = np.arange(5)
a[2::-1] = a[:1:-1]
assert_equal(a, [2, 3, 4, 3, 4])
a = np.arange(5)
a[:1:-1] = a[2::-1]
assert_equal(a, [0, 1, 0, 1, 2])
class TestCreation(TestCase):
def test_from_attribute(self):
class x(object):
def __array__(self, dtype=None):
pass
self.assertRaises(ValueError, array, x())
def test_from_string(self) :
types = np.typecodes['AllInteger'] + np.typecodes['Float']
nstr = ['123', '123']
result = array([123, 123], dtype=int)
for type in types :
msg = 'String conversion for %s' % type
assert_equal(array(nstr, dtype=type), result, err_msg=msg)
def test_void(self):
arr = np.array([], dtype='V')
assert_equal(arr.dtype.kind, 'V')
def test_zeros(self):
types = np.typecodes['AllInteger'] + np.typecodes['AllFloat']
for dt in types:
d = np.zeros((13,), dtype=dt)
assert_equal(np.count_nonzero(d), 0)
# true for ieee floats
assert_equal(d.sum(), 0)
assert_(not d.any())
d = np.zeros(2, dtype='(2,4)i4')
assert_equal(np.count_nonzero(d), 0)
assert_equal(d.sum(), 0)
assert_(not d.any())
d = np.zeros(2, dtype='4i4')
assert_equal(np.count_nonzero(d), 0)
assert_equal(d.sum(), 0)
assert_(not d.any())
d = np.zeros(2, dtype='(2,4)i4, (2,4)i4')
assert_equal(np.count_nonzero(d), 0)
@dec.slow
def test_zeros_big(self):
# test big array as they might be allocated different by the sytem
types = np.typecodes['AllInteger'] + np.typecodes['AllFloat']
for dt in types:
d = np.zeros((30 * 1024**2,), dtype=dt)
assert_(not d.any())
def test_zeros_obj(self):
# test initialization from PyLong(0)
d = np.zeros((13,), dtype=object)
assert_array_equal(d, [0] * 13)
assert_equal(np.count_nonzero(d), 0)
def test_zeros_obj_obj(self):
d = zeros(10, dtype=[('k', object, 2)])
assert_array_equal(d['k'], 0)
def test_zeros_like_like_zeros(self):
# test zeros_like returns the same as zeros
for c in np.typecodes['All']:
if c == 'V':
continue
d = zeros((3,3), dtype=c)
assert_array_equal(zeros_like(d), d)
assert_equal(zeros_like(d).dtype, d.dtype)
# explicitly check some special cases
d = zeros((3,3), dtype='S5')
assert_array_equal(zeros_like(d), d)
assert_equal(zeros_like(d).dtype, d.dtype)
d = zeros((3,3), dtype='U5')
assert_array_equal(zeros_like(d), d)
assert_equal(zeros_like(d).dtype, d.dtype)
d = zeros((3,3), dtype='<i4')
assert_array_equal(zeros_like(d), d)
assert_equal(zeros_like(d).dtype, d.dtype)
d = zeros((3,3), dtype='>i4')
assert_array_equal(zeros_like(d), d)
assert_equal(zeros_like(d).dtype, d.dtype)
d = zeros((3,3), dtype='<M8[s]')
assert_array_equal(zeros_like(d), d)
assert_equal(zeros_like(d).dtype, d.dtype)
d = zeros((3,3), dtype='>M8[s]')
assert_array_equal(zeros_like(d), d)
assert_equal(zeros_like(d).dtype, d.dtype)
d = zeros((3,3), dtype='f4,f4')
assert_array_equal(zeros_like(d), d)
assert_equal(zeros_like(d).dtype, d.dtype)
def test_empty_unicode(self):
# don't throw decode errors on garbage memory
for i in range(5, 100, 5):
d = np.empty(i, dtype='U')
str(d)
def test_sequence_non_homogenous(self):
assert_equal(np.array([4, 2**80]).dtype, np.object)
assert_equal(np.array([4, 2**80, 4]).dtype, np.object)
assert_equal(np.array([2**80, 4]).dtype, np.object)
assert_equal(np.array([2**80] * 3).dtype, np.object)
assert_equal(np.array([[1, 1],[1j, 1j]]).dtype, np.complex)
assert_equal(np.array([[1j, 1j],[1, 1]]).dtype, np.complex)
assert_equal(np.array([[1, 1, 1],[1, 1j, 1.], [1, 1, 1]]).dtype, np.complex)
@dec.skipif(sys.version_info[0] >= 3)
def test_sequence_long(self):
assert_equal(np.array([long(4), long(4)]).dtype, np.long)
assert_equal(np.array([long(4), 2**80]).dtype, np.object)
assert_equal(np.array([long(4), 2**80, long(4)]).dtype, np.object)
assert_equal(np.array([2**80, long(4)]).dtype, np.object)
def test_non_sequence_sequence(self):
"""Should not segfault.
Class Fail breaks the sequence protocol for new style classes, i.e.,
those derived from object. Class Map is a mapping type indicated by
raising a ValueError. At some point we may raise a warning instead
of an error in the Fail case.
"""
class Fail(object):
def __len__(self):
return 1
def __getitem__(self, index):
raise ValueError()
class Map(object):
def __len__(self):
return 1
def __getitem__(self, index):
raise KeyError()
a = np.array([Map()])
assert_(a.shape == (1,))
assert_(a.dtype == np.dtype(object))
assert_raises(ValueError, np.array, [Fail()])
def test_no_len_object_type(self):
# gh-5100, want object array from iterable object without len()
class Point2:
def __init__(self):
pass
def __getitem__(self, ind):
if ind in [0, 1]:
return ind
else:
raise IndexError()
d = np.array([Point2(), Point2(), Point2()])
assert_equal(d.dtype, np.dtype(object))
class TestStructured(TestCase):
def test_subarray_field_access(self):
a = np.zeros((3, 5), dtype=[('a', ('i4', (2, 2)))])
a['a'] = np.arange(60).reshape(3, 5, 2, 2)
# Since the subarray is always in C-order, a transpose
# does not swap the subarray:
assert_array_equal(a.T['a'], a['a'].transpose(1, 0, 2, 3))
# In Fortran order, the subarray gets appended
# like in all other cases, not prepended as a special case
b = a.copy(order='F')
assert_equal(a['a'].shape, b['a'].shape)
assert_equal(a.T['a'].shape, a.T.copy()['a'].shape)
def test_subarray_comparison(self):
# Check that comparisons between record arrays with
# multi-dimensional field types work properly
a = np.rec.fromrecords(
[([1, 2, 3], 'a', [[1, 2], [3, 4]]), ([3, 3, 3], 'b', [[0, 0], [0, 0]])],
dtype=[('a', ('f4', 3)), ('b', np.object), ('c', ('i4', (2, 2)))])
b = a.copy()
assert_equal(a==b, [True, True])
assert_equal(a!=b, [False, False])
b[1].b = 'c'
assert_equal(a==b, [True, False])
assert_equal(a!=b, [False, True])
for i in range(3):
b[0].a = a[0].a
b[0].a[i] = 5
assert_equal(a==b, [False, False])
assert_equal(a!=b, [True, True])
for i in range(2):
for j in range(2):
b = a.copy()
b[0].c[i, j] = 10
assert_equal(a==b, [False, True])
assert_equal(a!=b, [True, False])
# Check that broadcasting with a subarray works
a = np.array([[(0,)], [(1,)]], dtype=[('a', 'f8')])
b = np.array([(0,), (0,), (1,)], dtype=[('a', 'f8')])
assert_equal(a==b, [[True, True, False], [False, False, True]])
assert_equal(b==a, [[True, True, False], [False, False, True]])
a = np.array([[(0,)], [(1,)]], dtype=[('a', 'f8', (1,))])
b = np.array([(0,), (0,), (1,)], dtype=[('a', 'f8', (1,))])
assert_equal(a==b, [[True, True, False], [False, False, True]])
assert_equal(b==a, [[True, True, False], [False, False, True]])
a = np.array([[([0, 0],)], [([1, 1],)]], dtype=[('a', 'f8', (2,))])
b = np.array([([0, 0],), ([0, 1],), ([1, 1],)], dtype=[('a', 'f8', (2,))])
assert_equal(a==b, [[True, False, False], [False, False, True]])
assert_equal(b==a, [[True, False, False], [False, False, True]])
# Check that broadcasting Fortran-style arrays with a subarray work
a = np.array([[([0, 0],)], [([1, 1],)]], dtype=[('a', 'f8', (2,))], order='F')
b = np.array([([0, 0],), ([0, 1],), ([1, 1],)], dtype=[('a', 'f8', (2,))])
assert_equal(a==b, [[True, False, False], [False, False, True]])
assert_equal(b==a, [[True, False, False], [False, False, True]])
# Check that incompatible sub-array shapes don't result to broadcasting
x = np.zeros((1,), dtype=[('a', ('f4', (1, 2))), ('b', 'i1')])
y = np.zeros((1,), dtype=[('a', ('f4', (2,))), ('b', 'i1')])
# This comparison invokes deprecated behaviour, and will probably
# start raising an error eventually. What we really care about in this
# test is just that it doesn't return True.
with warnings.catch_warnings():
warnings.filterwarnings("ignore", category=DeprecationWarning)
assert_equal(x == y, False)
x = np.zeros((1,), dtype=[('a', ('f4', (2, 1))), ('b', 'i1')])
y = np.zeros((1,), dtype=[('a', ('f4', (2,))), ('b', 'i1')])
# This comparison invokes deprecated behaviour, and will probably
# start raising an error eventually. What we really care about in this
# test is just that it doesn't return True.
with warnings.catch_warnings():
warnings.filterwarnings("ignore", category=DeprecationWarning)
assert_equal(x == y, False)
# Check that structured arrays that are different only in
# byte-order work
a = np.array([(5, 42), (10, 1)], dtype=[('a', '>i8'), ('b', '<f8')])
b = np.array([(5, 43), (10, 1)], dtype=[('a', '<i8'), ('b', '>f8')])
assert_equal(a == b, [False, True])
def test_casting(self):
# Check that casting a structured array to change its byte order
# works
a = np.array([(1,)], dtype=[('a', '<i4')])
assert_(np.can_cast(a.dtype, [('a', '>i4')], casting='unsafe'))
b = a.astype([('a', '>i4')])
assert_equal(b, a.byteswap().newbyteorder())
assert_equal(a['a'][0], b['a'][0])
# Check that equality comparison works on structured arrays if
# they are 'equiv'-castable
a = np.array([(5, 42), (10, 1)], dtype=[('a', '>i4'), ('b', '<f8')])
b = np.array([(42, 5), (1, 10)], dtype=[('b', '>f8'), ('a', '<i4')])
assert_(np.can_cast(a.dtype, b.dtype, casting='equiv'))
assert_equal(a == b, [True, True])
# Check that 'equiv' casting can reorder fields and change byte
# order
assert_(np.can_cast(a.dtype, b.dtype, casting='equiv'))
c = a.astype(b.dtype, casting='equiv')
assert_equal(a == c, [True, True])
# Check that 'safe' casting can change byte order and up-cast
# fields
t = [('a', '<i8'), ('b', '>f8')]
assert_(np.can_cast(a.dtype, t, casting='safe'))
c = a.astype(t, casting='safe')
assert_equal((c == np.array([(5, 42), (10, 1)], dtype=t)),
[True, True])
# Check that 'same_kind' casting can change byte order and
# change field widths within a "kind"
t = [('a', '<i4'), ('b', '>f4')]
assert_(np.can_cast(a.dtype, t, casting='same_kind'))
c = a.astype(t, casting='same_kind')
assert_equal((c == np.array([(5, 42), (10, 1)], dtype=t)),
[True, True])
# Check that casting fails if the casting rule should fail on
# any of the fields
t = [('a', '>i8'), ('b', '<f4')]
assert_(not np.can_cast(a.dtype, t, casting='safe'))
assert_raises(TypeError, a.astype, t, casting='safe')
t = [('a', '>i2'), ('b', '<f8')]
assert_(not np.can_cast(a.dtype, t, casting='equiv'))
assert_raises(TypeError, a.astype, t, casting='equiv')
t = [('a', '>i8'), ('b', '<i2')]
assert_(not np.can_cast(a.dtype, t, casting='same_kind'))
assert_raises(TypeError, a.astype, t, casting='same_kind')
assert_(not np.can_cast(a.dtype, b.dtype, casting='no'))
assert_raises(TypeError, a.astype, b.dtype, casting='no')
# Check that non-'unsafe' casting can't change the set of field names
for casting in ['no', 'safe', 'equiv', 'same_kind']:
t = [('a', '>i4')]
assert_(not np.can_cast(a.dtype, t, casting=casting))
t = [('a', '>i4'), ('b', '<f8'), ('c', 'i4')]
assert_(not np.can_cast(a.dtype, t, casting=casting))
def test_objview(self):
# https://github.com/numpy/numpy/issues/3286
a = np.array([], dtype=[('a', 'f'), ('b', 'f'), ('c', 'O')])
a[['a', 'b']] # TypeError?
# https://github.com/numpy/numpy/issues/3253
dat2 = np.zeros(3, [('A', 'i'), ('B', '|O')])
new2 = dat2[['B', 'A']] # TypeError?
def test_setfield(self):
# https://github.com/numpy/numpy/issues/3126
struct_dt = np.dtype([('elem', 'i4', 5),])
dt = np.dtype([('field', 'i4', 10),('struct', struct_dt)])
x = np.zeros(1, dt)
x[0]['field'] = np.ones(10, dtype='i4')
x[0]['struct'] = np.ones(1, dtype=struct_dt)
assert_equal(x[0]['field'], np.ones(10, dtype='i4'))
def test_setfield_object(self):
# make sure object field assignment with ndarray value
# on void scalar mimics setitem behavior
b = np.zeros(1, dtype=[('x', 'O')])
# next line should work identically to b['x'][0] = np.arange(3)
b[0]['x'] = np.arange(3)
assert_equal(b[0]['x'], np.arange(3))
#check that broadcasting check still works
c = np.zeros(1, dtype=[('x', 'O', 5)])
def testassign():
c[0]['x'] = np.arange(3)
assert_raises(ValueError, testassign)
class TestBool(TestCase):
def test_test_interning(self):
a0 = bool_(0)
b0 = bool_(False)
self.assertTrue(a0 is b0)
a1 = bool_(1)
b1 = bool_(True)
self.assertTrue(a1 is b1)
self.assertTrue(array([True])[0] is a1)
self.assertTrue(array(True)[()] is a1)
def test_sum(self):
d = np.ones(101, dtype=np.bool);
assert_equal(d.sum(), d.size)
assert_equal(d[::2].sum(), d[::2].size)
assert_equal(d[::-2].sum(), d[::-2].size)
d = np.frombuffer(b'\xff\xff' * 100, dtype=bool)
assert_equal(d.sum(), d.size)
assert_equal(d[::2].sum(), d[::2].size)
assert_equal(d[::-2].sum(), d[::-2].size)
def check_count_nonzero(self, power, length):
powers = [2 ** i for i in range(length)]
for i in range(2**power):
l = [(i & x) != 0 for x in powers]
a = np.array(l, dtype=np.bool)
c = builtins.sum(l)
self.assertEqual(np.count_nonzero(a), c)
av = a.view(np.uint8)
av *= 3
self.assertEqual(np.count_nonzero(a), c)
av *= 4
self.assertEqual(np.count_nonzero(a), c)
av[av != 0] = 0xFF
self.assertEqual(np.count_nonzero(a), c)
def test_count_nonzero(self):
# check all 12 bit combinations in a length 17 array
# covers most cases of the 16 byte unrolled code
self.check_count_nonzero(12, 17)
@dec.slow
def test_count_nonzero_all(self):
# check all combinations in a length 17 array
# covers all cases of the 16 byte unrolled code
self.check_count_nonzero(17, 17)
def test_count_nonzero_unaligned(self):
# prevent mistakes as e.g. gh-4060
for o in range(7):
a = np.zeros((18,), dtype=np.bool)[o+1:]
a[:o] = True
self.assertEqual(np.count_nonzero(a), builtins.sum(a.tolist()))
a = np.ones((18,), dtype=np.bool)[o+1:]
a[:o] = False
self.assertEqual(np.count_nonzero(a), builtins.sum(a.tolist()))
class TestMethods(TestCase):
def test_round(self):
def check_round(arr, expected, *round_args):
assert_equal(arr.round(*round_args), expected)
# With output array
out = np.zeros_like(arr)
res = arr.round(*round_args, out=out)
assert_equal(out, expected)
assert_equal(out, res)
check_round(array([1.2, 1.5]), [1, 2])
check_round(array(1.5), 2)
check_round(array([12.2, 15.5]), [10, 20], -1)
check_round(array([12.15, 15.51]), [12.2, 15.5], 1)
# Complex rounding
check_round(array([4.5 + 1.5j]), [4 + 2j])
check_round(array([12.5 + 15.5j]), [10 + 20j], -1)
def test_transpose(self):
a = array([[1, 2], [3, 4]])
assert_equal(a.transpose(), [[1, 3], [2, 4]])
self.assertRaises(ValueError, lambda: a.transpose(0))
self.assertRaises(ValueError, lambda: a.transpose(0, 0))
self.assertRaises(ValueError, lambda: a.transpose(0, 1, 2))
def test_sort(self):
# test ordering for floats and complex containing nans. It is only
# necessary to check the lessthan comparison, so sorts that
# only follow the insertion sort path are sufficient. We only
# test doubles and complex doubles as the logic is the same.
# check doubles
msg = "Test real sort order with nans"
a = np.array([np.nan, 1, 0])
b = sort(a)
assert_equal(b, a[::-1], msg)
# check complex
msg = "Test complex sort order with nans"
a = np.zeros(9, dtype=np.complex128)
a.real += [np.nan, np.nan, np.nan, 1, 0, 1, 1, 0, 0]
a.imag += [np.nan, 1, 0, np.nan, np.nan, 1, 0, 1, 0]
b = sort(a)
assert_equal(b, a[::-1], msg)
# all c scalar sorts use the same code with different types
# so it suffices to run a quick check with one type. The number
# of sorted items must be greater than ~50 to check the actual
# algorithm because quick and merge sort fall over to insertion
# sort for small arrays.
a = np.arange(101)
b = a[::-1].copy()
for kind in ['q', 'm', 'h'] :
msg = "scalar sort, kind=%s" % kind
c = a.copy();
c.sort(kind=kind)
assert_equal(c, a, msg)
c = b.copy();
c.sort(kind=kind)
assert_equal(c, a, msg)
# test complex sorts. These use the same code as the scalars
# but the compare function differs.
ai = a*1j + 1
bi = b*1j + 1
for kind in ['q', 'm', 'h'] :
msg = "complex sort, real part == 1, kind=%s" % kind
c = ai.copy();
c.sort(kind=kind)
assert_equal(c, ai, msg)
c = bi.copy();
c.sort(kind=kind)
assert_equal(c, ai, msg)
ai = a + 1j
bi = b + 1j
for kind in ['q', 'm', 'h'] :
msg = "complex sort, imag part == 1, kind=%s" % kind
c = ai.copy();
c.sort(kind=kind)
assert_equal(c, ai, msg)
c = bi.copy();
c.sort(kind=kind)
assert_equal(c, ai, msg)
# test sorting of complex arrays requiring byte-swapping, gh-5441
for endianess in '<>':
for dt in np.typecodes['Complex']:
dtype = '{0}{1}'.format(endianess, dt)
arr = np.array([1+3.j, 2+2.j, 3+1.j], dtype=dt)
c = arr.copy()
c.sort()
msg = 'byte-swapped complex sort, dtype={0}'.format(dt)
assert_equal(c, arr, msg)
# test string sorts.
s = 'aaaaaaaa'
a = np.array([s + chr(i) for i in range(101)])
b = a[::-1].copy()
for kind in ['q', 'm', 'h'] :
msg = "string sort, kind=%s" % kind
c = a.copy();
c.sort(kind=kind)
assert_equal(c, a, msg)
c = b.copy();
c.sort(kind=kind)
assert_equal(c, a, msg)
# test unicode sorts.
s = 'aaaaaaaa'
a = np.array([s + chr(i) for i in range(101)], dtype=np.unicode)
b = a[::-1].copy()
for kind in ['q', 'm', 'h'] :
msg = "unicode sort, kind=%s" % kind
c = a.copy();
c.sort(kind=kind)
assert_equal(c, a, msg)
c = b.copy();
c.sort(kind=kind)
assert_equal(c, a, msg)
# test object array sorts.
a = np.empty((101,), dtype=np.object)
a[:] = list(range(101))
b = a[::-1]
for kind in ['q', 'h', 'm'] :
msg = "object sort, kind=%s" % kind
c = a.copy();
c.sort(kind=kind)
assert_equal(c, a, msg)
c = b.copy();
c.sort(kind=kind)
assert_equal(c, a, msg)
# test record array sorts.
dt = np.dtype([('f', float), ('i', int)])
a = array([(i, i) for i in range(101)], dtype = dt)
b = a[::-1]
for kind in ['q', 'h', 'm'] :
msg = "object sort, kind=%s" % kind
c = a.copy();
c.sort(kind=kind)
assert_equal(c, a, msg)
c = b.copy();
c.sort(kind=kind)
assert_equal(c, a, msg)
# test datetime64 sorts.
a = np.arange(0, 101, dtype='datetime64[D]')
b = a[::-1]
for kind in ['q', 'h', 'm'] :
msg = "datetime64 sort, kind=%s" % kind
c = a.copy();
c.sort(kind=kind)
assert_equal(c, a, msg)
c = b.copy();
c.sort(kind=kind)
assert_equal(c, a, msg)
# test timedelta64 sorts.
a = np.arange(0, 101, dtype='timedelta64[D]')
b = a[::-1]
for kind in ['q', 'h', 'm'] :
msg = "timedelta64 sort, kind=%s" % kind
c = a.copy();
c.sort(kind=kind)
assert_equal(c, a, msg)
c = b.copy();
c.sort(kind=kind)
assert_equal(c, a, msg)
# check axis handling. This should be the same for all type
# specific sorts, so we only check it for one type and one kind
a = np.array([[3, 2], [1, 0]])
b = np.array([[1, 0], [3, 2]])
c = np.array([[2, 3], [0, 1]])
d = a.copy()
d.sort(axis=0)
assert_equal(d, b, "test sort with axis=0")
d = a.copy()
d.sort(axis=1)
assert_equal(d, c, "test sort with axis=1")
d = a.copy()
d.sort()
assert_equal(d, c, "test sort with default axis")
# check axis handling for multidimensional empty arrays
a = np.array([])
a.shape = (3, 2, 1, 0)
for axis in range(-a.ndim, a.ndim):
msg = 'test empty array sort with axis={0}'.format(axis)
assert_equal(np.sort(a, axis=axis), a, msg)
msg = 'test empty array sort with axis=None'
assert_equal(np.sort(a, axis=None), a.ravel(), msg)
def test_copy(self):
def assert_fortran(arr):
assert_(arr.flags.fortran)
assert_(arr.flags.f_contiguous)
assert_(not arr.flags.c_contiguous)
def assert_c(arr):
assert_(not arr.flags.fortran)
assert_(not arr.flags.f_contiguous)
assert_(arr.flags.c_contiguous)
a = np.empty((2, 2), order='F')
# Test copying a Fortran array
assert_c(a.copy())
assert_c(a.copy('C'))
assert_fortran(a.copy('F'))
assert_fortran(a.copy('A'))
# Now test starting with a C array.
a = np.empty((2, 2), order='C')
assert_c(a.copy())
assert_c(a.copy('C'))
assert_fortran(a.copy('F'))
assert_c(a.copy('A'))
def test_sort_order(self):
# Test sorting an array with fields
x1=np.array([21, 32, 14])
x2=np.array(['my', 'first', 'name'])
x3=np.array([3.1, 4.5, 6.2])
r=np.rec.fromarrays([x1, x2, x3], names='id,word,number')
r.sort(order=['id'])
assert_equal(r.id, array([14, 21, 32]))
assert_equal(r.word, array(['name', 'my', 'first']))
assert_equal(r.number, array([6.2, 3.1, 4.5]))
r.sort(order=['word'])
assert_equal(r.id, array([32, 21, 14]))
assert_equal(r.word, array(['first', 'my', 'name']))
assert_equal(r.number, array([4.5, 3.1, 6.2]))
r.sort(order=['number'])
assert_equal(r.id, array([21, 32, 14]))
assert_equal(r.word, array(['my', 'first', 'name']))
assert_equal(r.number, array([3.1, 4.5, 6.2]))
if sys.byteorder == 'little':
strtype = '>i2'
else:
strtype = '<i2'
mydtype = [('name', strchar + '5'), ('col2', strtype)]
r = np.array([('a', 1), ('b', 255), ('c', 3), ('d', 258)],
dtype= mydtype)
r.sort(order='col2')
assert_equal(r['col2'], [1, 3, 255, 258])
assert_equal(r, np.array([('a', 1), ('c', 3), ('b', 255), ('d', 258)],
dtype=mydtype))
def test_argsort(self):
# all c scalar argsorts use the same code with different types
# so it suffices to run a quick check with one type. The number
# of sorted items must be greater than ~50 to check the actual
# algorithm because quick and merge sort fall over to insertion
# sort for small arrays.
a = np.arange(101)
b = a[::-1].copy()
for kind in ['q', 'm', 'h'] :
msg = "scalar argsort, kind=%s" % kind
assert_equal(a.copy().argsort(kind=kind), a, msg)
assert_equal(b.copy().argsort(kind=kind), b, msg)
# test complex argsorts. These use the same code as the scalars
# but the compare fuction differs.
ai = a*1j + 1
bi = b*1j + 1
for kind in ['q', 'm', 'h'] :
msg = "complex argsort, kind=%s" % kind
assert_equal(ai.copy().argsort(kind=kind), a, msg)
assert_equal(bi.copy().argsort(kind=kind), b, msg)
ai = a + 1j
bi = b + 1j
for kind in ['q', 'm', 'h'] :
msg = "complex argsort, kind=%s" % kind
assert_equal(ai.copy().argsort(kind=kind), a, msg)
assert_equal(bi.copy().argsort(kind=kind), b, msg)
# test argsort of complex arrays requiring byte-swapping, gh-5441
for endianess in '<>':
for dt in np.typecodes['Complex']:
dtype = '{0}{1}'.format(endianess, dt)
arr = np.array([1+3.j, 2+2.j, 3+1.j], dtype=dt)
msg = 'byte-swapped complex argsort, dtype={0}'.format(dt)
assert_equal(arr.argsort(),
np.arange(len(arr), dtype=np.intp), msg)
# test string argsorts.
s = 'aaaaaaaa'
a = np.array([s + chr(i) for i in range(101)])
b = a[::-1].copy()
r = np.arange(101)
rr = r[::-1]
for kind in ['q', 'm', 'h'] :
msg = "string argsort, kind=%s" % kind
assert_equal(a.copy().argsort(kind=kind), r, msg)
assert_equal(b.copy().argsort(kind=kind), rr, msg)
# test unicode argsorts.
s = 'aaaaaaaa'
a = np.array([s + chr(i) for i in range(101)], dtype=np.unicode)
b = a[::-1]
r = np.arange(101)
rr = r[::-1]
for kind in ['q', 'm', 'h'] :
msg = "unicode argsort, kind=%s" % kind
assert_equal(a.copy().argsort(kind=kind), r, msg)
assert_equal(b.copy().argsort(kind=kind), rr, msg)
# test object array argsorts.
a = np.empty((101,), dtype=np.object)
a[:] = list(range(101))
b = a[::-1]
r = np.arange(101)
rr = r[::-1]
for kind in ['q', 'm', 'h'] :
msg = "object argsort, kind=%s" % kind
assert_equal(a.copy().argsort(kind=kind), r, msg)
assert_equal(b.copy().argsort(kind=kind), rr, msg)
# test structured array argsorts.
dt = np.dtype([('f', float), ('i', int)])
a = array([(i, i) for i in range(101)], dtype = dt)
b = a[::-1]
r = np.arange(101)
rr = r[::-1]
for kind in ['q', 'm', 'h'] :
msg = "structured array argsort, kind=%s" % kind
assert_equal(a.copy().argsort(kind=kind), r, msg)
assert_equal(b.copy().argsort(kind=kind), rr, msg)
# test datetime64 argsorts.
a = np.arange(0, 101, dtype='datetime64[D]')
b = a[::-1]
r = np.arange(101)
rr = r[::-1]
for kind in ['q', 'h', 'm'] :
msg = "datetime64 argsort, kind=%s" % kind
assert_equal(a.copy().argsort(kind=kind), r, msg)
assert_equal(b.copy().argsort(kind=kind), rr, msg)
# test timedelta64 argsorts.
a = np.arange(0, 101, dtype='timedelta64[D]')
b = a[::-1]
r = np.arange(101)
rr = r[::-1]
for kind in ['q', 'h', 'm'] :
msg = "timedelta64 argsort, kind=%s" % kind
assert_equal(a.copy().argsort(kind=kind), r, msg)
assert_equal(b.copy().argsort(kind=kind), rr, msg)
# check axis handling. This should be the same for all type
# specific argsorts, so we only check it for one type and one kind
a = np.array([[3, 2], [1, 0]])
b = np.array([[1, 1], [0, 0]])
c = np.array([[1, 0], [1, 0]])
assert_equal(a.copy().argsort(axis=0), b)
assert_equal(a.copy().argsort(axis=1), c)
assert_equal(a.copy().argsort(), c)
# using None is known fail at this point
#assert_equal(a.copy().argsort(axis=None, c)
# check axis handling for multidimensional empty arrays
a = np.array([])
a.shape = (3, 2, 1, 0)
for axis in range(-a.ndim, a.ndim):
msg = 'test empty array argsort with axis={0}'.format(axis)
assert_equal(np.argsort(a, axis=axis),
np.zeros_like(a, dtype=np.intp), msg)
msg = 'test empty array argsort with axis=None'
assert_equal(np.argsort(a, axis=None),
np.zeros_like(a.ravel(), dtype=np.intp), msg)
# check that stable argsorts are stable
r = np.arange(100)
# scalars
a = np.zeros(100)
assert_equal(a.argsort(kind='m'), r)
# complex
a = np.zeros(100, dtype=np.complex)
assert_equal(a.argsort(kind='m'), r)
# string
a = np.array(['aaaaaaaaa' for i in range(100)])
assert_equal(a.argsort(kind='m'), r)
# unicode
a = np.array(['aaaaaaaaa' for i in range(100)], dtype=np.unicode)
assert_equal(a.argsort(kind='m'), r)
def test_sort_unicode_kind(self):
d = np.arange(10)
k = b'\xc3\xa4'.decode("UTF8")
assert_raises(ValueError, d.sort, kind=k)
assert_raises(ValueError, d.argsort, kind=k)
def test_searchsorted(self):
# test for floats and complex containing nans. The logic is the
# same for all float types so only test double types for now.
# The search sorted routines use the compare functions for the
# array type, so this checks if that is consistent with the sort
# order.
# check double
a = np.array([0, 1, np.nan])
msg = "Test real searchsorted with nans, side='l'"
b = a.searchsorted(a, side='l')
assert_equal(b, np.arange(3), msg)
msg = "Test real searchsorted with nans, side='r'"
b = a.searchsorted(a, side='r')
assert_equal(b, np.arange(1, 4), msg)
# check double complex
a = np.zeros(9, dtype=np.complex128)
a.real += [0, 0, 1, 1, 0, 1, np.nan, np.nan, np.nan]
a.imag += [0, 1, 0, 1, np.nan, np.nan, 0, 1, np.nan]
msg = "Test complex searchsorted with nans, side='l'"
b = a.searchsorted(a, side='l')
assert_equal(b, np.arange(9), msg)
msg = "Test complex searchsorted with nans, side='r'"
b = a.searchsorted(a, side='r')
assert_equal(b, np.arange(1, 10), msg)
msg = "Test searchsorted with little endian, side='l'"
a = np.array([0, 128], dtype='<i4')
b = a.searchsorted(np.array(128, dtype='<i4'))
assert_equal(b, 1, msg)
msg = "Test searchsorted with big endian, side='l'"
a = np.array([0, 128], dtype='>i4')
b = a.searchsorted(np.array(128, dtype='>i4'))
assert_equal(b, 1, msg)
# Check 0 elements
a = np.ones(0)
b = a.searchsorted([0, 1, 2], 'l')
assert_equal(b, [0, 0, 0])
b = a.searchsorted([0, 1, 2], 'r')
assert_equal(b, [0, 0, 0])
a = np.ones(1)
# Check 1 element
b = a.searchsorted([0, 1, 2], 'l')
assert_equal(b, [0, 0, 1])
b = a.searchsorted([0, 1, 2], 'r')
assert_equal(b, [0, 1, 1])
# Check all elements equal
a = np.ones(2)
b = a.searchsorted([0, 1, 2], 'l')
assert_equal(b, [0, 0, 2])
b = a.searchsorted([0, 1, 2], 'r')
assert_equal(b, [0, 2, 2])
# Test searching unaligned array
a = np.arange(10)
aligned = np.empty(a.itemsize * a.size + 1, 'uint8')
unaligned = aligned[1:].view(a.dtype)
unaligned[:] = a
# Test searching unaligned array
b = unaligned.searchsorted(a, 'l')
assert_equal(b, a)
b = unaligned.searchsorted(a, 'r')
assert_equal(b, a + 1)
# Test searching for unaligned keys
b = a.searchsorted(unaligned, 'l')
assert_equal(b, a)
b = a.searchsorted(unaligned, 'r')
assert_equal(b, a + 1)
# Test smart resetting of binsearch indices
a = np.arange(5)
b = a.searchsorted([6, 5, 4], 'l')
assert_equal(b, [5, 5, 4])
b = a.searchsorted([6, 5, 4], 'r')
assert_equal(b, [5, 5, 5])
# Test all type specific binary search functions
types = ''.join((np.typecodes['AllInteger'], np.typecodes['AllFloat'],
np.typecodes['Datetime'], '?O'))
for dt in types:
if dt == 'M':
dt = 'M8[D]'
if dt == '?':
a = np.arange(2, dtype=dt)
out = np.arange(2)
else:
a = np.arange(0, 5, dtype=dt)
out = np.arange(5)
b = a.searchsorted(a, 'l')
assert_equal(b, out)
b = a.searchsorted(a, 'r')
assert_equal(b, out + 1)
def test_searchsorted_unicode(self):
# Test searchsorted on unicode strings.
# 1.6.1 contained a string length miscalculation in
# arraytypes.c.src:UNICODE_compare() which manifested as
# incorrect/inconsistent results from searchsorted.
a = np.array(['P:\\20x_dapi_cy3\\20x_dapi_cy3_20100185_1',
'P:\\20x_dapi_cy3\\20x_dapi_cy3_20100186_1',
'P:\\20x_dapi_cy3\\20x_dapi_cy3_20100187_1',
'P:\\20x_dapi_cy3\\20x_dapi_cy3_20100189_1',
'P:\\20x_dapi_cy3\\20x_dapi_cy3_20100190_1',
'P:\\20x_dapi_cy3\\20x_dapi_cy3_20100191_1',
'P:\\20x_dapi_cy3\\20x_dapi_cy3_20100192_1',
'P:\\20x_dapi_cy3\\20x_dapi_cy3_20100193_1',
'P:\\20x_dapi_cy3\\20x_dapi_cy3_20100194_1',
'P:\\20x_dapi_cy3\\20x_dapi_cy3_20100195_1',
'P:\\20x_dapi_cy3\\20x_dapi_cy3_20100196_1',
'P:\\20x_dapi_cy3\\20x_dapi_cy3_20100197_1',
'P:\\20x_dapi_cy3\\20x_dapi_cy3_20100198_1',
'P:\\20x_dapi_cy3\\20x_dapi_cy3_20100199_1'],
dtype=np.unicode)
ind = np.arange(len(a))
assert_equal([a.searchsorted(v, 'left') for v in a], ind)
assert_equal([a.searchsorted(v, 'right') for v in a], ind + 1)
assert_equal([a.searchsorted(a[i], 'left') for i in ind], ind)
assert_equal([a.searchsorted(a[i], 'right') for i in ind], ind + 1)
def test_searchsorted_with_sorter(self):
a = np.array([5, 2, 1, 3, 4])
s = np.argsort(a)
assert_raises(TypeError, np.searchsorted, a, 0, sorter=(1, (2, 3)))
assert_raises(TypeError, np.searchsorted, a, 0, sorter=[1.1])
assert_raises(ValueError, np.searchsorted, a, 0, sorter=[1, 2, 3, 4])
assert_raises(ValueError, np.searchsorted, a, 0, sorter=[1, 2, 3, 4, 5, 6])
# bounds check
assert_raises(ValueError, np.searchsorted, a, 4, sorter=[0, 1, 2, 3, 5])
assert_raises(ValueError, np.searchsorted, a, 0, sorter=[-1, 0, 1, 2, 3])
assert_raises(ValueError, np.searchsorted, a, 0, sorter=[4, 0, -1, 2, 3])
a = np.random.rand(300)
s = a.argsort()
b = np.sort(a)
k = np.linspace(0, 1, 20)
assert_equal(b.searchsorted(k), a.searchsorted(k, sorter=s))
a = np.array([0, 1, 2, 3, 5]*20)
s = a.argsort()
k = [0, 1, 2, 3, 5]
expected = [0, 20, 40, 60, 80]
assert_equal(a.searchsorted(k, side='l', sorter=s), expected)
expected = [20, 40, 60, 80, 100]
assert_equal(a.searchsorted(k, side='r', sorter=s), expected)
# Test searching unaligned array
keys = np.arange(10)
a = keys.copy()
np.random.shuffle(s)
s = a.argsort()
aligned = np.empty(a.itemsize * a.size + 1, 'uint8')
unaligned = aligned[1:].view(a.dtype)
# Test searching unaligned array
unaligned[:] = a
b = unaligned.searchsorted(keys, 'l', s)
assert_equal(b, keys)
b = unaligned.searchsorted(keys, 'r', s)
assert_equal(b, keys + 1)
# Test searching for unaligned keys
unaligned[:] = keys
b = a.searchsorted(unaligned, 'l', s)
assert_equal(b, keys)
b = a.searchsorted(unaligned, 'r', s)
assert_equal(b, keys + 1)
# Test all type specific indirect binary search functions
types = ''.join((np.typecodes['AllInteger'], np.typecodes['AllFloat'],
np.typecodes['Datetime'], '?O'))
for dt in types:
if dt == 'M':
dt = 'M8[D]'
if dt == '?':
a = np.array([1, 0], dtype=dt)
# We want the sorter array to be of a type that is different
# from np.intp in all platforms, to check for #4698
s = np.array([1, 0], dtype=np.int16)
out = np.array([1, 0])
else:
a = np.array([3, 4, 1, 2, 0], dtype=dt)
# We want the sorter array to be of a type that is different
# from np.intp in all platforms, to check for #4698
s = np.array([4, 2, 3, 0, 1], dtype=np.int16)
out = np.array([3, 4, 1, 2, 0], dtype=np.intp)
b = a.searchsorted(a, 'l', s)
assert_equal(b, out)
b = a.searchsorted(a, 'r', s)
assert_equal(b, out + 1)
# Test non-contiguous sorter array
a = np.array([3, 4, 1, 2, 0])
srt = np.empty((10,), dtype=np.intp)
srt[1::2] = -1
srt[::2] = [4, 2, 3, 0, 1]
s = srt[::2]
out = np.array([3, 4, 1, 2, 0], dtype=np.intp)
b = a.searchsorted(a, 'l', s)
assert_equal(b, out)
b = a.searchsorted(a, 'r', s)
assert_equal(b, out + 1)
def test_argpartition_out_of_range(self):
# Test out of range values in kth raise an error, gh-5469
d = np.arange(10)
assert_raises(ValueError, d.argpartition, 10)
assert_raises(ValueError, d.argpartition, -11)
# Test also for generic type argpartition, which uses sorting
# and used to not bound check kth
d_obj = np.arange(10, dtype=object)
assert_raises(ValueError, d_obj.argpartition, 10)
assert_raises(ValueError, d_obj.argpartition, -11)
def test_partition_out_of_range(self):
# Test out of range values in kth raise an error, gh-5469
d = np.arange(10)
assert_raises(ValueError, d.partition, 10)
assert_raises(ValueError, d.partition, -11)
# Test also for generic type partition, which uses sorting
# and used to not bound check kth
d_obj = np.arange(10, dtype=object)
assert_raises(ValueError, d_obj.partition, 10)
assert_raises(ValueError, d_obj.partition, -11)
def test_partition_empty_array(self):
# check axis handling for multidimensional empty arrays
a = np.array([])
a.shape = (3, 2, 1, 0)
for axis in range(-a.ndim, a.ndim):
msg = 'test empty array partition with axis={0}'.format(axis)
assert_equal(np.partition(a, 0, axis=axis), a, msg)
msg = 'test empty array partition with axis=None'
assert_equal(np.partition(a, 0, axis=None), a.ravel(), msg)
def test_argpartition_empty_array(self):
# check axis handling for multidimensional empty arrays
a = np.array([])
a.shape = (3, 2, 1, 0)
for axis in range(-a.ndim, a.ndim):
msg = 'test empty array argpartition with axis={0}'.format(axis)
assert_equal(np.partition(a, 0, axis=axis),
np.zeros_like(a, dtype=np.intp), msg)
msg = 'test empty array argpartition with axis=None'
assert_equal(np.partition(a, 0, axis=None),
np.zeros_like(a.ravel(), dtype=np.intp), msg)
def test_partition(self):
d = np.arange(10)
assert_raises(TypeError, np.partition, d, 2, kind=1)
assert_raises(ValueError, np.partition, d, 2, kind="nonsense")
assert_raises(ValueError, np.argpartition, d, 2, kind="nonsense")
assert_raises(ValueError, d.partition, 2, axis=0, kind="nonsense")
assert_raises(ValueError, d.argpartition, 2, axis=0, kind="nonsense")
for k in ("introselect",):
d = np.array([])
assert_array_equal(np.partition(d, 0, kind=k), d)
assert_array_equal(np.argpartition(d, 0, kind=k), d)
d = np.ones((1))
assert_array_equal(np.partition(d, 0, kind=k)[0], d)
assert_array_equal(d[np.argpartition(d, 0, kind=k)],
np.partition(d, 0, kind=k))
# kth not modified
kth = np.array([30, 15, 5])
okth = kth.copy()
np.partition(np.arange(40), kth)
assert_array_equal(kth, okth)
for r in ([2, 1], [1, 2], [1, 1]):
d = np.array(r)
tgt = np.sort(d)
assert_array_equal(np.partition(d, 0, kind=k)[0], tgt[0])
assert_array_equal(np.partition(d, 1, kind=k)[1], tgt[1])
assert_array_equal(d[np.argpartition(d, 0, kind=k)],
np.partition(d, 0, kind=k))
assert_array_equal(d[np.argpartition(d, 1, kind=k)],
np.partition(d, 1, kind=k))
for i in range(d.size):
d[i:].partition(0, kind=k)
assert_array_equal(d, tgt)
for r in ([3, 2, 1], [1, 2, 3], [2, 1, 3], [2, 3, 1],
[1, 1, 1], [1, 2, 2], [2, 2, 1], [1, 2, 1]):
d = np.array(r)
tgt = np.sort(d)
assert_array_equal(np.partition(d, 0, kind=k)[0], tgt[0])
assert_array_equal(np.partition(d, 1, kind=k)[1], tgt[1])
assert_array_equal(np.partition(d, 2, kind=k)[2], tgt[2])
assert_array_equal(d[np.argpartition(d, 0, kind=k)],
np.partition(d, 0, kind=k))
assert_array_equal(d[np.argpartition(d, 1, kind=k)],
np.partition(d, 1, kind=k))
assert_array_equal(d[np.argpartition(d, 2, kind=k)],
np.partition(d, 2, kind=k))
for i in range(d.size):
d[i:].partition(0, kind=k)
assert_array_equal(d, tgt)
d = np.ones((50))
assert_array_equal(np.partition(d, 0, kind=k), d)
assert_array_equal(d[np.argpartition(d, 0, kind=k)],
np.partition(d, 0, kind=k))
# sorted
d = np.arange((49))
self.assertEqual(np.partition(d, 5, kind=k)[5], 5)
self.assertEqual(np.partition(d, 15, kind=k)[15], 15)
assert_array_equal(d[np.argpartition(d, 5, kind=k)],
np.partition(d, 5, kind=k))
assert_array_equal(d[np.argpartition(d, 15, kind=k)],
np.partition(d, 15, kind=k))
# rsorted
d = np.arange((47))[::-1]
self.assertEqual(np.partition(d, 6, kind=k)[6], 6)
self.assertEqual(np.partition(d, 16, kind=k)[16], 16)
assert_array_equal(d[np.argpartition(d, 6, kind=k)],
np.partition(d, 6, kind=k))
assert_array_equal(d[np.argpartition(d, 16, kind=k)],
np.partition(d, 16, kind=k))
assert_array_equal(np.partition(d, -6, kind=k),
np.partition(d, 41, kind=k))
assert_array_equal(np.partition(d, -16, kind=k),
np.partition(d, 31, kind=k))
assert_array_equal(d[np.argpartition(d, -6, kind=k)],
np.partition(d, 41, kind=k))
# median of 3 killer, O(n^2) on pure median 3 pivot quickselect
# exercises the median of median of 5 code used to keep O(n)
d = np.arange(1000000)
x = np.roll(d, d.size // 2)
mid = x.size // 2 + 1
assert_equal(np.partition(x, mid)[mid], mid)
d = np.arange(1000001)
x = np.roll(d, d.size // 2 + 1)
mid = x.size // 2 + 1
assert_equal(np.partition(x, mid)[mid], mid)
# max
d = np.ones(10); d[1] = 4;
assert_equal(np.partition(d, (2, -1))[-1], 4)
assert_equal(np.partition(d, (2, -1))[2], 1)
assert_equal(d[np.argpartition(d, (2, -1))][-1], 4)
assert_equal(d[np.argpartition(d, (2, -1))][2], 1)
d[1] = np.nan
assert_(np.isnan(d[np.argpartition(d, (2, -1))][-1]))
assert_(np.isnan(np.partition(d, (2, -1))[-1]))
# equal elements
d = np.arange((47)) % 7
tgt = np.sort(np.arange((47)) % 7)
np.random.shuffle(d)
for i in range(d.size):
self.assertEqual(np.partition(d, i, kind=k)[i], tgt[i])
assert_array_equal(d[np.argpartition(d, 6, kind=k)],
np.partition(d, 6, kind=k))
assert_array_equal(d[np.argpartition(d, 16, kind=k)],
np.partition(d, 16, kind=k))
for i in range(d.size):
d[i:].partition(0, kind=k)
assert_array_equal(d, tgt)
d = np.array([0, 1, 2, 3, 4, 5, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7,
7, 7, 7, 7, 7, 9])
kth = [0, 3, 19, 20]
assert_equal(np.partition(d, kth, kind=k)[kth], (0, 3, 7, 7))
assert_equal(d[np.argpartition(d, kth, kind=k)][kth], (0, 3, 7, 7))
d = np.array([2, 1])
d.partition(0, kind=k)
assert_raises(ValueError, d.partition, 2)
assert_raises(ValueError, d.partition, 3, axis=1)
assert_raises(ValueError, np.partition, d, 2)
assert_raises(ValueError, np.partition, d, 2, axis=1)
assert_raises(ValueError, d.argpartition, 2)
assert_raises(ValueError, d.argpartition, 3, axis=1)
assert_raises(ValueError, np.argpartition, d, 2)
assert_raises(ValueError, np.argpartition, d, 2, axis=1)
d = np.arange(10).reshape((2, 5))
d.partition(1, axis=0, kind=k)
d.partition(4, axis=1, kind=k)
np.partition(d, 1, axis=0, kind=k)
np.partition(d, 4, axis=1, kind=k)
np.partition(d, 1, axis=None, kind=k)
np.partition(d, 9, axis=None, kind=k)
d.argpartition(1, axis=0, kind=k)
d.argpartition(4, axis=1, kind=k)
np.argpartition(d, 1, axis=0, kind=k)
np.argpartition(d, 4, axis=1, kind=k)
np.argpartition(d, 1, axis=None, kind=k)
np.argpartition(d, 9, axis=None, kind=k)
assert_raises(ValueError, d.partition, 2, axis=0)
assert_raises(ValueError, d.partition, 11, axis=1)
assert_raises(TypeError, d.partition, 2, axis=None)
assert_raises(ValueError, np.partition, d, 9, axis=1)
assert_raises(ValueError, np.partition, d, 11, axis=None)
assert_raises(ValueError, d.argpartition, 2, axis=0)
assert_raises(ValueError, d.argpartition, 11, axis=1)
assert_raises(ValueError, np.argpartition, d, 9, axis=1)
assert_raises(ValueError, np.argpartition, d, 11, axis=None)
td = [(dt, s) for dt in [np.int32, np.float32, np.complex64]
for s in (9, 16)]
for dt, s in td:
aae = assert_array_equal
at = self.assertTrue
d = np.arange(s, dtype=dt)
np.random.shuffle(d)
d1 = np.tile(np.arange(s, dtype=dt), (4, 1))
map(np.random.shuffle, d1)
d0 = np.transpose(d1)
for i in range(d.size):
p = np.partition(d, i, kind=k)
self.assertEqual(p[i], i)
# all before are smaller
assert_array_less(p[:i], p[i])
# all after are larger
assert_array_less(p[i], p[i + 1:])
aae(p, d[np.argpartition(d, i, kind=k)])
p = np.partition(d1, i, axis=1, kind=k)
aae(p[:, i], np.array([i] * d1.shape[0], dtype=dt))
# array_less does not seem to work right
at((p[:, :i].T <= p[:, i]).all(),
msg="%d: %r <= %r" % (i, p[:, i], p[:, :i].T))
at((p[:, i + 1:].T > p[:, i]).all(),
msg="%d: %r < %r" % (i, p[:, i], p[:, i + 1:].T))
aae(p, d1[np.arange(d1.shape[0])[:, None],
np.argpartition(d1, i, axis=1, kind=k)])
p = np.partition(d0, i, axis=0, kind=k)
aae(p[i,:], np.array([i] * d1.shape[0],
dtype=dt))
# array_less does not seem to work right
at((p[:i,:] <= p[i,:]).all(),
msg="%d: %r <= %r" % (i, p[i,:], p[:i,:]))
at((p[i + 1:,:] > p[i,:]).all(),
msg="%d: %r < %r" % (i, p[i,:], p[:, i + 1:]))
aae(p, d0[np.argpartition(d0, i, axis=0, kind=k),
np.arange(d0.shape[1])[None,:]])
# check inplace
dc = d.copy()
dc.partition(i, kind=k)
assert_equal(dc, np.partition(d, i, kind=k))
dc = d0.copy()
dc.partition(i, axis=0, kind=k)
assert_equal(dc, np.partition(d0, i, axis=0, kind=k))
dc = d1.copy()
dc.partition(i, axis=1, kind=k)
assert_equal(dc, np.partition(d1, i, axis=1, kind=k))
def assert_partitioned(self, d, kth):
prev = 0
for k in np.sort(kth):
assert_array_less(d[prev:k], d[k], err_msg='kth %d' % k)
assert_((d[k:] >= d[k]).all(),
msg="kth %d, %r not greater equal %d" % (k, d[k:], d[k]))
prev = k + 1
def test_partition_iterative(self):
d = np.arange(17)
kth = (0, 1, 2, 429, 231)
assert_raises(ValueError, d.partition, kth)
assert_raises(ValueError, d.argpartition, kth)
d = np.arange(10).reshape((2, 5))
assert_raises(ValueError, d.partition, kth, axis=0)
assert_raises(ValueError, d.partition, kth, axis=1)
assert_raises(ValueError, np.partition, d, kth, axis=1)
assert_raises(ValueError, np.partition, d, kth, axis=None)
d = np.array([3, 4, 2, 1])
p = np.partition(d, (0, 3))
self.assert_partitioned(p, (0, 3))
self.assert_partitioned(d[np.argpartition(d, (0, 3))], (0, 3))
assert_array_equal(p, np.partition(d, (-3, -1)))
assert_array_equal(p, d[np.argpartition(d, (-3, -1))])
d = np.arange(17)
np.random.shuffle(d)
d.partition(range(d.size))
assert_array_equal(np.arange(17), d)
np.random.shuffle(d)
assert_array_equal(np.arange(17), d[d.argpartition(range(d.size))])
# test unsorted kth
d = np.arange(17)
np.random.shuffle(d)
keys = np.array([1, 3, 8, -2])
np.random.shuffle(d)
p = np.partition(d, keys)
self.assert_partitioned(p, keys)
p = d[np.argpartition(d, keys)]
self.assert_partitioned(p, keys)
np.random.shuffle(keys)
assert_array_equal(np.partition(d, keys), p)
assert_array_equal(d[np.argpartition(d, keys)], p)
# equal kth
d = np.arange(20)[::-1]
self.assert_partitioned(np.partition(d, [5]*4), [5])
self.assert_partitioned(np.partition(d, [5]*4 + [6, 13]),
[5]*4 + [6, 13])
self.assert_partitioned(d[np.argpartition(d, [5]*4)], [5])
self.assert_partitioned(d[np.argpartition(d, [5]*4 + [6, 13])],
[5]*4 + [6, 13])
d = np.arange(12)
np.random.shuffle(d)
d1 = np.tile(np.arange(12), (4, 1))
map(np.random.shuffle, d1)
d0 = np.transpose(d1)
kth = (1, 6, 7, -1)
p = np.partition(d1, kth, axis=1)
pa = d1[np.arange(d1.shape[0])[:, None],
d1.argpartition(kth, axis=1)]
assert_array_equal(p, pa)
for i in range(d1.shape[0]):
self.assert_partitioned(p[i,:], kth)
p = np.partition(d0, kth, axis=0)
pa = d0[np.argpartition(d0, kth, axis=0),
np.arange(d0.shape[1])[None,:]]
assert_array_equal(p, pa)
for i in range(d0.shape[1]):
self.assert_partitioned(p[:, i], kth)
def test_partition_cdtype(self):
d = array([('Galahad', 1.7, 38), ('Arthur', 1.8, 41),
('Lancelot', 1.9, 38)],
dtype=[('name', '|S10'), ('height', '<f8'), ('age', '<i4')])
tgt = np.sort(d, order=['age', 'height'])
assert_array_equal(np.partition(d, range(d.size),
order=['age', 'height']),
tgt)
assert_array_equal(d[np.argpartition(d, range(d.size),
order=['age', 'height'])],
tgt)
for k in range(d.size):
assert_equal(np.partition(d, k, order=['age', 'height'])[k],
tgt[k])
assert_equal(d[np.argpartition(d, k, order=['age', 'height'])][k],
tgt[k])
d = array(['Galahad', 'Arthur', 'zebra', 'Lancelot'])
tgt = np.sort(d)
assert_array_equal(np.partition(d, range(d.size)), tgt)
for k in range(d.size):
assert_equal(np.partition(d, k)[k], tgt[k])
assert_equal(d[np.argpartition(d, k)][k], tgt[k])
def test_partition_unicode_kind(self):
d = np.arange(10)
k = b'\xc3\xa4'.decode("UTF8")
assert_raises(ValueError, d.partition, 2, kind=k)
assert_raises(ValueError, d.argpartition, 2, kind=k)
def test_partition_fuzz(self):
# a few rounds of random data testing
for j in range(10, 30):
for i in range(1, j - 2):
d = np.arange(j)
np.random.shuffle(d)
d = d % np.random.randint(2, 30)
idx = np.random.randint(d.size)
kth = [0, idx, i, i + 1]
tgt = np.sort(d)[kth]
assert_array_equal(np.partition(d, kth)[kth], tgt,
err_msg="data: %r\n kth: %r" % (d, kth))
def test_argpartition_gh5524(self):
# A test for functionality of argpartition on lists.
d = [6,7,3,2,9,0]
p = np.argpartition(d,1)
self.assert_partitioned(np.array(d)[p],[1])
def test_flatten(self):
x0 = np.array([[1, 2, 3], [4, 5, 6]], np.int32)
x1 = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]]], np.int32)
y0 = np.array([1, 2, 3, 4, 5, 6], np.int32)
y0f = np.array([1, 4, 2, 5, 3, 6], np.int32)
y1 = np.array([1, 2, 3, 4, 5, 6, 7, 8], np.int32)
y1f = np.array([1, 5, 3, 7, 2, 6, 4, 8], np.int32)
assert_equal(x0.flatten(), y0)
assert_equal(x0.flatten('F'), y0f)
assert_equal(x0.flatten('F'), x0.T.flatten())
assert_equal(x1.flatten(), y1)
assert_equal(x1.flatten('F'), y1f)
assert_equal(x1.flatten('F'), x1.T.flatten())
def test_dot(self):
a = np.array([[1, 0], [0, 1]])
b = np.array([[0, 1], [1, 0]])
c = np.array([[9, 1], [1, -9]])
assert_equal(np.dot(a, b), a.dot(b))
assert_equal(np.dot(np.dot(a, b), c), a.dot(b).dot(c))
# test passing in an output array
c = np.zeros_like(a)
a.dot(b, c)
assert_equal(c, np.dot(a, b))
# test keyword args
c = np.zeros_like(a)
a.dot(b=b, out=c)
assert_equal(c, np.dot(a, b))
def test_dot_override(self):
class A(object):
def __numpy_ufunc__(self, ufunc, method, pos, inputs, **kwargs):
return "A"
class B(object):
def __numpy_ufunc__(self, ufunc, method, pos, inputs, **kwargs):
return NotImplemented
a = A()
b = B()
c = np.array([[1]])
assert_equal(np.dot(a, b), "A")
assert_equal(c.dot(a), "A")
assert_raises(TypeError, np.dot, b, c)
assert_raises(TypeError, c.dot, b)
def test_diagonal(self):
a = np.arange(12).reshape((3, 4))
assert_equal(a.diagonal(), [0, 5, 10])
assert_equal(a.diagonal(0), [0, 5, 10])
assert_equal(a.diagonal(1), [1, 6, 11])
assert_equal(a.diagonal(-1), [4, 9])
b = np.arange(8).reshape((2, 2, 2))
assert_equal(b.diagonal(), [[0, 6], [1, 7]])
assert_equal(b.diagonal(0), [[0, 6], [1, 7]])
assert_equal(b.diagonal(1), [[2], [3]])
assert_equal(b.diagonal(-1), [[4], [5]])
assert_raises(ValueError, b.diagonal, axis1=0, axis2=0)
assert_equal(b.diagonal(0, 1, 2), [[0, 3], [4, 7]])
assert_equal(b.diagonal(0, 0, 1), [[0, 6], [1, 7]])
assert_equal(b.diagonal(offset=1, axis1=0, axis2=2), [[1], [3]])
# Order of axis argument doesn't matter:
assert_equal(b.diagonal(0, 2, 1), [[0, 3], [4, 7]])
def test_diagonal_view_notwriteable(self):
# this test is only for 1.9, the diagonal view will be
# writeable in 1.10.
a = np.eye(3).diagonal()
assert_(not a.flags.writeable)
assert_(not a.flags.owndata)
a = np.diagonal(np.eye(3))
assert_(not a.flags.writeable)
assert_(not a.flags.owndata)
a = np.diag(np.eye(3))
assert_(not a.flags.writeable)
assert_(not a.flags.owndata)
def test_diagonal_memleak(self):
# Regression test for a bug that crept in at one point
a = np.zeros((100, 100))
assert_(sys.getrefcount(a) < 50)
for i in range(100):
a.diagonal()
assert_(sys.getrefcount(a) < 50)
def test_put(self):
icodes = np.typecodes['AllInteger']
fcodes = np.typecodes['AllFloat']
for dt in icodes + fcodes + 'O':
tgt = np.array([0, 1, 0, 3, 0, 5], dtype=dt)
# test 1-d
a = np.zeros(6, dtype=dt)
a.put([1, 3, 5], [1, 3, 5])
assert_equal(a, tgt)
# test 2-d
a = np.zeros((2, 3), dtype=dt)
a.put([1, 3, 5], [1, 3, 5])
assert_equal(a, tgt.reshape(2, 3))
for dt in '?':
tgt = np.array([False, True, False, True, False, True], dtype=dt)
# test 1-d
a = np.zeros(6, dtype=dt)
a.put([1, 3, 5], [True]*3)
assert_equal(a, tgt)
# test 2-d
a = np.zeros((2, 3), dtype=dt)
a.put([1, 3, 5], [True]*3)
assert_equal(a, tgt.reshape(2, 3))
# check must be writeable
a = np.zeros(6)
a.flags.writeable = False
assert_raises(ValueError, a.put, [1, 3, 5], [1, 3, 5])
def test_ravel(self):
a = np.array([[0, 1], [2, 3]])
assert_equal(a.ravel(), [0, 1, 2, 3])
assert_(not a.ravel().flags.owndata)
assert_equal(a.ravel('F'), [0, 2, 1, 3])
assert_equal(a.ravel(order='C'), [0, 1, 2, 3])
assert_equal(a.ravel(order='F'), [0, 2, 1, 3])
assert_equal(a.ravel(order='A'), [0, 1, 2, 3])
assert_(not a.ravel(order='A').flags.owndata)
assert_equal(a.ravel(order='K'), [0, 1, 2, 3])
assert_(not a.ravel(order='K').flags.owndata)
assert_equal(a.ravel(), a.reshape(-1))
a = np.array([[0, 1], [2, 3]], order='F')
assert_equal(a.ravel(), [0, 1, 2, 3])
assert_equal(a.ravel(order='A'), [0, 2, 1, 3])
assert_equal(a.ravel(order='K'), [0, 2, 1, 3])
assert_(not a.ravel(order='A').flags.owndata)
assert_(not a.ravel(order='K').flags.owndata)
assert_equal(a.ravel(), a.reshape(-1))
assert_equal(a.ravel(order='A'), a.reshape(-1, order='A'))
a = np.array([[0, 1], [2, 3]])[::-1, :]
assert_equal(a.ravel(), [2, 3, 0, 1])
assert_equal(a.ravel(order='C'), [2, 3, 0, 1])
assert_equal(a.ravel(order='F'), [2, 0, 3, 1])
assert_equal(a.ravel(order='A'), [2, 3, 0, 1])
# 'K' doesn't reverse the axes of negative strides
assert_equal(a.ravel(order='K'), [2, 3, 0, 1])
assert_(a.ravel(order='K').flags.owndata)
# Not contiguous and 1-sized axis with non matching stride
a = np.arange(2**3 * 2)[::2]
a = a.reshape(2, 1, 2, 2).swapaxes(-1, -2)
strides = list(a.strides)
strides[1] = 123
a.strides = strides
assert_(np.may_share_memory(a.ravel(order='K'), a))
assert_equal(a.ravel('K'), np.arange(0, 15, 2))
# General case of possible ravel that is not contiguous but
# works and includes a 1-sized axis with non matching stride
a = a.swapaxes(-1, -2) # swap back to C-order
assert_(np.may_share_memory(a.ravel(order='C'), a))
assert_(np.may_share_memory(a.ravel(order='K'), a))
a = a.T # swap all to Fortran order
assert_(np.may_share_memory(a.ravel(order='F'), a))
assert_(np.may_share_memory(a.ravel(order='K'), a))
# Test negative strides:
a = np.arange(4)[::-1].reshape(2, 2)
assert_(np.may_share_memory(a.ravel(order='C'), a))
assert_(np.may_share_memory(a.ravel(order='K'), a))
assert_equal(a.ravel('C'), [3, 2, 1, 0])
assert_equal(a.ravel('K'), [3, 2, 1, 0])
# Test keeporder with weirdly strided 1-sized dims (1-d first stride)
a = np.arange(8)[::2].reshape(1, 2, 2, 1) # neither C, nor F order
strides = list(a.strides)
strides[0] = -12
strides[-1] = 0
a.strides = strides
assert_(np.may_share_memory(a.ravel(order='K'), a))
assert_equal(a.ravel('K'), a.ravel('C'))
# 1-element tidy strides test (NPY_RELAXED_STRIDES_CHECKING):
a = np.array([[1]])
a.strides = (123, 432)
# If the stride is not 8, NPY_RELAXED_STRIDES_CHECKING is messing
# them up on purpose:
if np.ones(1).strides == (8,):
assert_(np.may_share_memory(a.ravel('K'), a))
assert_equal(a.ravel('K').strides, (a.dtype.itemsize,))
for order in ('C', 'F', 'A', 'K'):
# 0-d corner case:
a = np.array(0)
assert_equal(a.ravel(order), [0])
assert_(np.may_share_memory(a.ravel(order), a))
#Test that certain non-inplace ravels work right (mostly) for 'K':
b = np.arange(2**4 * 2)[::2].reshape(2, 2, 2, 2)
a = b[..., ::2]
assert_equal(a.ravel('K'), [0, 4, 8, 12, 16, 20, 24, 28])
assert_equal(a.ravel('C'), [0, 4, 8, 12, 16, 20, 24, 28])
assert_equal(a.ravel('A'), [0, 4, 8, 12, 16, 20, 24, 28])
assert_equal(a.ravel('F'), [0, 16, 8, 24, 4, 20, 12, 28])
a = b[::2, ...]
assert_equal(a.ravel('K'), [0, 2, 4, 6, 8, 10, 12, 14])
assert_equal(a.ravel('C'), [0, 2, 4, 6, 8, 10, 12, 14])
assert_equal(a.ravel('A'), [0, 2, 4, 6, 8, 10, 12, 14])
assert_equal(a.ravel('F'), [0, 8, 4, 12, 2, 10, 6, 14])
def test_swapaxes(self):
a = np.arange(1*2*3*4).reshape(1, 2, 3, 4).copy()
idx = np.indices(a.shape)
assert_(a.flags['OWNDATA'])
b = a.copy()
# check exceptions
assert_raises(ValueError, a.swapaxes, -5, 0)
assert_raises(ValueError, a.swapaxes, 4, 0)
assert_raises(ValueError, a.swapaxes, 0, -5)
assert_raises(ValueError, a.swapaxes, 0, 4)
for i in range(-4, 4):
for j in range(-4, 4):
for k, src in enumerate((a, b)):
c = src.swapaxes(i, j)
# check shape
shape = list(src.shape)
shape[i] = src.shape[j]
shape[j] = src.shape[i]
assert_equal(c.shape, shape, str((i, j, k)))
# check array contents
i0, i1, i2, i3 = [dim-1 for dim in c.shape]
j0, j1, j2, j3 = [dim-1 for dim in src.shape]
assert_equal(src[idx[j0], idx[j1], idx[j2], idx[j3]],
c[idx[i0], idx[i1], idx[i2], idx[i3]],
str((i, j, k)))
# check a view is always returned, gh-5260
assert_(not c.flags['OWNDATA'], str((i, j, k)))
# check on non-contiguous input array
if k == 1:
b = c
def test_conjugate(self):
a = np.array([1-1j, 1+1j, 23+23.0j])
ac = a.conj()
assert_equal(a.real, ac.real)
assert_equal(a.imag, -ac.imag)
assert_equal(ac, a.conjugate())
assert_equal(ac, np.conjugate(a))
a = np.array([1-1j, 1+1j, 23+23.0j], 'F')
ac = a.conj()
assert_equal(a.real, ac.real)
assert_equal(a.imag, -ac.imag)
assert_equal(ac, a.conjugate())
assert_equal(ac, np.conjugate(a))
a = np.array([1, 2, 3])
ac = a.conj()
assert_equal(a, ac)
assert_equal(ac, a.conjugate())
assert_equal(ac, np.conjugate(a))
a = np.array([1.0, 2.0, 3.0])
ac = a.conj()
assert_equal(a, ac)
assert_equal(ac, a.conjugate())
assert_equal(ac, np.conjugate(a))
a = np.array([1-1j, 1+1j, 1, 2.0], object)
ac = a.conj()
assert_equal(ac, [k.conjugate() for k in a])
assert_equal(ac, a.conjugate())
assert_equal(ac, np.conjugate(a))
a = np.array([1-1j, 1, 2.0, 'f'], object)
assert_raises(AttributeError, lambda: a.conj())
assert_raises(AttributeError, lambda: a.conjugate())
class TestBinop(object):
def test_inplace(self):
# test refcount 1 inplace conversion
assert_array_almost_equal(np.array([0.5]) * np.array([1.0, 2.0]),
[0.5, 1.0])
d = np.array([0.5, 0.5])[::2]
assert_array_almost_equal(d * (d * np.array([1.0, 2.0])),
[0.25, 0.5])
a = np.array([0.5])
b = np.array([0.5])
c = a + b
c = a - b
c = a * b
c = a / b
assert_equal(a, b)
assert_almost_equal(c, 1.)
c = a + b * 2. / b * a - a / b
assert_equal(a, b)
assert_equal(c, 0.5)
# true divide
a = np.array([5])
b = np.array([3])
c = (a * a) / b
assert_almost_equal(c, 25 / 3)
assert_equal(a, 5)
assert_equal(b, 3)
def test_extension_incref_elide(self):
# test extension (e.g. cython) calling PyNumber_* slots without
# increasing the reference counts
#
# def incref_elide(a):
# d = input.copy() # refcount 1
# return d, d + d # PyNumber_Add without increasing refcount
from numpy.core.multiarray_tests import incref_elide
d = np.ones(5)
orig, res = incref_elide(d)
# the return original should not be changed to an inplace operation
assert_array_equal(orig, d)
assert_array_equal(res, d + d)
def test_extension_incref_elide_stack(self):
# scanning if the refcount == 1 object is on the python stack to check
# that we are called directly from python is flawed as object may still
# be above the stack pointer and we have no access to the top of it
#
# def incref_elide_l(d):
# return l[4] + l[4] # PyNumber_Add without increasing refcount
from numpy.core.multiarray_tests import incref_elide_l
# padding with 1 makes sure the object on the stack is not overwriten
l = [1, 1, 1, 1, np.ones(5)]
res = incref_elide_l(l)
# the return original should not be changed to an inplace operation
assert_array_equal(l[4], np.ones(5))
assert_array_equal(res, l[4] + l[4])
def test_ufunc_override_rop_precedence(self):
# Check that __rmul__ and other right-hand operations have
# precedence over __numpy_ufunc__
ops = {
'__add__': ('__radd__', np.add, True),
'__sub__': ('__rsub__', np.subtract, True),
'__mul__': ('__rmul__', np.multiply, True),
'__truediv__': ('__rtruediv__', np.true_divide, True),
'__floordiv__': ('__rfloordiv__', np.floor_divide, True),
'__mod__': ('__rmod__', np.remainder, True),
'__divmod__': ('__rdivmod__', None, False),
'__pow__': ('__rpow__', np.power, True),
'__lshift__': ('__rlshift__', np.left_shift, True),
'__rshift__': ('__rrshift__', np.right_shift, True),
'__and__': ('__rand__', np.bitwise_and, True),
'__xor__': ('__rxor__', np.bitwise_xor, True),
'__or__': ('__ror__', np.bitwise_or, True),
'__ge__': ('__le__', np.less_equal, False),
'__gt__': ('__lt__', np.less, False),
'__le__': ('__ge__', np.greater_equal, False),
'__lt__': ('__gt__', np.greater, False),
'__eq__': ('__eq__', np.equal, False),
'__ne__': ('__ne__', np.not_equal, False),
}
class OtherNdarraySubclass(ndarray):
pass
class OtherNdarraySubclassWithOverride(ndarray):
def __numpy_ufunc__(self, *a, **kw):
raise AssertionError(("__numpy_ufunc__ %r %r shouldn't have "
"been called!") % (a, kw))
def check(op_name, ndsubclass):
rop_name, np_op, has_iop = ops[op_name]
if has_iop:
iop_name = '__i' + op_name[2:]
iop = getattr(operator, iop_name)
if op_name == "__divmod__":
op = divmod
else:
op = getattr(operator, op_name)
# Dummy class
def __init__(self, *a, **kw):
pass
def __numpy_ufunc__(self, *a, **kw):
raise AssertionError(("__numpy_ufunc__ %r %r shouldn't have "
"been called!") % (a, kw))
def __op__(self, *other):
return "op"
def __rop__(self, *other):
return "rop"
if ndsubclass:
bases = (ndarray,)
else:
bases = (object,)
dct = {'__init__': __init__,
'__numpy_ufunc__': __numpy_ufunc__,
op_name: __op__}
if op_name != rop_name:
dct[rop_name] = __rop__
cls = type("Rop" + rop_name, bases, dct)
# Check behavior against both bare ndarray objects and a
# ndarray subclasses with and without their own override
obj = cls((1,), buffer=np.ones(1,))
arr_objs = [np.array([1]),
np.array([2]).view(OtherNdarraySubclass),
np.array([3]).view(OtherNdarraySubclassWithOverride),
]
for arr in arr_objs:
err_msg = "%r %r" % (op_name, arr,)
# Check that ndarray op gives up if it sees a non-subclass
if not isinstance(obj, arr.__class__):
assert_equal(getattr(arr, op_name)(obj),
NotImplemented, err_msg=err_msg)
# Check that the Python binops have priority
assert_equal(op(obj, arr), "op", err_msg=err_msg)
if op_name == rop_name:
assert_equal(op(arr, obj), "op", err_msg=err_msg)
else:
assert_equal(op(arr, obj), "rop", err_msg=err_msg)
# Check that Python binops have priority also for in-place ops
if has_iop:
assert_equal(getattr(arr, iop_name)(obj),
NotImplemented, err_msg=err_msg)
if op_name != "__pow__":
# inplace pow requires the other object to be
# integer-like?
assert_equal(iop(arr, obj), "rop", err_msg=err_msg)
# Check that ufunc call __numpy_ufunc__ normally
if np_op is not None:
assert_raises(AssertionError, np_op, arr, obj,
err_msg=err_msg)
assert_raises(AssertionError, np_op, obj, arr,
err_msg=err_msg)
# Check all binary operations
for op_name in sorted(ops.keys()):
yield check, op_name, True
yield check, op_name, False
def test_ufunc_override_rop_simple(self):
# Check parts of the binary op overriding behavior in an
# explicit test case that is easier to understand.
class SomeClass(object):
def __numpy_ufunc__(self, *a, **kw):
return "ufunc"
def __mul__(self, other):
return 123
def __rmul__(self, other):
return 321
def __rsub__(self, other):
return "no subs for me"
def __gt__(self, other):
return "yep"
def __lt__(self, other):
return "nope"
class SomeClass2(SomeClass, ndarray):
def __numpy_ufunc__(self, ufunc, method, i, inputs, **kw):
if ufunc is np.multiply or ufunc is np.bitwise_and:
return "ufunc"
else:
inputs = list(inputs)
inputs[i] = np.asarray(self)
func = getattr(ufunc, method)
r = func(*inputs, **kw)
if 'out' in kw:
return r
else:
x = self.__class__(r.shape, dtype=r.dtype)
x[...] = r
return x
class SomeClass3(SomeClass2):
def __rsub__(self, other):
return "sub for me"
arr = np.array([0])
obj = SomeClass()
obj2 = SomeClass2((1,), dtype=np.int_)
obj2[0] = 9
obj3 = SomeClass3((1,), dtype=np.int_)
obj3[0] = 4
# obj is first, so should get to define outcome.
assert_equal(obj * arr, 123)
# obj is second, but has __numpy_ufunc__ and defines __rmul__.
assert_equal(arr * obj, 321)
# obj is second, but has __numpy_ufunc__ and defines __rsub__.
assert_equal(arr - obj, "no subs for me")
# obj is second, but has __numpy_ufunc__ and defines __lt__.
assert_equal(arr > obj, "nope")
# obj is second, but has __numpy_ufunc__ and defines __gt__.
assert_equal(arr < obj, "yep")
# Called as a ufunc, obj.__numpy_ufunc__ is used.
assert_equal(np.multiply(arr, obj), "ufunc")
# obj is second, but has __numpy_ufunc__ and defines __rmul__.
arr *= obj
assert_equal(arr, 321)
# obj2 is an ndarray subclass, so CPython takes care of the same rules.
assert_equal(obj2 * arr, 123)
assert_equal(arr * obj2, 321)
assert_equal(arr - obj2, "no subs for me")
assert_equal(arr > obj2, "nope")
assert_equal(arr < obj2, "yep")
# Called as a ufunc, obj2.__numpy_ufunc__ is called.
assert_equal(np.multiply(arr, obj2), "ufunc")
# Also when the method is not overridden.
assert_equal(arr & obj2, "ufunc")
arr *= obj2
assert_equal(arr, 321)
obj2 += 33
assert_equal(obj2[0], 42)
assert_equal(obj2.sum(), 42)
assert_(isinstance(obj2, SomeClass2))
# Obj3 is subclass that defines __rsub__. CPython calls it.
assert_equal(arr - obj3, "sub for me")
assert_equal(obj2 - obj3, "sub for me")
# obj3 is a subclass that defines __rmul__. CPython calls it.
assert_equal(arr * obj3, 321)
# But not here, since obj3.__rmul__ is obj2.__rmul__.
assert_equal(obj2 * obj3, 123)
# And of course, here obj3.__mul__ should be called.
assert_equal(obj3 * obj2, 123)
# obj3 defines __numpy_ufunc__ but obj3.__radd__ is obj2.__radd__.
# (and both are just ndarray.__radd__); see #4815.
res = obj2 + obj3
assert_equal(res, 46)
assert_(isinstance(res, SomeClass2))
# Since obj3 is a subclass, it should have precedence, like CPython
# would give, even though obj2 has __numpy_ufunc__ and __radd__.
# See gh-4815 and gh-5747.
res = obj3 + obj2
assert_equal(res, 46)
assert_(isinstance(res, SomeClass3))
def test_ufunc_override_normalize_signature(self):
# gh-5674
class SomeClass(object):
def __numpy_ufunc__(self, ufunc, method, i, inputs, **kw):
return kw
a = SomeClass()
kw = np.add(a, [1])
assert_('sig' not in kw and 'signature' not in kw)
kw = np.add(a, [1], sig='ii->i')
assert_('sig' not in kw and 'signature' in kw)
assert_equal(kw['signature'], 'ii->i')
kw = np.add(a, [1], signature='ii->i')
assert_('sig' not in kw and 'signature' in kw)
assert_equal(kw['signature'], 'ii->i')
class TestCAPI(TestCase):
def test_IsPythonScalar(self):
from numpy.core.multiarray_tests import IsPythonScalar
assert_(IsPythonScalar(b'foobar'))
assert_(IsPythonScalar(1))
assert_(IsPythonScalar(2**80))
assert_(IsPythonScalar(2.))
assert_(IsPythonScalar("a"))
class TestSubscripting(TestCase):
def test_test_zero_rank(self):
x = array([1, 2, 3])
self.assertTrue(isinstance(x[0], np.int_))
if sys.version_info[0] < 3:
self.assertTrue(isinstance(x[0], int))
self.assertTrue(type(x[0, ...]) is ndarray)
class TestPickling(TestCase):
def test_roundtrip(self):
import pickle
carray = array([[2, 9], [7, 0], [3, 8]])
DATA = [
carray,
transpose(carray),
array([('xxx', 1, 2.0)], dtype=[('a', (str, 3)), ('b', int),
('c', float)])
]
for a in DATA:
assert_equal(a, pickle.loads(a.dumps()), err_msg="%r" % a)
def _loads(self, obj):
if sys.version_info[0] >= 3:
return loads(obj, encoding='latin1')
else:
return loads(obj)
# version 0 pickles, using protocol=2 to pickle
# version 0 doesn't have a version field
def test_version0_int8(self):
s = '\x80\x02cnumpy.core._internal\n_reconstruct\nq\x01cnumpy\nndarray\nq\x02K\x00\x85U\x01b\x87Rq\x03(K\x04\x85cnumpy\ndtype\nq\x04U\x02i1K\x00K\x01\x87Rq\x05(U\x01|NNJ\xff\xff\xff\xffJ\xff\xff\xff\xfftb\x89U\x04\x01\x02\x03\x04tb.'
a = array([1, 2, 3, 4], dtype=int8)
p = self._loads(asbytes(s))
assert_equal(a, p)
def test_version0_float32(self):
s = '\x80\x02cnumpy.core._internal\n_reconstruct\nq\x01cnumpy\nndarray\nq\x02K\x00\x85U\x01b\x87Rq\x03(K\x04\x85cnumpy\ndtype\nq\x04U\x02f4K\x00K\x01\x87Rq\x05(U\x01<NNJ\xff\xff\xff\xffJ\xff\xff\xff\xfftb\x89U\x10\x00\x00\x80?\x00\x00\x00@\x00\x00@@\x00\x00\x80@tb.'
a = array([1.0, 2.0, 3.0, 4.0], dtype=float32)
p = self._loads(asbytes(s))
assert_equal(a, p)
def test_version0_object(self):
s = '\x80\x02cnumpy.core._internal\n_reconstruct\nq\x01cnumpy\nndarray\nq\x02K\x00\x85U\x01b\x87Rq\x03(K\x02\x85cnumpy\ndtype\nq\x04U\x02O8K\x00K\x01\x87Rq\x05(U\x01|NNJ\xff\xff\xff\xffJ\xff\xff\xff\xfftb\x89]q\x06(}q\x07U\x01aK\x01s}q\x08U\x01bK\x02setb.'
a = np.array([{'a':1}, {'b':2}])
p = self._loads(asbytes(s))
assert_equal(a, p)
# version 1 pickles, using protocol=2 to pickle
def test_version1_int8(self):
s = '\x80\x02cnumpy.core._internal\n_reconstruct\nq\x01cnumpy\nndarray\nq\x02K\x00\x85U\x01b\x87Rq\x03(K\x01K\x04\x85cnumpy\ndtype\nq\x04U\x02i1K\x00K\x01\x87Rq\x05(K\x01U\x01|NNJ\xff\xff\xff\xffJ\xff\xff\xff\xfftb\x89U\x04\x01\x02\x03\x04tb.'
a = array([1, 2, 3, 4], dtype=int8)
p = self._loads(asbytes(s))
assert_equal(a, p)
def test_version1_float32(self):
s = '\x80\x02cnumpy.core._internal\n_reconstruct\nq\x01cnumpy\nndarray\nq\x02K\x00\x85U\x01b\x87Rq\x03(K\x01K\x04\x85cnumpy\ndtype\nq\x04U\x02f4K\x00K\x01\x87Rq\x05(K\x01U\x01<NNJ\xff\xff\xff\xffJ\xff\xff\xff\xfftb\x89U\x10\x00\x00\x80?\x00\x00\x00@\x00\x00@@\x00\x00\x80@tb.'
a = array([1.0, 2.0, 3.0, 4.0], dtype=float32)
p = self._loads(asbytes(s))
assert_equal(a, p)
def test_version1_object(self):
s = '\x80\x02cnumpy.core._internal\n_reconstruct\nq\x01cnumpy\nndarray\nq\x02K\x00\x85U\x01b\x87Rq\x03(K\x01K\x02\x85cnumpy\ndtype\nq\x04U\x02O8K\x00K\x01\x87Rq\x05(K\x01U\x01|NNJ\xff\xff\xff\xffJ\xff\xff\xff\xfftb\x89]q\x06(}q\x07U\x01aK\x01s}q\x08U\x01bK\x02setb.'
a = array([{'a':1}, {'b':2}])
p = self._loads(asbytes(s))
assert_equal(a, p)
def test_subarray_int_shape(self):
s = "cnumpy.core.multiarray\n_reconstruct\np0\n(cnumpy\nndarray\np1\n(I0\ntp2\nS'b'\np3\ntp4\nRp5\n(I1\n(I1\ntp6\ncnumpy\ndtype\np7\n(S'V6'\np8\nI0\nI1\ntp9\nRp10\n(I3\nS'|'\np11\nN(S'a'\np12\ng3\ntp13\n(dp14\ng12\n(g7\n(S'V4'\np15\nI0\nI1\ntp16\nRp17\n(I3\nS'|'\np18\n(g7\n(S'i1'\np19\nI0\nI1\ntp20\nRp21\n(I3\nS'|'\np22\nNNNI-1\nI-1\nI0\ntp23\nb(I2\nI2\ntp24\ntp25\nNNI4\nI1\nI0\ntp26\nbI0\ntp27\nsg3\n(g7\n(S'V2'\np28\nI0\nI1\ntp29\nRp30\n(I3\nS'|'\np31\n(g21\nI2\ntp32\nNNI2\nI1\nI0\ntp33\nbI4\ntp34\nsI6\nI1\nI0\ntp35\nbI00\nS'\\x01\\x01\\x01\\x01\\x01\\x02'\np36\ntp37\nb."
a = np.array([(1, (1, 2))], dtype=[('a', 'i1', (2, 2)), ('b', 'i1', 2)])
p = self._loads(asbytes(s))
assert_equal(a, p)
class TestFancyIndexing(TestCase):
def test_list(self):
x = ones((1, 1))
x[:, [0]] = 2.0
assert_array_equal(x, array([[2.0]]))
x = ones((1, 1, 1))
x[:,:, [0]] = 2.0
assert_array_equal(x, array([[[2.0]]]))
def test_tuple(self):
x = ones((1, 1))
x[:, (0,)] = 2.0
assert_array_equal(x, array([[2.0]]))
x = ones((1, 1, 1))
x[:,:, (0,)] = 2.0
assert_array_equal(x, array([[[2.0]]]))
def test_mask(self):
x = array([1, 2, 3, 4])
m = array([0, 1, 0, 0], bool)
assert_array_equal(x[m], array([2]))
def test_mask2(self):
x = array([[1, 2, 3, 4], [5, 6, 7, 8]])
m = array([0, 1], bool)
m2 = array([[0, 1, 0, 0], [1, 0, 0, 0]], bool)
m3 = array([[0, 1, 0, 0], [0, 0, 0, 0]], bool)
assert_array_equal(x[m], array([[5, 6, 7, 8]]))
assert_array_equal(x[m2], array([2, 5]))
assert_array_equal(x[m3], array([2]))
def test_assign_mask(self):
x = array([1, 2, 3, 4])
m = array([0, 1, 0, 0], bool)
x[m] = 5
assert_array_equal(x, array([1, 5, 3, 4]))
def test_assign_mask2(self):
xorig = array([[1, 2, 3, 4], [5, 6, 7, 8]])
m = array([0, 1], bool)
m2 = array([[0, 1, 0, 0], [1, 0, 0, 0]], bool)
m3 = array([[0, 1, 0, 0], [0, 0, 0, 0]], bool)
x = xorig.copy()
x[m] = 10
assert_array_equal(x, array([[1, 2, 3, 4], [10, 10, 10, 10]]))
x = xorig.copy()
x[m2] = 10
assert_array_equal(x, array([[1, 10, 3, 4], [10, 6, 7, 8]]))
x = xorig.copy()
x[m3] = 10
assert_array_equal(x, array([[1, 10, 3, 4], [5, 6, 7, 8]]))
class TestStringCompare(TestCase):
def test_string(self):
g1 = array(["This", "is", "example"])
g2 = array(["This", "was", "example"])
assert_array_equal(g1 == g2, [g1[i] == g2[i] for i in [0, 1, 2]])
assert_array_equal(g1 != g2, [g1[i] != g2[i] for i in [0, 1, 2]])
assert_array_equal(g1 <= g2, [g1[i] <= g2[i] for i in [0, 1, 2]])
assert_array_equal(g1 >= g2, [g1[i] >= g2[i] for i in [0, 1, 2]])
assert_array_equal(g1 < g2, [g1[i] < g2[i] for i in [0, 1, 2]])
assert_array_equal(g1 > g2, [g1[i] > g2[i] for i in [0, 1, 2]])
def test_mixed(self):
g1 = array(["spam", "spa", "spammer", "and eggs"])
g2 = "spam"
assert_array_equal(g1 == g2, [x == g2 for x in g1])
assert_array_equal(g1 != g2, [x != g2 for x in g1])
assert_array_equal(g1 < g2, [x < g2 for x in g1])
assert_array_equal(g1 > g2, [x > g2 for x in g1])
assert_array_equal(g1 <= g2, [x <= g2 for x in g1])
assert_array_equal(g1 >= g2, [x >= g2 for x in g1])
def test_unicode(self):
g1 = array([sixu("This"), sixu("is"), sixu("example")])
g2 = array([sixu("This"), sixu("was"), sixu("example")])
assert_array_equal(g1 == g2, [g1[i] == g2[i] for i in [0, 1, 2]])
assert_array_equal(g1 != g2, [g1[i] != g2[i] for i in [0, 1, 2]])
assert_array_equal(g1 <= g2, [g1[i] <= g2[i] for i in [0, 1, 2]])
assert_array_equal(g1 >= g2, [g1[i] >= g2[i] for i in [0, 1, 2]])
assert_array_equal(g1 < g2, [g1[i] < g2[i] for i in [0, 1, 2]])
assert_array_equal(g1 > g2, [g1[i] > g2[i] for i in [0, 1, 2]])
class TestArgmax(TestCase):
nan_arr = [
([0, 1, 2, 3, np.nan], 4),
([0, 1, 2, np.nan, 3], 3),
([np.nan, 0, 1, 2, 3], 0),
([np.nan, 0, np.nan, 2, 3], 0),
([0, 1, 2, 3, complex(0, np.nan)], 4),
([0, 1, 2, 3, complex(np.nan, 0)], 4),
([0, 1, 2, complex(np.nan, 0), 3], 3),
([0, 1, 2, complex(0, np.nan), 3], 3),
([complex(0, np.nan), 0, 1, 2, 3], 0),
([complex(np.nan, np.nan), 0, 1, 2, 3], 0),
([complex(np.nan, 0), complex(np.nan, 2), complex(np.nan, 1)], 0),
([complex(np.nan, np.nan), complex(np.nan, 2), complex(np.nan, 1)], 0),
([complex(np.nan, 0), complex(np.nan, 2), complex(np.nan, np.nan)], 0),
([complex(0, 0), complex(0, 2), complex(0, 1)], 1),
([complex(1, 0), complex(0, 2), complex(0, 1)], 0),
([complex(1, 0), complex(0, 2), complex(1, 1)], 2),
([np.datetime64('1923-04-14T12:43:12'),
np.datetime64('1994-06-21T14:43:15'),
np.datetime64('2001-10-15T04:10:32'),
np.datetime64('1995-11-25T16:02:16'),
np.datetime64('2005-01-04T03:14:12'),
np.datetime64('2041-12-03T14:05:03')], 5),
([np.datetime64('1935-09-14T04:40:11'),
np.datetime64('1949-10-12T12:32:11'),
np.datetime64('2010-01-03T05:14:12'),
np.datetime64('2015-11-20T12:20:59'),
np.datetime64('1932-09-23T10:10:13'),
np.datetime64('2014-10-10T03:50:30')], 3),
# Assorted tests with NaTs
([np.datetime64('NaT'),
np.datetime64('NaT'),
np.datetime64('2010-01-03T05:14:12'),
np.datetime64('NaT'),
np.datetime64('2015-09-23T10:10:13'),
np.datetime64('1932-10-10T03:50:30')], 4),
([np.datetime64('2059-03-14T12:43:12'),
np.datetime64('1996-09-21T14:43:15'),
np.datetime64('NaT'),
np.datetime64('2022-12-25T16:02:16'),
np.datetime64('1963-10-04T03:14:12'),
np.datetime64('2013-05-08T18:15:23')], 0),
([np.timedelta64(2, 's'),
np.timedelta64(1, 's'),
np.timedelta64('NaT', 's'),
np.timedelta64(3, 's')], 3),
([np.timedelta64('NaT', 's')] * 3, 0),
([timedelta(days=5, seconds=14), timedelta(days=2, seconds=35),
timedelta(days=-1, seconds=23)], 0),
([timedelta(days=1, seconds=43), timedelta(days=10, seconds=5),
timedelta(days=5, seconds=14)], 1),
([timedelta(days=10, seconds=24), timedelta(days=10, seconds=5),
timedelta(days=10, seconds=43)], 2),
([False, False, False, False, True], 4),
([False, False, False, True, False], 3),
([True, False, False, False, False], 0),
([True, False, True, False, False], 0),
# Can't reduce a "flexible type"
#(['a', 'z', 'aa', 'zz'], 3),
#(['zz', 'a', 'aa', 'a'], 0),
#(['aa', 'z', 'zz', 'a'], 2),
]
def test_all(self):
a = np.random.normal(0, 1, (4, 5, 6, 7, 8))
for i in range(a.ndim):
amax = a.max(i)
aargmax = a.argmax(i)
axes = list(range(a.ndim))
axes.remove(i)
assert_(all(amax == aargmax.choose(*a.transpose(i,*axes))))
def test_combinations(self):
for arr, pos in self.nan_arr:
assert_equal(np.argmax(arr), pos, err_msg="%r"%arr)
assert_equal(arr[np.argmax(arr)], np.max(arr), err_msg="%r"%arr)
def test_output_shape(self):
# see also gh-616
a = np.ones((10, 5))
# Check some simple shape mismatches
out = np.ones(11, dtype=np.int_)
assert_raises(ValueError, a.argmax, -1, out)
out = np.ones((2, 5), dtype=np.int_)
assert_raises(ValueError, a.argmax, -1, out)
# these could be relaxed possibly (used to allow even the previous)
out = np.ones((1, 10), dtype=np.int_)
assert_raises(ValueError, a.argmax, -1, np.ones((1, 10)))
out = np.ones(10, dtype=np.int_)
a.argmax(-1, out=out)
assert_equal(out, a.argmax(-1))
def test_argmax_unicode(self):
d = np.zeros(6031, dtype='<U9')
d[5942] = "as"
assert_equal(d.argmax(), 5942)
def test_np_vs_ndarray(self):
# make sure both ndarray.argmax and numpy.argmax support out/axis args
a = np.random.normal(size=(2,3))
#check positional args
out1 = zeros(2, dtype=int)
out2 = zeros(2, dtype=int)
assert_equal(a.argmax(1, out1), np.argmax(a, 1, out2))
assert_equal(out1, out2)
#check keyword args
out1 = zeros(3, dtype=int)
out2 = zeros(3, dtype=int)
assert_equal(a.argmax(out=out1, axis=0), np.argmax(a, out=out2, axis=0))
assert_equal(out1, out2)
class TestArgmin(TestCase):
nan_arr = [
([0, 1, 2, 3, np.nan], 4),
([0, 1, 2, np.nan, 3], 3),
([np.nan, 0, 1, 2, 3], 0),
([np.nan, 0, np.nan, 2, 3], 0),
([0, 1, 2, 3, complex(0, np.nan)], 4),
([0, 1, 2, 3, complex(np.nan, 0)], 4),
([0, 1, 2, complex(np.nan, 0), 3], 3),
([0, 1, 2, complex(0, np.nan), 3], 3),
([complex(0, np.nan), 0, 1, 2, 3], 0),
([complex(np.nan, np.nan), 0, 1, 2, 3], 0),
([complex(np.nan, 0), complex(np.nan, 2), complex(np.nan, 1)], 0),
([complex(np.nan, np.nan), complex(np.nan, 2), complex(np.nan, 1)], 0),
([complex(np.nan, 0), complex(np.nan, 2), complex(np.nan, np.nan)], 0),
([complex(0, 0), complex(0, 2), complex(0, 1)], 0),
([complex(1, 0), complex(0, 2), complex(0, 1)], 2),
([complex(1, 0), complex(0, 2), complex(1, 1)], 1),
([np.datetime64('1923-04-14T12:43:12'),
np.datetime64('1994-06-21T14:43:15'),
np.datetime64('2001-10-15T04:10:32'),
np.datetime64('1995-11-25T16:02:16'),
np.datetime64('2005-01-04T03:14:12'),
np.datetime64('2041-12-03T14:05:03')], 0),
([np.datetime64('1935-09-14T04:40:11'),
np.datetime64('1949-10-12T12:32:11'),
np.datetime64('2010-01-03T05:14:12'),
np.datetime64('2014-11-20T12:20:59'),
np.datetime64('2015-09-23T10:10:13'),
np.datetime64('1932-10-10T03:50:30')], 5),
# Assorted tests with NaTs
([np.datetime64('NaT'),
np.datetime64('NaT'),
np.datetime64('2010-01-03T05:14:12'),
np.datetime64('NaT'),
np.datetime64('2015-09-23T10:10:13'),
np.datetime64('1932-10-10T03:50:30')], 5),
([np.datetime64('2059-03-14T12:43:12'),
np.datetime64('1996-09-21T14:43:15'),
np.datetime64('NaT'),
np.datetime64('2022-12-25T16:02:16'),
np.datetime64('1963-10-04T03:14:12'),
np.datetime64('2013-05-08T18:15:23')], 4),
([np.timedelta64(2, 's'),
np.timedelta64(1, 's'),
np.timedelta64('NaT', 's'),
np.timedelta64(3, 's')], 1),
([np.timedelta64('NaT', 's')] * 3, 0),
([timedelta(days=5, seconds=14), timedelta(days=2, seconds=35),
timedelta(days=-1, seconds=23)], 2),
([timedelta(days=1, seconds=43), timedelta(days=10, seconds=5),
timedelta(days=5, seconds=14)], 0),
([timedelta(days=10, seconds=24), timedelta(days=10, seconds=5),
timedelta(days=10, seconds=43)], 1),
([True, True, True, True, False], 4),
([True, True, True, False, True], 3),
([False, True, True, True, True], 0),
([False, True, False, True, True], 0),
# Can't reduce a "flexible type"
#(['a', 'z', 'aa', 'zz'], 0),
#(['zz', 'a', 'aa', 'a'], 1),
#(['aa', 'z', 'zz', 'a'], 3),
]
def test_all(self):
a = np.random.normal(0, 1, (4, 5, 6, 7, 8))
for i in range(a.ndim):
amin = a.min(i)
aargmin = a.argmin(i)
axes = list(range(a.ndim))
axes.remove(i)
assert_(all(amin == aargmin.choose(*a.transpose(i,*axes))))
def test_combinations(self):
for arr, pos in self.nan_arr:
assert_equal(np.argmin(arr), pos, err_msg="%r"%arr)
assert_equal(arr[np.argmin(arr)], np.min(arr), err_msg="%r"%arr)
def test_minimum_signed_integers(self):
a = np.array([1, -2**7, -2**7 + 1], dtype=np.int8)
assert_equal(np.argmin(a), 1)
a = np.array([1, -2**15, -2**15 + 1], dtype=np.int16)
assert_equal(np.argmin(a), 1)
a = np.array([1, -2**31, -2**31 + 1], dtype=np.int32)
assert_equal(np.argmin(a), 1)
a = np.array([1, -2**63, -2**63 + 1], dtype=np.int64)
assert_equal(np.argmin(a), 1)
def test_output_shape(self):
# see also gh-616
a = np.ones((10, 5))
# Check some simple shape mismatches
out = np.ones(11, dtype=np.int_)
assert_raises(ValueError, a.argmin, -1, out)
out = np.ones((2, 5), dtype=np.int_)
assert_raises(ValueError, a.argmin, -1, out)
# these could be relaxed possibly (used to allow even the previous)
out = np.ones((1, 10), dtype=np.int_)
assert_raises(ValueError, a.argmin, -1, np.ones((1, 10)))
out = np.ones(10, dtype=np.int_)
a.argmin(-1, out=out)
assert_equal(out, a.argmin(-1))
def test_argmin_unicode(self):
d = np.ones(6031, dtype='<U9')
d[6001] = "0"
assert_equal(d.argmin(), 6001)
def test_np_vs_ndarray(self):
# make sure both ndarray.argmin and numpy.argmin support out/axis args
a = np.random.normal(size=(2,3))
#check positional args
out1 = zeros(2, dtype=int)
out2 = ones(2, dtype=int)
assert_equal(a.argmin(1, out1), np.argmin(a, 1, out2))
assert_equal(out1, out2)
#check keyword args
out1 = zeros(3, dtype=int)
out2 = ones(3, dtype=int)
assert_equal(a.argmin(out=out1, axis=0), np.argmin(a, out=out2, axis=0))
assert_equal(out1, out2)
class TestMinMax(TestCase):
def test_scalar(self):
assert_raises(ValueError, np.amax, 1, 1)
assert_raises(ValueError, np.amin, 1, 1)
assert_equal(np.amax(1, axis=0), 1)
assert_equal(np.amin(1, axis=0), 1)
assert_equal(np.amax(1, axis=None), 1)
assert_equal(np.amin(1, axis=None), 1)
def test_axis(self):
assert_raises(ValueError, np.amax, [1, 2, 3], 1000)
assert_equal(np.amax([[1, 2, 3]], axis=1), 3)
def test_datetime(self):
# NaTs are ignored
for dtype in ('m8[s]', 'm8[Y]'):
a = np.arange(10).astype(dtype)
a[3] = 'NaT'
assert_equal(np.amin(a), a[0])
assert_equal(np.amax(a), a[9])
a[0] = 'NaT'
assert_equal(np.amin(a), a[1])
assert_equal(np.amax(a), a[9])
a.fill('NaT')
assert_equal(np.amin(a), a[0])
assert_equal(np.amax(a), a[0])
class TestNewaxis(TestCase):
def test_basic(self):
sk = array([0, -0.1, 0.1])
res = 250*sk[:, newaxis]
assert_almost_equal(res.ravel(), 250*sk)
class TestClip(TestCase):
def _check_range(self, x, cmin, cmax):
assert_(np.all(x >= cmin))
assert_(np.all(x <= cmax))
def _clip_type(self,type_group,array_max,
clip_min,clip_max,inplace=False,
expected_min=None,expected_max=None):
if expected_min is None:
expected_min = clip_min
if expected_max is None:
expected_max = clip_max
for T in np.sctypes[type_group]:
if sys.byteorder == 'little':
byte_orders = ['=', '>']
else:
byte_orders = ['<', '=']
for byteorder in byte_orders:
dtype = np.dtype(T).newbyteorder(byteorder)
x = (np.random.random(1000) * array_max).astype(dtype)
if inplace:
x.clip(clip_min, clip_max, x)
else:
x = x.clip(clip_min, clip_max)
byteorder = '='
if x.dtype.byteorder == '|': byteorder = '|'
assert_equal(x.dtype.byteorder, byteorder)
self._check_range(x, expected_min, expected_max)
return x
def test_basic(self):
for inplace in [False, True]:
self._clip_type('float', 1024, -12.8, 100.2, inplace=inplace)
self._clip_type('float', 1024, 0, 0, inplace=inplace)
self._clip_type('int', 1024, -120, 100.5, inplace=inplace)
self._clip_type('int', 1024, 0, 0, inplace=inplace)
x = self._clip_type('uint', 1024, -120, 100, expected_min=0,
inplace=inplace)
x = self._clip_type('uint', 1024, 0, 0, inplace=inplace)
def test_record_array(self):
rec = np.array([(-5, 2.0, 3.0), (5.0, 4.0, 3.0)],
dtype=[('x', '<f8'), ('y', '<f8'), ('z', '<f8')])
y = rec['x'].clip(-0.3, 0.5)
self._check_range(y, -0.3, 0.5)
def test_max_or_min(self):
val = np.array([0, 1, 2, 3, 4, 5, 6, 7])
x = val.clip(3)
assert_(np.all(x >= 3))
x = val.clip(min=3)
assert_(np.all(x >= 3))
x = val.clip(max=4)
assert_(np.all(x <= 4))
class TestPutmask(object):
def tst_basic(self, x, T, mask, val):
np.putmask(x, mask, val)
assert_(np.all(x[mask] == T(val)))
assert_(x.dtype == T)
def test_ip_types(self):
unchecked_types = [str, unicode, np.void, object]
x = np.random.random(1000)*100
mask = x < 40
for val in [-100, 0, 15]:
for types in np.sctypes.values():
for T in types:
if T not in unchecked_types:
yield self.tst_basic, x.copy().astype(T), T, mask, val
def test_mask_size(self):
assert_raises(ValueError, np.putmask, np.array([1, 2, 3]), [True], 5)
def tst_byteorder(self, dtype):
x = np.array([1, 2, 3], dtype)
np.putmask(x, [True, False, True], -1)
assert_array_equal(x, [-1, 2, -1])
def test_ip_byteorder(self):
for dtype in ('>i4', '<i4'):
yield self.tst_byteorder, dtype
def test_record_array(self):
# Note mixed byteorder.
rec = np.array([(-5, 2.0, 3.0), (5.0, 4.0, 3.0)],
dtype=[('x', '<f8'), ('y', '>f8'), ('z', '<f8')])
np.putmask(rec['x'], [True, False], 10)
assert_array_equal(rec['x'], [10, 5])
assert_array_equal(rec['y'], [2, 4])
assert_array_equal(rec['z'], [3, 3])
np.putmask(rec['y'], [True, False], 11)
assert_array_equal(rec['x'], [10, 5])
assert_array_equal(rec['y'], [11, 4])
assert_array_equal(rec['z'], [3, 3])
def test_masked_array(self):
## x = np.array([1,2,3])
## z = np.ma.array(x,mask=[True,False,False])
## np.putmask(z,[True,True,True],3)
pass
class TestTake(object):
def tst_basic(self, x):
ind = list(range(x.shape[0]))
assert_array_equal(x.take(ind, axis=0), x)
def test_ip_types(self):
unchecked_types = [str, unicode, np.void, object]
x = np.random.random(24)*100
x.shape = 2, 3, 4
for types in np.sctypes.values():
for T in types:
if T not in unchecked_types:
yield self.tst_basic, x.copy().astype(T)
def test_raise(self):
x = np.random.random(24)*100
x.shape = 2, 3, 4
assert_raises(IndexError, x.take, [0, 1, 2], axis=0)
assert_raises(IndexError, x.take, [-3], axis=0)
assert_array_equal(x.take([-1], axis=0)[0], x[1])
def test_clip(self):
x = np.random.random(24)*100
x.shape = 2, 3, 4
assert_array_equal(x.take([-1], axis=0, mode='clip')[0], x[0])
assert_array_equal(x.take([2], axis=0, mode='clip')[0], x[1])
def test_wrap(self):
x = np.random.random(24)*100
x.shape = 2, 3, 4
assert_array_equal(x.take([-1], axis=0, mode='wrap')[0], x[1])
assert_array_equal(x.take([2], axis=0, mode='wrap')[0], x[0])
assert_array_equal(x.take([3], axis=0, mode='wrap')[0], x[1])
def tst_byteorder(self, dtype):
x = np.array([1, 2, 3], dtype)
assert_array_equal(x.take([0, 2, 1]), [1, 3, 2])
def test_ip_byteorder(self):
for dtype in ('>i4', '<i4'):
yield self.tst_byteorder, dtype
def test_record_array(self):
# Note mixed byteorder.
rec = np.array([(-5, 2.0, 3.0), (5.0, 4.0, 3.0)],
dtype=[('x', '<f8'), ('y', '>f8'), ('z', '<f8')])
rec1 = rec.take([1])
assert_(rec1['x'] == 5.0 and rec1['y'] == 4.0)
class TestLexsort(TestCase):
def test_basic(self):
a = [1, 2, 1, 3, 1, 5]
b = [0, 4, 5, 6, 2, 3]
idx = np.lexsort((b, a))
expected_idx = np.array([0, 4, 2, 1, 3, 5])
assert_array_equal(idx, expected_idx)
x = np.vstack((b, a))
idx = np.lexsort(x)
assert_array_equal(idx, expected_idx)
assert_array_equal(x[1][idx], np.sort(x[1]))
def test_datetime(self):
a = np.array([0,0,0], dtype='datetime64[D]')
b = np.array([2,1,0], dtype='datetime64[D]')
idx = np.lexsort((b, a))
expected_idx = np.array([2, 1, 0])
assert_array_equal(idx, expected_idx)
a = np.array([0,0,0], dtype='timedelta64[D]')
b = np.array([2,1,0], dtype='timedelta64[D]')
idx = np.lexsort((b, a))
expected_idx = np.array([2, 1, 0])
assert_array_equal(idx, expected_idx)
class TestIO(object):
"""Test tofile, fromfile, tobytes, and fromstring"""
def setUp(self):
shape = (2, 4, 3)
rand = np.random.random
self.x = rand(shape) + rand(shape).astype(np.complex)*1j
self.x[0,:, 1] = [nan, inf, -inf, nan]
self.dtype = self.x.dtype
self.tempdir = tempfile.mkdtemp()
self.filename = tempfile.mktemp(dir=self.tempdir)
def tearDown(self):
shutil.rmtree(self.tempdir)
def test_bool_fromstring(self):
v = np.array([True, False, True, False], dtype=np.bool_)
y = np.fromstring('1 0 -2.3 0.0', sep=' ', dtype=np.bool_)
assert_array_equal(v, y)
def test_uint64_fromstring(self):
d = np.fromstring("9923372036854775807 104783749223640",
dtype=np.uint64, sep=' ');
e = np.array([9923372036854775807, 104783749223640], dtype=np.uint64)
assert_array_equal(d, e)
def test_int64_fromstring(self):
d = np.fromstring("-25041670086757 104783749223640",
dtype=np.int64, sep=' ');
e = np.array([-25041670086757, 104783749223640], dtype=np.int64)
assert_array_equal(d, e)
def test_empty_files_binary(self):
f = open(self.filename, 'w')
f.close()
y = fromfile(self.filename)
assert_(y.size == 0, "Array not empty")
def test_empty_files_text(self):
f = open(self.filename, 'w')
f.close()
y = fromfile(self.filename, sep=" ")
assert_(y.size == 0, "Array not empty")
def test_roundtrip_file(self):
f = open(self.filename, 'wb')
self.x.tofile(f)
f.close()
# NB. doesn't work with flush+seek, due to use of C stdio
f = open(self.filename, 'rb')
y = np.fromfile(f, dtype=self.dtype)
f.close()
assert_array_equal(y, self.x.flat)
def test_roundtrip_filename(self):
self.x.tofile(self.filename)
y = np.fromfile(self.filename, dtype=self.dtype)
assert_array_equal(y, self.x.flat)
def test_roundtrip_binary_str(self):
s = self.x.tobytes()
y = np.fromstring(s, dtype=self.dtype)
assert_array_equal(y, self.x.flat)
s = self.x.tobytes('F')
y = np.fromstring(s, dtype=self.dtype)
assert_array_equal(y, self.x.flatten('F'))
def test_roundtrip_str(self):
x = self.x.real.ravel()
s = "@".join(map(str, x))
y = np.fromstring(s, sep="@")
# NB. str imbues less precision
nan_mask = ~np.isfinite(x)
assert_array_equal(x[nan_mask], y[nan_mask])
assert_array_almost_equal(x[~nan_mask], y[~nan_mask], decimal=5)
def test_roundtrip_repr(self):
x = self.x.real.ravel()
s = "@".join(map(repr, x))
y = np.fromstring(s, sep="@")
assert_array_equal(x, y)
def test_file_position_after_fromfile(self):
# gh-4118
sizes = [io.DEFAULT_BUFFER_SIZE//8,
io.DEFAULT_BUFFER_SIZE,
io.DEFAULT_BUFFER_SIZE*8]
for size in sizes:
f = open(self.filename, 'wb')
f.seek(size-1)
f.write(b'\0')
f.close()
for mode in ['rb', 'r+b']:
err_msg = "%d %s" % (size, mode)
f = open(self.filename, mode)
f.read(2)
np.fromfile(f, dtype=np.float64, count=1)
pos = f.tell()
f.close()
assert_equal(pos, 10, err_msg=err_msg)
def test_file_position_after_tofile(self):
# gh-4118
sizes = [io.DEFAULT_BUFFER_SIZE//8,
io.DEFAULT_BUFFER_SIZE,
io.DEFAULT_BUFFER_SIZE*8]
for size in sizes:
err_msg = "%d" % (size,)
f = open(self.filename, 'wb')
f.seek(size-1)
f.write(b'\0')
f.seek(10)
f.write(b'12')
np.array([0], dtype=np.float64).tofile(f)
pos = f.tell()
f.close()
assert_equal(pos, 10 + 2 + 8, err_msg=err_msg)
f = open(self.filename, 'r+b')
f.read(2)
f.seek(0, 1) # seek between read&write required by ANSI C
np.array([0], dtype=np.float64).tofile(f)
pos = f.tell()
f.close()
assert_equal(pos, 10, err_msg=err_msg)
def _check_from(self, s, value, **kw):
y = np.fromstring(asbytes(s), **kw)
assert_array_equal(y, value)
f = open(self.filename, 'wb')
f.write(asbytes(s))
f.close()
y = np.fromfile(self.filename, **kw)
assert_array_equal(y, value)
def test_nan(self):
self._check_from("nan +nan -nan NaN nan(foo) +NaN(BAR) -NAN(q_u_u_x_)",
[nan, nan, nan, nan, nan, nan, nan],
sep=' ')
def test_inf(self):
self._check_from("inf +inf -inf infinity -Infinity iNfInItY -inF",
[inf, inf, -inf, inf, -inf, inf, -inf], sep=' ')
def test_numbers(self):
self._check_from("1.234 -1.234 .3 .3e55 -123133.1231e+133",
[1.234, -1.234, .3, .3e55, -123133.1231e+133], sep=' ')
def test_binary(self):
self._check_from('\x00\x00\x80?\x00\x00\x00@\x00\x00@@\x00\x00\x80@',
array([1, 2, 3, 4]),
dtype='<f4')
@dec.slow # takes > 1 minute on mechanical hard drive
def test_big_binary(self):
"""Test workarounds for 32-bit limited fwrite, fseek, and ftell
calls in windows. These normally would hang doing something like this.
See http://projects.scipy.org/numpy/ticket/1660"""
if sys.platform != 'win32':
return
try:
# before workarounds, only up to 2**32-1 worked
fourgbplus = 2**32 + 2**16
testbytes = np.arange(8, dtype=np.int8)
n = len(testbytes)
flike = tempfile.NamedTemporaryFile()
f = flike.file
np.tile(testbytes, fourgbplus // testbytes.nbytes).tofile(f)
flike.seek(0)
a = np.fromfile(f, dtype=np.int8)
flike.close()
assert_(len(a) == fourgbplus)
# check only start and end for speed:
assert_((a[:n] == testbytes).all())
assert_((a[-n:] == testbytes).all())
except (MemoryError, ValueError):
pass
def test_string(self):
self._check_from('1,2,3,4', [1., 2., 3., 4.], sep=',')
def test_counted_string(self):
self._check_from('1,2,3,4', [1., 2., 3., 4.], count=4, sep=',')
self._check_from('1,2,3,4', [1., 2., 3.], count=3, sep=',')
self._check_from('1,2,3,4', [1., 2., 3., 4.], count=-1, sep=',')
def test_string_with_ws(self):
self._check_from('1 2 3 4 ', [1, 2, 3, 4], dtype=int, sep=' ')
def test_counted_string_with_ws(self):
self._check_from('1 2 3 4 ', [1, 2, 3], count=3, dtype=int,
sep=' ')
def test_ascii(self):
self._check_from('1 , 2 , 3 , 4', [1., 2., 3., 4.], sep=',')
self._check_from('1,2,3,4', [1., 2., 3., 4.], dtype=float, sep=',')
def test_malformed(self):
self._check_from('1.234 1,234', [1.234, 1.], sep=' ')
def test_long_sep(self):
self._check_from('1_x_3_x_4_x_5', [1, 3, 4, 5], sep='_x_')
def test_dtype(self):
v = np.array([1, 2, 3, 4], dtype=np.int_)
self._check_from('1,2,3,4', v, sep=',', dtype=np.int_)
def test_dtype_bool(self):
# can't use _check_from because fromstring can't handle True/False
v = np.array([True, False, True, False], dtype=np.bool_)
s = '1,0,-2.3,0'
f = open(self.filename, 'wb')
f.write(asbytes(s))
f.close()
y = np.fromfile(self.filename, sep=',', dtype=np.bool_)
assert_(y.dtype == '?')
assert_array_equal(y, v)
def test_tofile_sep(self):
x = np.array([1.51, 2, 3.51, 4], dtype=float)
f = open(self.filename, 'w')
x.tofile(f, sep=',')
f.close()
f = open(self.filename, 'r')
s = f.read()
f.close()
assert_equal(s, '1.51,2.0,3.51,4.0')
def test_tofile_format(self):
x = np.array([1.51, 2, 3.51, 4], dtype=float)
f = open(self.filename, 'w')
x.tofile(f, sep=',', format='%.2f')
f.close()
f = open(self.filename, 'r')
s = f.read()
f.close()
assert_equal(s, '1.51,2.00,3.51,4.00')
def test_locale(self):
in_foreign_locale(self.test_numbers)()
in_foreign_locale(self.test_nan)()
in_foreign_locale(self.test_inf)()
in_foreign_locale(self.test_counted_string)()
in_foreign_locale(self.test_ascii)()
in_foreign_locale(self.test_malformed)()
in_foreign_locale(self.test_tofile_sep)()
in_foreign_locale(self.test_tofile_format)()
class TestFromBuffer(object):
def tst_basic(self, buffer, expected, kwargs):
assert_array_equal(np.frombuffer(buffer,**kwargs), expected)
def test_ip_basic(self):
for byteorder in ['<', '>']:
for dtype in [float, int, np.complex]:
dt = np.dtype(dtype).newbyteorder(byteorder)
x = (np.random.random((4, 7))*5).astype(dt)
buf = x.tobytes()
yield self.tst_basic, buf, x.flat, {'dtype':dt}
def test_empty(self):
yield self.tst_basic, asbytes(''), np.array([]), {}
class TestFlat(TestCase):
def setUp(self):
a0 = arange(20.0)
a = a0.reshape(4, 5)
a0.shape = (4, 5)
a.flags.writeable = False
self.a = a
self.b = a[::2, ::2]
self.a0 = a0
self.b0 = a0[::2, ::2]
def test_contiguous(self):
testpassed = False
try:
self.a.flat[12] = 100.0
except ValueError:
testpassed = True
assert testpassed
assert self.a.flat[12] == 12.0
def test_discontiguous(self):
testpassed = False
try:
self.b.flat[4] = 100.0
except ValueError:
testpassed = True
assert testpassed
assert self.b.flat[4] == 12.0
def test___array__(self):
c = self.a.flat.__array__()
d = self.b.flat.__array__()
e = self.a0.flat.__array__()
f = self.b0.flat.__array__()
assert c.flags.writeable is False
assert d.flags.writeable is False
assert e.flags.writeable is True
assert f.flags.writeable is True
assert c.flags.updateifcopy is False
assert d.flags.updateifcopy is False
assert e.flags.updateifcopy is False
assert f.flags.updateifcopy is True
assert f.base is self.b0
class TestResize(TestCase):
def test_basic(self):
x = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]])
x.resize((5, 5))
assert_array_equal(x.flat[:9],
np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]]).flat)
assert_array_equal(x[9:].flat, 0)
def test_check_reference(self):
x = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]])
y = x
self.assertRaises(ValueError, x.resize, (5, 1))
def test_int_shape(self):
x = np.eye(3)
x.resize(3)
assert_array_equal(x, np.eye(3)[0,:])
def test_none_shape(self):
x = np.eye(3)
x.resize(None)
assert_array_equal(x, np.eye(3))
x.resize()
assert_array_equal(x, np.eye(3))
def test_invalid_arguements(self):
self.assertRaises(TypeError, np.eye(3).resize, 'hi')
self.assertRaises(ValueError, np.eye(3).resize, -1)
self.assertRaises(TypeError, np.eye(3).resize, order=1)
self.assertRaises(TypeError, np.eye(3).resize, refcheck='hi')
def test_freeform_shape(self):
x = np.eye(3)
x.resize(3, 2, 1)
assert_(x.shape == (3, 2, 1))
def test_zeros_appended(self):
x = np.eye(3)
x.resize(2, 3, 3)
assert_array_equal(x[0], np.eye(3))
assert_array_equal(x[1], np.zeros((3, 3)))
def test_obj_obj(self):
# check memory is initialized on resize, gh-4857
a = ones(10, dtype=[('k', object, 2)])
a.resize(15,)
assert_equal(a.shape, (15,))
assert_array_equal(a['k'][-5:], 0)
assert_array_equal(a['k'][:-5], 1)
class TestRecord(TestCase):
def test_field_rename(self):
dt = np.dtype([('f', float), ('i', int)])
dt.names = ['p', 'q']
assert_equal(dt.names, ['p', 'q'])
if sys.version_info[0] >= 3:
def test_bytes_fields(self):
# Bytes are not allowed in field names and not recognized in titles
# on Py3
assert_raises(TypeError, np.dtype, [(asbytes('a'), int)])
assert_raises(TypeError, np.dtype, [(('b', asbytes('a')), int)])
dt = np.dtype([((asbytes('a'), 'b'), int)])
assert_raises(ValueError, dt.__getitem__, asbytes('a'))
x = np.array([(1,), (2,), (3,)], dtype=dt)
assert_raises(IndexError, x.__getitem__, asbytes('a'))
y = x[0]
assert_raises(IndexError, y.__getitem__, asbytes('a'))
else:
def test_unicode_field_titles(self):
# Unicode field titles are added to field dict on Py2
title = unicode('b')
dt = np.dtype([((title, 'a'), int)])
dt[title]
dt['a']
x = np.array([(1,), (2,), (3,)], dtype=dt)
x[title]
x['a']
y = x[0]
y[title]
y['a']
def test_unicode_field_names(self):
# Unicode field names are not allowed on Py2
title = unicode('b')
assert_raises(TypeError, np.dtype, [(title, int)])
assert_raises(TypeError, np.dtype, [(('a', title), int)])
def test_field_names(self):
# Test unicode and 8-bit / byte strings can be used
a = np.zeros((1,), dtype=[('f1', 'i4'),
('f2', 'i4'),
('f3', [('sf1', 'i4')])])
is_py3 = sys.version_info[0] >= 3
if is_py3:
funcs = (str,)
# byte string indexing fails gracefully
assert_raises(IndexError, a.__setitem__, asbytes('f1'), 1)
assert_raises(IndexError, a.__getitem__, asbytes('f1'))
assert_raises(IndexError, a['f1'].__setitem__, asbytes('sf1'), 1)
assert_raises(IndexError, a['f1'].__getitem__, asbytes('sf1'))
else:
funcs = (str, unicode)
for func in funcs:
b = a.copy()
fn1 = func('f1')
b[fn1] = 1
assert_equal(b[fn1], 1)
fnn = func('not at all')
assert_raises(ValueError, b.__setitem__, fnn, 1)
assert_raises(ValueError, b.__getitem__, fnn)
b[0][fn1] = 2
assert_equal(b[fn1], 2)
# Subfield
assert_raises(IndexError, b[0].__setitem__, fnn, 1)
assert_raises(IndexError, b[0].__getitem__, fnn)
# Subfield
fn3 = func('f3')
sfn1 = func('sf1')
b[fn3][sfn1] = 1
assert_equal(b[fn3][sfn1], 1)
assert_raises(ValueError, b[fn3].__setitem__, fnn, 1)
assert_raises(ValueError, b[fn3].__getitem__, fnn)
# multiple Subfields
fn2 = func('f2')
b[fn2] = 3
assert_equal(b[['f1', 'f2']][0].tolist(), (2, 3))
assert_equal(b[['f2', 'f1']][0].tolist(), (3, 2))
assert_equal(b[['f1', 'f3']][0].tolist(), (2, (1,)))
# view of subfield view/copy
assert_equal(b[['f1', 'f2']][0].view(('i4', 2)).tolist(), (2, 3))
assert_equal(b[['f2', 'f1']][0].view(('i4', 2)).tolist(), (3, 2))
view_dtype=[('f1', 'i4'), ('f3', [('', 'i4')])]
assert_equal(b[['f1', 'f3']][0].view(view_dtype).tolist(), (2, (1,)))
# non-ascii unicode field indexing is well behaved
if not is_py3:
raise SkipTest('non ascii unicode field indexing skipped; '
'raises segfault on python 2.x')
else:
assert_raises(ValueError, a.__setitem__, sixu('\u03e0'), 1)
assert_raises(ValueError, a.__getitem__, sixu('\u03e0'))
def test_field_names_deprecation(self):
def collect_warnings(f, *args, **kwargs):
with warnings.catch_warnings(record=True) as log:
warnings.simplefilter("always")
f(*args, **kwargs)
return [w.category for w in log]
a = np.zeros((1,), dtype=[('f1', 'i4'),
('f2', 'i4'),
('f3', [('sf1', 'i4')])])
a['f1'][0] = 1
a['f2'][0] = 2
a['f3'][0] = (3,)
b = np.zeros((1,), dtype=[('f1', 'i4'),
('f2', 'i4'),
('f3', [('sf1', 'i4')])])
b['f1'][0] = 1
b['f2'][0] = 2
b['f3'][0] = (3,)
# All the different functions raise a warning, but not an error, and
# 'a' is not modified:
assert_equal(collect_warnings(a[['f1', 'f2']].__setitem__, 0, (10, 20)),
[FutureWarning])
assert_equal(a, b)
# Views also warn
subset = a[['f1', 'f2']]
subset_view = subset.view()
assert_equal(collect_warnings(subset_view['f1'].__setitem__, 0, 10),
[FutureWarning])
# But the write goes through:
assert_equal(subset['f1'][0], 10)
# Only one warning per multiple field indexing, though (even if there
# are multiple views involved):
assert_equal(collect_warnings(subset['f1'].__setitem__, 0, 10), [])
def test_record_hash(self):
a = np.array([(1, 2), (1, 2)], dtype='i1,i2')
a.flags.writeable = False
b = np.array([(1, 2), (3, 4)], dtype=[('num1', 'i1'), ('num2', 'i2')])
b.flags.writeable = False
c = np.array([(1, 2), (3, 4)], dtype='i1,i2')
c.flags.writeable = False
self.assertTrue(hash(a[0]) == hash(a[1]))
self.assertTrue(hash(a[0]) == hash(b[0]))
self.assertTrue(hash(a[0]) != hash(b[1]))
self.assertTrue(hash(c[0]) == hash(a[0]) and c[0] == a[0])
def test_record_no_hash(self):
a = np.array([(1, 2), (1, 2)], dtype='i1,i2')
self.assertRaises(TypeError, hash, a[0])
def test_empty_structure_creation(self):
# make sure these do not raise errors (gh-5631)
array([()], dtype={'names': [], 'formats': [],
'offsets': [], 'itemsize': 12})
array([(), (), (), (), ()], dtype={'names': [], 'formats': [],
'offsets': [], 'itemsize': 12})
class TestView(TestCase):
def test_basic(self):
x = np.array([(1, 2, 3, 4), (5, 6, 7, 8)],
dtype=[('r', np.int8), ('g', np.int8),
('b', np.int8), ('a', np.int8)])
# We must be specific about the endianness here:
y = x.view(dtype='<i4')
# ... and again without the keyword.
z = x.view('<i4')
assert_array_equal(y, z)
assert_array_equal(y, [67305985, 134678021])
def _mean(a, **args):
return a.mean(**args)
def _var(a, **args):
return a.var(**args)
def _std(a, **args):
return a.std(**args)
class TestStats(TestCase):
funcs = [_mean, _var, _std]
def setUp(self):
np.random.seed(range(3))
self.rmat = np.random.random((4, 5))
self.cmat = self.rmat + 1j * self.rmat
self.omat = np.array([Decimal(repr(r)) for r in self.rmat.flat])
self.omat = self.omat.reshape(4, 5)
def test_keepdims(self):
mat = np.eye(3)
for f in self.funcs:
for axis in [0, 1]:
res = f(mat, axis=axis, keepdims=True)
assert_(res.ndim == mat.ndim)
assert_(res.shape[axis] == 1)
for axis in [None]:
res = f(mat, axis=axis, keepdims=True)
assert_(res.shape == (1, 1))
def test_out(self):
mat = np.eye(3)
for f in self.funcs:
out = np.zeros(3)
tgt = f(mat, axis=1)
res = f(mat, axis=1, out=out)
assert_almost_equal(res, out)
assert_almost_equal(res, tgt)
out = np.empty(2)
assert_raises(ValueError, f, mat, axis=1, out=out)
out = np.empty((2, 2))
assert_raises(ValueError, f, mat, axis=1, out=out)
def test_dtype_from_input(self):
icodes = np.typecodes['AllInteger']
fcodes = np.typecodes['AllFloat']
# object type
for f in self.funcs:
mat = np.array([[Decimal(1)]*3]*3)
tgt = mat.dtype.type
res = f(mat, axis=1).dtype.type
assert_(res is tgt)
# scalar case
res = type(f(mat, axis=None))
assert_(res is Decimal)
# integer types
for f in self.funcs:
for c in icodes:
mat = np.eye(3, dtype=c)
tgt = np.float64
res = f(mat, axis=1).dtype.type
assert_(res is tgt)
# scalar case
res = f(mat, axis=None).dtype.type
assert_(res is tgt)
# mean for float types
for f in [_mean]:
for c in fcodes:
mat = np.eye(3, dtype=c)
tgt = mat.dtype.type
res = f(mat, axis=1).dtype.type
assert_(res is tgt)
# scalar case
res = f(mat, axis=None).dtype.type
assert_(res is tgt)
# var, std for float types
for f in [_var, _std]:
for c in fcodes:
mat = np.eye(3, dtype=c)
# deal with complex types
tgt = mat.real.dtype.type
res = f(mat, axis=1).dtype.type
assert_(res is tgt)
# scalar case
res = f(mat, axis=None).dtype.type
assert_(res is tgt)
def test_dtype_from_dtype(self):
icodes = np.typecodes['AllInteger']
fcodes = np.typecodes['AllFloat']
mat = np.eye(3)
# stats for integer types
# fixme:
# this needs definition as there are lots places along the line
# where type casting may take place.
#for f in self.funcs:
#for c in icodes:
#tgt = np.dtype(c).type
#res = f(mat, axis=1, dtype=c).dtype.type
#assert_(res is tgt)
## scalar case
#res = f(mat, axis=None, dtype=c).dtype.type
#assert_(res is tgt)
# stats for float types
for f in self.funcs:
for c in fcodes:
tgt = np.dtype(c).type
res = f(mat, axis=1, dtype=c).dtype.type
assert_(res is tgt)
# scalar case
res = f(mat, axis=None, dtype=c).dtype.type
assert_(res is tgt)
def test_ddof(self):
for f in [_var]:
for ddof in range(3):
dim = self.rmat.shape[1]
tgt = f(self.rmat, axis=1) * dim
res = f(self.rmat, axis=1, ddof=ddof) * (dim - ddof)
for f in [_std]:
for ddof in range(3):
dim = self.rmat.shape[1]
tgt = f(self.rmat, axis=1) * np.sqrt(dim)
res = f(self.rmat, axis=1, ddof=ddof) * np.sqrt(dim - ddof)
assert_almost_equal(res, tgt)
assert_almost_equal(res, tgt)
def test_ddof_too_big(self):
dim = self.rmat.shape[1]
for f in [_var, _std]:
for ddof in range(dim, dim + 2):
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always')
res = f(self.rmat, axis=1, ddof=ddof)
assert_(not (res < 0).any())
assert_(len(w) > 0)
assert_(issubclass(w[0].category, RuntimeWarning))
def test_empty(self):
A = np.zeros((0, 3))
for f in self.funcs:
for axis in [0, None]:
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always')
assert_(np.isnan(f(A, axis=axis)).all())
assert_(len(w) > 0)
assert_(issubclass(w[0].category, RuntimeWarning))
for axis in [1]:
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always')
assert_equal(f(A, axis=axis), np.zeros([]))
def test_mean_values(self):
for mat in [self.rmat, self.cmat, self.omat]:
for axis in [0, 1]:
tgt = mat.sum(axis=axis)
res = _mean(mat, axis=axis) * mat.shape[axis]
assert_almost_equal(res, tgt)
for axis in [None]:
tgt = mat.sum(axis=axis)
res = _mean(mat, axis=axis) * np.prod(mat.shape)
assert_almost_equal(res, tgt)
def test_var_values(self):
for mat in [self.rmat, self.cmat, self.omat]:
for axis in [0, 1, None]:
msqr = _mean(mat * mat.conj(), axis=axis)
mean = _mean(mat, axis=axis)
tgt = msqr - mean * mean.conjugate()
res = _var(mat, axis=axis)
assert_almost_equal(res, tgt)
def test_std_values(self):
for mat in [self.rmat, self.cmat, self.omat]:
for axis in [0, 1, None]:
tgt = np.sqrt(_var(mat, axis=axis))
res = _std(mat, axis=axis)
assert_almost_equal(res, tgt)
def test_subclass(self):
class TestArray(np.ndarray):
def __new__(cls, data, info):
result = np.array(data)
result = result.view(cls)
result.info = info
return result
def __array_finalize__(self, obj):
self.info = getattr(obj, "info", '')
dat = TestArray([[1, 2, 3, 4], [5, 6, 7, 8]], 'jubba')
res = dat.mean(1)
assert_(res.info == dat.info)
res = dat.std(1)
assert_(res.info == dat.info)
res = dat.var(1)
assert_(res.info == dat.info)
class TestVdot(TestCase):
def test_basic(self):
dt_numeric = np.typecodes['AllFloat'] + np.typecodes['AllInteger']
dt_complex = np.typecodes['Complex']
# test real
a = np.eye(3)
for dt in dt_numeric + 'O':
b = a.astype(dt)
res = np.vdot(b, b)
assert_(np.isscalar(res))
assert_equal(np.vdot(b, b), 3)
# test complex
a = np.eye(3) * 1j
for dt in dt_complex + 'O':
b = a.astype(dt)
res = np.vdot(b, b)
assert_(np.isscalar(res))
assert_equal(np.vdot(b, b), 3)
# test boolean
b = np.eye(3, dtype=np.bool)
res = np.vdot(b, b)
assert_(np.isscalar(res))
assert_equal(np.vdot(b, b), True)
def test_vdot_array_order(self):
a = array([[1, 2], [3, 4]], order='C')
b = array([[1, 2], [3, 4]], order='F')
res = np.vdot(a, a)
# integer arrays are exact
assert_equal(np.vdot(a, b), res)
assert_equal(np.vdot(b, a), res)
assert_equal(np.vdot(b, b), res)
class TestDot(TestCase):
def test_dot_2args(self):
from numpy.core.multiarray import dot
a = np.array([[1, 2], [3, 4]], dtype=float)
b = np.array([[1, 0], [1, 1]], dtype=float)
c = np.array([[3, 2], [7, 4]], dtype=float)
d = dot(a, b)
assert_allclose(c, d)
def test_dot_3args(self):
from numpy.core.multiarray import dot
np.random.seed(22)
f = np.random.random_sample((1024, 16))
v = np.random.random_sample((16, 32))
r = np.empty((1024, 32))
for i in range(12):
dot(f, v, r)
assert_equal(sys.getrefcount(r), 2)
r2 = dot(f, v, out=None)
assert_array_equal(r2, r)
assert_(r is dot(f, v, out=r))
v = v[:, 0].copy() # v.shape == (16,)
r = r[:, 0].copy() # r.shape == (1024,)
r2 = dot(f, v)
assert_(r is dot(f, v, r))
assert_array_equal(r2, r)
def test_dot_3args_errors(self):
from numpy.core.multiarray import dot
np.random.seed(22)
f = np.random.random_sample((1024, 16))
v = np.random.random_sample((16, 32))
r = np.empty((1024, 31))
assert_raises(ValueError, dot, f, v, r)
r = np.empty((1024,))
assert_raises(ValueError, dot, f, v, r)
r = np.empty((32,))
assert_raises(ValueError, dot, f, v, r)
r = np.empty((32, 1024))
assert_raises(ValueError, dot, f, v, r)
assert_raises(ValueError, dot, f, v, r.T)
r = np.empty((1024, 64))
assert_raises(ValueError, dot, f, v, r[:, ::2])
assert_raises(ValueError, dot, f, v, r[:, :32])
r = np.empty((1024, 32), dtype=np.float32)
assert_raises(ValueError, dot, f, v, r)
r = np.empty((1024, 32), dtype=int)
assert_raises(ValueError, dot, f, v, r)
def test_dot_array_order(self):
a = array([[1, 2], [3, 4]], order='C')
b = array([[1, 2], [3, 4]], order='F')
res = np.dot(a, a)
# integer arrays are exact
assert_equal(np.dot(a, b), res)
assert_equal(np.dot(b, a), res)
assert_equal(np.dot(b, b), res)
def test_dot_scalar_and_matrix_of_objects(self):
# Ticket #2469
arr = np.matrix([1, 2], dtype=object)
desired = np.matrix([[3, 6]], dtype=object)
assert_equal(np.dot(arr, 3), desired)
assert_equal(np.dot(3, arr), desired)
def test_dot_override(self):
class A(object):
def __numpy_ufunc__(self, ufunc, method, pos, inputs, **kwargs):
return "A"
class B(object):
def __numpy_ufunc__(self, ufunc, method, pos, inputs, **kwargs):
return NotImplemented
a = A()
b = B()
c = np.array([[1]])
assert_equal(np.dot(a, b), "A")
assert_equal(c.dot(a), "A")
assert_raises(TypeError, np.dot, b, c)
assert_raises(TypeError, c.dot, b)
def test_accelerate_framework_sgemv_fix(self):
if sys.platform != 'darwin':
return
def aligned_array(shape, align, dtype, order='C'):
d = dtype()
N = np.prod(shape)
tmp = np.zeros(N * d.nbytes + align, dtype=np.uint8)
address = tmp.__array_interface__["data"][0]
for offset in range(align):
if (address + offset) % align == 0:
break
tmp = tmp[offset:offset+N*d.nbytes].view(dtype=dtype)
return tmp.reshape(shape, order=order)
def as_aligned(arr, align, dtype, order='C'):
aligned = aligned_array(arr.shape, align, dtype, order)
aligned[:] = arr[:]
return aligned
def assert_dot_close(A, X, desired):
assert_allclose(np.dot(A, X), desired, rtol=1e-5, atol=1e-7)
m = aligned_array(100, 15, np.float32)
s = aligned_array((100, 100), 15, np.float32)
np.dot(s, m) # this will always segfault if the bug is present
testdata = itertools.product((15,32), (10000,), (200,89), ('C','F'))
for align, m, n, a_order in testdata:
# Calculation in double precision
A_d = np.random.rand(m, n)
X_d = np.random.rand(n)
desired = np.dot(A_d, X_d)
# Calculation with aligned single precision
A_f = as_aligned(A_d, align, np.float32, order=a_order)
X_f = as_aligned(X_d, align, np.float32)
assert_dot_close(A_f, X_f, desired)
# Strided A rows
A_d_2 = A_d[::2]
desired = np.dot(A_d_2, X_d)
A_f_2 = A_f[::2]
assert_dot_close(A_f_2, X_f, desired)
# Strided A columns, strided X vector
A_d_22 = A_d_2[:, ::2]
X_d_2 = X_d[::2]
desired = np.dot(A_d_22, X_d_2)
A_f_22 = A_f_2[:, ::2]
X_f_2 = X_f[::2]
assert_dot_close(A_f_22, X_f_2, desired)
# Check the strides are as expected
if a_order == 'F':
assert_equal(A_f_22.strides, (8, 8 * m))
else:
assert_equal(A_f_22.strides, (8 * n, 8))
assert_equal(X_f_2.strides, (8,))
# Strides in A rows + cols only
X_f_2c = as_aligned(X_f_2, align, np.float32)
assert_dot_close(A_f_22, X_f_2c, desired)
# Strides just in A cols
A_d_12 = A_d[:, ::2]
desired = np.dot(A_d_12, X_d_2)
A_f_12 = A_f[:, ::2]
assert_dot_close(A_f_12, X_f_2c, desired)
# Strides in A cols and X
assert_dot_close(A_f_12, X_f_2, desired)
class MatmulCommon():
"""Common tests for '@' operator and numpy.matmul.
Do not derive from TestCase to avoid nose running it.
"""
# Should work with these types. Will want to add
# "O" at some point
types = "?bhilqBHILQefdgFDG"
def test_exceptions(self):
dims = [
((1,), (2,)), # mismatched vector vector
((2, 1,), (2,)), # mismatched matrix vector
((2,), (1, 2)), # mismatched vector matrix
((1, 2), (3, 1)), # mismatched matrix matrix
((1,), ()), # vector scalar
((), (1)), # scalar vector
((1, 1), ()), # matrix scalar
((), (1, 1)), # scalar matrix
((2, 2, 1), (3, 1, 2)), # cannot broadcast
]
for dt, (dm1, dm2) in itertools.product(self.types, dims):
a = np.ones(dm1, dtype=dt)
b = np.ones(dm2, dtype=dt)
assert_raises(ValueError, self.matmul, a, b)
def test_shapes(self):
dims = [
((1, 1), (2, 1, 1)), # broadcast first argument
((2, 1, 1), (1, 1)), # broadcast second argument
((2, 1, 1), (2, 1, 1)), # matrix stack sizes match
]
for dt, (dm1, dm2) in itertools.product(self.types, dims):
a = np.ones(dm1, dtype=dt)
b = np.ones(dm2, dtype=dt)
res = self.matmul(a, b)
assert_(res.shape == (2, 1, 1))
# vector vector returns scalars.
for dt in self.types:
a = np.ones((2,), dtype=dt)
b = np.ones((2,), dtype=dt)
c = self.matmul(a, b)
assert_(np.array(c).shape == ())
def test_result_types(self):
mat = np.ones((1,1))
vec = np.ones((1,))
for dt in self.types:
m = mat.astype(dt)
v = vec.astype(dt)
for arg in [(m, v), (v, m), (m, m)]:
res = matmul(*arg)
assert_(res.dtype == dt)
# vector vector returns scalars
res = matmul(v, v)
assert_(type(res) is dtype(dt).type)
def test_vector_vector_values(self):
vec = np.array([1, 2])
tgt = 5
for dt in self.types[1:]:
v1 = vec.astype(dt)
res = matmul(v1, v1)
assert_equal(res, tgt)
# boolean type
vec = np.array([True, True], dtype='?')
res = matmul(vec, vec)
assert_equal(res, True)
def test_vector_matrix_values(self):
vec = np.array([1, 2])
mat1 = np.array([[1, 2], [3, 4]])
mat2 = np.stack([mat1]*2, axis=0)
tgt1 = np.array([7, 10])
tgt2 = np.stack([tgt1]*2, axis=0)
for dt in self.types[1:]:
v = vec.astype(dt)
m1 = mat1.astype(dt)
m2 = mat2.astype(dt)
res = matmul(v, m1)
assert_equal(res, tgt1)
res = matmul(v, m2)
assert_equal(res, tgt2)
# boolean type
vec = np.array([True, False])
mat1 = np.array([[True, False], [False, True]])
mat2 = np.stack([mat1]*2, axis=0)
tgt1 = np.array([True, False])
tgt2 = np.stack([tgt1]*2, axis=0)
res = matmul(vec, mat1)
assert_equal(res, tgt1)
res = matmul(vec, mat2)
assert_equal(res, tgt2)
def test_matrix_vector_values(self):
vec = np.array([1, 2])
mat1 = np.array([[1, 2], [3, 4]])
mat2 = np.stack([mat1]*2, axis=0)
tgt1 = np.array([5, 11])
tgt2 = np.stack([tgt1]*2, axis=0)
for dt in self.types[1:]:
v = vec.astype(dt)
m1 = mat1.astype(dt)
m2 = mat2.astype(dt)
res = matmul(m1, v)
assert_equal(res, tgt1)
res = matmul(m2, v)
assert_equal(res, tgt2)
# boolean type
vec = np.array([True, False])
mat1 = np.array([[True, False], [False, True]])
mat2 = np.stack([mat1]*2, axis=0)
tgt1 = np.array([True, False])
tgt2 = np.stack([tgt1]*2, axis=0)
res = matmul(vec, mat1)
assert_equal(res, tgt1)
res = matmul(vec, mat2)
assert_equal(res, tgt2)
def test_matrix_matrix_values(self):
mat1 = np.array([[1, 2], [3, 4]])
mat2 = np.array([[1, 0], [1, 1]])
mat12 = np.stack([mat1, mat2], axis=0)
mat21 = np.stack([mat2, mat1], axis=0)
tgt11 = np.array([[7, 10], [15, 22]])
tgt12 = np.array([[3, 2], [7, 4]])
tgt21 = np.array([[1, 2], [4, 6]])
tgt22 = np.array([[1, 0], [2, 1]])
tgt12_21 = np.stack([tgt12, tgt21], axis=0)
tgt11_12 = np.stack((tgt11, tgt12), axis=0)
tgt11_21 = np.stack((tgt11, tgt21), axis=0)
for dt in self.types[1:]:
m1 = mat1.astype(dt)
m2 = mat2.astype(dt)
m12 = mat12.astype(dt)
m21 = mat21.astype(dt)
# matrix @ matrix
res = matmul(m1, m2)
assert_equal(res, tgt12)
res = matmul(m2, m1)
assert_equal(res, tgt21)
# stacked @ matrix
res = self.matmul(m12, m1)
assert_equal(res, tgt11_21)
# matrix @ stacked
res = self.matmul(m1, m12)
assert_equal(res, tgt11_12)
# stacked @ stacked
res = self.matmul(m12, m21)
assert_equal(res, tgt12_21)
# boolean type
m1 = np.array([[1, 1], [0, 0]], dtype=np.bool_)
m2 = np.array([[1, 0], [1, 1]], dtype=np.bool_)
m12 = np.stack([m1, m2], axis=0)
m21 = np.stack([m2, m1], axis=0)
tgt11 = m1
tgt12 = m1
tgt21 = np.array([[1, 1], [1, 1]], dtype=np.bool_)
tgt22 = m2
tgt12_21 = np.stack([tgt12, tgt21], axis=0)
tgt11_12 = np.stack((tgt11, tgt12), axis=0)
tgt11_21 = np.stack((tgt11, tgt21), axis=0)
# matrix @ matrix
res = matmul(m1, m2)
assert_equal(res, tgt12)
res = matmul(m2, m1)
assert_equal(res, tgt21)
# stacked @ matrix
res = self.matmul(m12, m1)
assert_equal(res, tgt11_21)
# matrix @ stacked
res = self.matmul(m1, m12)
assert_equal(res, tgt11_12)
# stacked @ stacked
res = self.matmul(m12, m21)
assert_equal(res, tgt12_21)
def test_numpy_ufunc_override(self):
class A(np.ndarray):
def __new__(cls, *args, **kwargs):
return np.array(*args, **kwargs).view(cls)
def __numpy_ufunc__(self, ufunc, method, pos, inputs, **kwargs):
return "A"
class B(np.ndarray):
def __new__(cls, *args, **kwargs):
return np.array(*args, **kwargs).view(cls)
def __numpy_ufunc__(self, ufunc, method, pos, inputs, **kwargs):
return NotImplemented
a = A([1, 2])
b = B([1, 2])
c = ones(2)
assert_equal(self.matmul(a, b), "A")
assert_equal(self.matmul(b, a), "A")
assert_raises(TypeError, self.matmul, b, c)
class TestMatmul(MatmulCommon, TestCase):
matmul = np.matmul
def test_out_arg(self):
a = np.ones((2, 2), dtype=np.float)
b = np.ones((2, 2), dtype=np.float)
tgt = np.full((2,2), 2, dtype=np.float)
# test as positional argument
msg = "out positional argument"
out = np.zeros((2, 2), dtype=np.float)
self.matmul(a, b, out)
assert_array_equal(out, tgt, err_msg=msg)
# test as keyword argument
msg = "out keyword argument"
out = np.zeros((2, 2), dtype=np.float)
self.matmul(a, b, out=out)
assert_array_equal(out, tgt, err_msg=msg)
# test out with not allowed type cast (safe casting)
# einsum and cblas raise different error types, so
# use Exception.
msg = "out argument with illegal cast"
out = np.zeros((2, 2), dtype=np.int32)
assert_raises(Exception, self.matmul, a, b, out=out)
# skip following tests for now, cblas does not allow non-contiguous
# outputs and consistency with dot would require same type,
# dimensions, subtype, and c_contiguous.
# test out with allowed type cast
# msg = "out argument with allowed cast"
# out = np.zeros((2, 2), dtype=np.complex128)
# self.matmul(a, b, out=out)
# assert_array_equal(out, tgt, err_msg=msg)
# test out non-contiguous
# msg = "out argument with non-contiguous layout"
# c = np.zeros((2, 2, 2), dtype=np.float)
# self.matmul(a, b, out=c[..., 0])
# assert_array_equal(c, tgt, err_msg=msg)
if sys.version_info[:2] >= (3, 5):
class TestMatmulOperator(MatmulCommon, TestCase):
from operator import matmul
def test_array_priority_override(self):
class A(object):
__array_priority__ = 1000
def __matmul__(self, other):
return "A"
def __rmatmul__(self, other):
return "A"
a = A()
b = ones(2)
assert_equal(self.matmul(a, b), "A")
assert_equal(self.matmul(b, a), "A")
def test_matmul_inplace():
# It would be nice to support in-place matmul eventually, but for now
# we don't have a working implementation, so better just to error out
# and nudge people to writing "a = a @ b".
a = np.eye(3)
b = np.eye(3)
assert_raises(TypeError, a.__imatmul__, b)
import operator
assert_raises(TypeError, operator.imatmul, a, b)
# we avoid writing the token `exec` so as not to crash python 2's
# parser
exec_ = getattr(builtins, "exec")
assert_raises(TypeError, exec_, "a @= b", globals(), locals())
class TestInner(TestCase):
def test_inner_scalar_and_matrix_of_objects(self):
# Ticket #4482
arr = np.matrix([1, 2], dtype=object)
desired = np.matrix([[3, 6]], dtype=object)
assert_equal(np.inner(arr, 3), desired)
assert_equal(np.inner(3, arr), desired)
def test_vecself(self):
# Ticket 844.
# Inner product of a vector with itself segfaults or give
# meaningless result
a = zeros(shape = (1, 80), dtype = float64)
p = inner(a, a)
assert_almost_equal(p, 0, decimal=14)
class TestSummarization(TestCase):
def test_1d(self):
A = np.arange(1001)
strA = '[ 0 1 2 ..., 998 999 1000]'
assert_(str(A) == strA)
reprA = 'array([ 0, 1, 2, ..., 998, 999, 1000])'
assert_(repr(A) == reprA)
def test_2d(self):
A = np.arange(1002).reshape(2, 501)
strA = '[[ 0 1 2 ..., 498 499 500]\n' \
' [ 501 502 503 ..., 999 1000 1001]]'
assert_(str(A) == strA)
reprA = 'array([[ 0, 1, 2, ..., 498, 499, 500],\n' \
' [ 501, 502, 503, ..., 999, 1000, 1001]])'
assert_(repr(A) == reprA)
class TestChoose(TestCase):
def setUp(self):
self.x = 2*ones((3,), dtype=int)
self.y = 3*ones((3,), dtype=int)
self.x2 = 2*ones((2, 3), dtype=int)
self.y2 = 3*ones((2, 3), dtype=int)
self.ind = [0, 0, 1]
def test_basic(self):
A = np.choose(self.ind, (self.x, self.y))
assert_equal(A, [2, 2, 3])
def test_broadcast1(self):
A = np.choose(self.ind, (self.x2, self.y2))
assert_equal(A, [[2, 2, 3], [2, 2, 3]])
def test_broadcast2(self):
A = np.choose(self.ind, (self.x, self.y2))
assert_equal(A, [[2, 2, 3], [2, 2, 3]])
# TODO: test for multidimensional
NEIGH_MODE = {'zero': 0, 'one': 1, 'constant': 2, 'circular': 3, 'mirror': 4}
class TestNeighborhoodIter(TestCase):
# Simple, 2d tests
def _test_simple2d(self, dt):
# Test zero and one padding for simple data type
x = np.array([[0, 1], [2, 3]], dtype=dt)
r = [np.array([[0, 0, 0], [0, 0, 1]], dtype=dt),
np.array([[0, 0, 0], [0, 1, 0]], dtype=dt),
np.array([[0, 0, 1], [0, 2, 3]], dtype=dt),
np.array([[0, 1, 0], [2, 3, 0]], dtype=dt)]
l = test_neighborhood_iterator(x, [-1, 0, -1, 1], x[0],
NEIGH_MODE['zero'])
assert_array_equal(l, r)
r = [np.array([[1, 1, 1], [1, 0, 1]], dtype=dt),
np.array([[1, 1, 1], [0, 1, 1]], dtype=dt),
np.array([[1, 0, 1], [1, 2, 3]], dtype=dt),
np.array([[0, 1, 1], [2, 3, 1]], dtype=dt)]
l = test_neighborhood_iterator(x, [-1, 0, -1, 1], x[0],
NEIGH_MODE['one'])
assert_array_equal(l, r)
r = [np.array([[4, 4, 4], [4, 0, 1]], dtype=dt),
np.array([[4, 4, 4], [0, 1, 4]], dtype=dt),
np.array([[4, 0, 1], [4, 2, 3]], dtype=dt),
np.array([[0, 1, 4], [2, 3, 4]], dtype=dt)]
l = test_neighborhood_iterator(x, [-1, 0, -1, 1], 4,
NEIGH_MODE['constant'])
assert_array_equal(l, r)
def test_simple2d(self):
self._test_simple2d(np.float)
def test_simple2d_object(self):
self._test_simple2d(Decimal)
def _test_mirror2d(self, dt):
x = np.array([[0, 1], [2, 3]], dtype=dt)
r = [np.array([[0, 0, 1], [0, 0, 1]], dtype=dt),
np.array([[0, 1, 1], [0, 1, 1]], dtype=dt),
np.array([[0, 0, 1], [2, 2, 3]], dtype=dt),
np.array([[0, 1, 1], [2, 3, 3]], dtype=dt)]
l = test_neighborhood_iterator(x, [-1, 0, -1, 1], x[0],
NEIGH_MODE['mirror'])
assert_array_equal(l, r)
def test_mirror2d(self):
self._test_mirror2d(np.float)
def test_mirror2d_object(self):
self._test_mirror2d(Decimal)
# Simple, 1d tests
def _test_simple(self, dt):
# Test padding with constant values
x = np.linspace(1, 5, 5).astype(dt)
r = [[0, 1, 2], [1, 2, 3], [2, 3, 4], [3, 4, 5], [4, 5, 0]]
l = test_neighborhood_iterator(x, [-1, 1], x[0], NEIGH_MODE['zero'])
assert_array_equal(l, r)
r = [[1, 1, 2], [1, 2, 3], [2, 3, 4], [3, 4, 5], [4, 5, 1]]
l = test_neighborhood_iterator(x, [-1, 1], x[0], NEIGH_MODE['one'])
assert_array_equal(l, r)
r = [[x[4], 1, 2], [1, 2, 3], [2, 3, 4], [3, 4, 5], [4, 5, x[4]]]
l = test_neighborhood_iterator(x, [-1, 1], x[4], NEIGH_MODE['constant'])
assert_array_equal(l, r)
def test_simple_float(self):
self._test_simple(np.float)
def test_simple_object(self):
self._test_simple(Decimal)
# Test mirror modes
def _test_mirror(self, dt):
x = np.linspace(1, 5, 5).astype(dt)
r = np.array([[2, 1, 1, 2, 3], [1, 1, 2, 3, 4], [1, 2, 3, 4, 5],
[2, 3, 4, 5, 5], [3, 4, 5, 5, 4]], dtype=dt)
l = test_neighborhood_iterator(x, [-2, 2], x[1], NEIGH_MODE['mirror'])
self.assertTrue([i.dtype == dt for i in l])
assert_array_equal(l, r)
def test_mirror(self):
self._test_mirror(np.float)
def test_mirror_object(self):
self._test_mirror(Decimal)
# Circular mode
def _test_circular(self, dt):
x = np.linspace(1, 5, 5).astype(dt)
r = np.array([[4, 5, 1, 2, 3], [5, 1, 2, 3, 4], [1, 2, 3, 4, 5],
[2, 3, 4, 5, 1], [3, 4, 5, 1, 2]], dtype=dt)
l = test_neighborhood_iterator(x, [-2, 2], x[0], NEIGH_MODE['circular'])
assert_array_equal(l, r)
def test_circular(self):
self._test_circular(np.float)
def test_circular_object(self):
self._test_circular(Decimal)
# Test stacking neighborhood iterators
class TestStackedNeighborhoodIter(TestCase):
# Simple, 1d test: stacking 2 constant-padded neigh iterators
def test_simple_const(self):
dt = np.float64
# Test zero and one padding for simple data type
x = np.array([1, 2, 3], dtype=dt)
r = [np.array([0], dtype=dt),
np.array([0], dtype=dt),
np.array([1], dtype=dt),
np.array([2], dtype=dt),
np.array([3], dtype=dt),
np.array([0], dtype=dt),
np.array([0], dtype=dt)]
l = test_neighborhood_iterator_oob(x, [-2, 4], NEIGH_MODE['zero'],
[0, 0], NEIGH_MODE['zero'])
assert_array_equal(l, r)
r = [np.array([1, 0, 1], dtype=dt),
np.array([0, 1, 2], dtype=dt),
np.array([1, 2, 3], dtype=dt),
np.array([2, 3, 0], dtype=dt),
np.array([3, 0, 1], dtype=dt)]
l = test_neighborhood_iterator_oob(x, [-1, 3], NEIGH_MODE['zero'],
[-1, 1], NEIGH_MODE['one'])
assert_array_equal(l, r)
# 2nd simple, 1d test: stacking 2 neigh iterators, mixing const padding and
# mirror padding
def test_simple_mirror(self):
dt = np.float64
# Stacking zero on top of mirror
x = np.array([1, 2, 3], dtype=dt)
r = [np.array([0, 1, 1], dtype=dt),
np.array([1, 1, 2], dtype=dt),
np.array([1, 2, 3], dtype=dt),
np.array([2, 3, 3], dtype=dt),
np.array([3, 3, 0], dtype=dt)]
l = test_neighborhood_iterator_oob(x, [-1, 3], NEIGH_MODE['mirror'],
[-1, 1], NEIGH_MODE['zero'])
assert_array_equal(l, r)
# Stacking mirror on top of zero
x = np.array([1, 2, 3], dtype=dt)
r = [np.array([1, 0, 0], dtype=dt),
np.array([0, 0, 1], dtype=dt),
np.array([0, 1, 2], dtype=dt),
np.array([1, 2, 3], dtype=dt),
np.array([2, 3, 0], dtype=dt)]
l = test_neighborhood_iterator_oob(x, [-1, 3], NEIGH_MODE['zero'],
[-2, 0], NEIGH_MODE['mirror'])
assert_array_equal(l, r)
# Stacking mirror on top of zero: 2nd
x = np.array([1, 2, 3], dtype=dt)
r = [np.array([0, 1, 2], dtype=dt),
np.array([1, 2, 3], dtype=dt),
np.array([2, 3, 0], dtype=dt),
np.array([3, 0, 0], dtype=dt),
np.array([0, 0, 3], dtype=dt)]
l = test_neighborhood_iterator_oob(x, [-1, 3], NEIGH_MODE['zero'],
[0, 2], NEIGH_MODE['mirror'])
assert_array_equal(l, r)
# Stacking mirror on top of zero: 3rd
x = np.array([1, 2, 3], dtype=dt)
r = [np.array([1, 0, 0, 1, 2], dtype=dt),
np.array([0, 0, 1, 2, 3], dtype=dt),
np.array([0, 1, 2, 3, 0], dtype=dt),
np.array([1, 2, 3, 0, 0], dtype=dt),
np.array([2, 3, 0, 0, 3], dtype=dt)]
l = test_neighborhood_iterator_oob(x, [-1, 3], NEIGH_MODE['zero'],
[-2, 2], NEIGH_MODE['mirror'])
assert_array_equal(l, r)
# 3rd simple, 1d test: stacking 2 neigh iterators, mixing const padding and
# circular padding
def test_simple_circular(self):
dt = np.float64
# Stacking zero on top of mirror
x = np.array([1, 2, 3], dtype=dt)
r = [np.array([0, 3, 1], dtype=dt),
np.array([3, 1, 2], dtype=dt),
np.array([1, 2, 3], dtype=dt),
np.array([2, 3, 1], dtype=dt),
np.array([3, 1, 0], dtype=dt)]
l = test_neighborhood_iterator_oob(x, [-1, 3], NEIGH_MODE['circular'],
[-1, 1], NEIGH_MODE['zero'])
assert_array_equal(l, r)
# Stacking mirror on top of zero
x = np.array([1, 2, 3], dtype=dt)
r = [np.array([3, 0, 0], dtype=dt),
np.array([0, 0, 1], dtype=dt),
np.array([0, 1, 2], dtype=dt),
np.array([1, 2, 3], dtype=dt),
np.array([2, 3, 0], dtype=dt)]
l = test_neighborhood_iterator_oob(x, [-1, 3], NEIGH_MODE['zero'],
[-2, 0], NEIGH_MODE['circular'])
assert_array_equal(l, r)
# Stacking mirror on top of zero: 2nd
x = np.array([1, 2, 3], dtype=dt)
r = [np.array([0, 1, 2], dtype=dt),
np.array([1, 2, 3], dtype=dt),
np.array([2, 3, 0], dtype=dt),
np.array([3, 0, 0], dtype=dt),
np.array([0, 0, 1], dtype=dt)]
l = test_neighborhood_iterator_oob(x, [-1, 3], NEIGH_MODE['zero'],
[0, 2], NEIGH_MODE['circular'])
assert_array_equal(l, r)
# Stacking mirror on top of zero: 3rd
x = np.array([1, 2, 3], dtype=dt)
r = [np.array([3, 0, 0, 1, 2], dtype=dt),
np.array([0, 0, 1, 2, 3], dtype=dt),
np.array([0, 1, 2, 3, 0], dtype=dt),
np.array([1, 2, 3, 0, 0], dtype=dt),
np.array([2, 3, 0, 0, 1], dtype=dt)]
l = test_neighborhood_iterator_oob(x, [-1, 3], NEIGH_MODE['zero'],
[-2, 2], NEIGH_MODE['circular'])
assert_array_equal(l, r)
# 4th simple, 1d test: stacking 2 neigh iterators, but with lower iterator
# being strictly within the array
def test_simple_strict_within(self):
dt = np.float64
# Stacking zero on top of zero, first neighborhood strictly inside the
# array
x = np.array([1, 2, 3], dtype=dt)
r = [np.array([1, 2, 3, 0], dtype=dt)]
l = test_neighborhood_iterator_oob(x, [1, 1], NEIGH_MODE['zero'],
[-1, 2], NEIGH_MODE['zero'])
assert_array_equal(l, r)
# Stacking mirror on top of zero, first neighborhood strictly inside the
# array
x = np.array([1, 2, 3], dtype=dt)
r = [np.array([1, 2, 3, 3], dtype=dt)]
l = test_neighborhood_iterator_oob(x, [1, 1], NEIGH_MODE['zero'],
[-1, 2], NEIGH_MODE['mirror'])
assert_array_equal(l, r)
# Stacking mirror on top of zero, first neighborhood strictly inside the
# array
x = np.array([1, 2, 3], dtype=dt)
r = [np.array([1, 2, 3, 1], dtype=dt)]
l = test_neighborhood_iterator_oob(x, [1, 1], NEIGH_MODE['zero'],
[-1, 2], NEIGH_MODE['circular'])
assert_array_equal(l, r)
class TestWarnings(object):
def test_complex_warning(self):
x = np.array([1, 2])
y = np.array([1-2j, 1+2j])
with warnings.catch_warnings():
warnings.simplefilter("error", np.ComplexWarning)
assert_raises(np.ComplexWarning, x.__setitem__, slice(None), y)
assert_equal(x, [1, 2])
class TestMinScalarType(object):
def test_usigned_shortshort(self):
dt = np.min_scalar_type(2**8-1)
wanted = np.dtype('uint8')
assert_equal(wanted, dt)
def test_usigned_short(self):
dt = np.min_scalar_type(2**16-1)
wanted = np.dtype('uint16')
assert_equal(wanted, dt)
def test_usigned_int(self):
dt = np.min_scalar_type(2**32-1)
wanted = np.dtype('uint32')
assert_equal(wanted, dt)
def test_usigned_longlong(self):
dt = np.min_scalar_type(2**63-1)
wanted = np.dtype('uint64')
assert_equal(wanted, dt)
def test_object(self):
dt = np.min_scalar_type(2**64)
wanted = np.dtype('O')
assert_equal(wanted, dt)
if sys.version_info[:2] == (2, 6):
from numpy.core.multiarray import memorysimpleview as memoryview
from numpy.core._internal import _dtype_from_pep3118
class TestPEP3118Dtype(object):
def _check(self, spec, wanted):
dt = np.dtype(wanted)
if isinstance(wanted, list) and isinstance(wanted[-1], tuple):
if wanted[-1][0] == '':
names = list(dt.names)
names[-1] = ''
dt.names = tuple(names)
assert_equal(_dtype_from_pep3118(spec), dt,
err_msg="spec %r != dtype %r" % (spec, wanted))
def test_native_padding(self):
align = np.dtype('i').alignment
for j in range(8):
if j == 0:
s = 'bi'
else:
s = 'b%dxi' % j
self._check('@'+s, {'f0': ('i1', 0),
'f1': ('i', align*(1 + j//align))})
self._check('='+s, {'f0': ('i1', 0),
'f1': ('i', 1+j)})
def test_native_padding_2(self):
# Native padding should work also for structs and sub-arrays
self._check('x3T{xi}', {'f0': (({'f0': ('i', 4)}, (3,)), 4)})
self._check('^x3T{xi}', {'f0': (({'f0': ('i', 1)}, (3,)), 1)})
def test_trailing_padding(self):
# Trailing padding should be included, *and*, the item size
# should match the alignment if in aligned mode
align = np.dtype('i').alignment
def VV(n):
return 'V%d' % (align*(1 + (n-1)//align))
self._check('ix', [('f0', 'i'), ('', VV(1))])
self._check('ixx', [('f0', 'i'), ('', VV(2))])
self._check('ixxx', [('f0', 'i'), ('', VV(3))])
self._check('ixxxx', [('f0', 'i'), ('', VV(4))])
self._check('i7x', [('f0', 'i'), ('', VV(7))])
self._check('^ix', [('f0', 'i'), ('', 'V1')])
self._check('^ixx', [('f0', 'i'), ('', 'V2')])
self._check('^ixxx', [('f0', 'i'), ('', 'V3')])
self._check('^ixxxx', [('f0', 'i'), ('', 'V4')])
self._check('^i7x', [('f0', 'i'), ('', 'V7')])
def test_native_padding_3(self):
dt = np.dtype(
[('a', 'b'), ('b', 'i'),
('sub', np.dtype('b,i')), ('c', 'i')],
align=True)
self._check("T{b:a:xxxi:b:T{b:f0:=i:f1:}:sub:xxxi:c:}", dt)
dt = np.dtype(
[('a', 'b'), ('b', 'i'), ('c', 'b'), ('d', 'b'),
('e', 'b'), ('sub', np.dtype('b,i', align=True))])
self._check("T{b:a:=i:b:b:c:b:d:b:e:T{b:f0:xxxi:f1:}:sub:}", dt)
def test_padding_with_array_inside_struct(self):
dt = np.dtype(
[('a', 'b'), ('b', 'i'), ('c', 'b', (3,)),
('d', 'i')],
align=True)
self._check("T{b:a:xxxi:b:3b:c:xi:d:}", dt)
def test_byteorder_inside_struct(self):
# The byte order after @T{=i} should be '=', not '@'.
# Check this by noting the absence of native alignment.
self._check('@T{^i}xi', {'f0': ({'f0': ('i', 0)}, 0),
'f1': ('i', 5)})
def test_intra_padding(self):
# Natively aligned sub-arrays may require some internal padding
align = np.dtype('i').alignment
def VV(n):
return 'V%d' % (align*(1 + (n-1)//align))
self._check('(3)T{ix}', ({'f0': ('i', 0), '': (VV(1), 4)}, (3,)))
class TestNewBufferProtocol(object):
def _check_roundtrip(self, obj):
obj = np.asarray(obj)
x = memoryview(obj)
y = np.asarray(x)
y2 = np.array(x)
assert_(not y.flags.owndata)
assert_(y2.flags.owndata)
assert_equal(y.dtype, obj.dtype)
assert_equal(y.shape, obj.shape)
assert_array_equal(obj, y)
assert_equal(y2.dtype, obj.dtype)
assert_equal(y2.shape, obj.shape)
assert_array_equal(obj, y2)
def test_roundtrip(self):
x = np.array([1, 2, 3, 4, 5], dtype='i4')
self._check_roundtrip(x)
x = np.array([[1, 2], [3, 4]], dtype=np.float64)
self._check_roundtrip(x)
x = np.zeros((3, 3, 3), dtype=np.float32)[:, 0,:]
self._check_roundtrip(x)
dt = [('a', 'b'),
('b', 'h'),
('c', 'i'),
('d', 'l'),
('dx', 'q'),
('e', 'B'),
('f', 'H'),
('g', 'I'),
('h', 'L'),
('hx', 'Q'),
('i', np.single),
('j', np.double),
('k', np.longdouble),
('ix', np.csingle),
('jx', np.cdouble),
('kx', np.clongdouble),
('l', 'S4'),
('m', 'U4'),
('n', 'V3'),
('o', '?'),
('p', np.half),
]
x = np.array(
[(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
asbytes('aaaa'), 'bbbb', asbytes('xxx'), True, 1.0)],
dtype=dt)
self._check_roundtrip(x)
x = np.array(([[1, 2], [3, 4]],), dtype=[('a', (int, (2, 2)))])
self._check_roundtrip(x)
x = np.array([1, 2, 3], dtype='>i2')
self._check_roundtrip(x)
x = np.array([1, 2, 3], dtype='<i2')
self._check_roundtrip(x)
x = np.array([1, 2, 3], dtype='>i4')
self._check_roundtrip(x)
x = np.array([1, 2, 3], dtype='<i4')
self._check_roundtrip(x)
# check long long can be represented as non-native
x = np.array([1, 2, 3], dtype='>q')
self._check_roundtrip(x)
# Native-only data types can be passed through the buffer interface
# only in native byte order
if sys.byteorder == 'little':
x = np.array([1, 2, 3], dtype='>g')
assert_raises(ValueError, self._check_roundtrip, x)
x = np.array([1, 2, 3], dtype='<g')
self._check_roundtrip(x)
else:
x = np.array([1, 2, 3], dtype='>g')
self._check_roundtrip(x)
x = np.array([1, 2, 3], dtype='<g')
assert_raises(ValueError, self._check_roundtrip, x)
def test_roundtrip_half(self):
half_list = [
1.0,
-2.0,
6.5504 * 10**4, # (max half precision)
2**-14, # ~= 6.10352 * 10**-5 (minimum positive normal)
2**-24, # ~= 5.96046 * 10**-8 (minimum strictly positive subnormal)
0.0,
-0.0,
float('+inf'),
float('-inf'),
0.333251953125, # ~= 1/3
]
x = np.array(half_list, dtype='>e')
self._check_roundtrip(x)
x = np.array(half_list, dtype='<e')
self._check_roundtrip(x)
def test_roundtrip_single_types(self):
for typ in np.typeDict.values():
dtype = np.dtype(typ)
if dtype.char in 'Mm':
# datetimes cannot be used in buffers
continue
if dtype.char == 'V':
# skip void
continue
x = np.zeros(4, dtype=dtype)
self._check_roundtrip(x)
if dtype.char not in 'qQgG':
dt = dtype.newbyteorder('<')
x = np.zeros(4, dtype=dt)
self._check_roundtrip(x)
dt = dtype.newbyteorder('>')
x = np.zeros(4, dtype=dt)
self._check_roundtrip(x)
def test_roundtrip_scalar(self):
# Issue #4015.
self._check_roundtrip(0)
def test_export_simple_1d(self):
x = np.array([1, 2, 3, 4, 5], dtype='i')
y = memoryview(x)
assert_equal(y.format, 'i')
assert_equal(y.shape, (5,))
assert_equal(y.ndim, 1)
assert_equal(y.strides, (4,))
assert_equal(y.suboffsets, EMPTY)
assert_equal(y.itemsize, 4)
def test_export_simple_nd(self):
x = np.array([[1, 2], [3, 4]], dtype=np.float64)
y = memoryview(x)
assert_equal(y.format, 'd')
assert_equal(y.shape, (2, 2))
assert_equal(y.ndim, 2)
assert_equal(y.strides, (16, 8))
assert_equal(y.suboffsets, EMPTY)
assert_equal(y.itemsize, 8)
def test_export_discontiguous(self):
x = np.zeros((3, 3, 3), dtype=np.float32)[:, 0,:]
y = memoryview(x)
assert_equal(y.format, 'f')
assert_equal(y.shape, (3, 3))
assert_equal(y.ndim, 2)
assert_equal(y.strides, (36, 4))
assert_equal(y.suboffsets, EMPTY)
assert_equal(y.itemsize, 4)
def test_export_record(self):
dt = [('a', 'b'),
('b', 'h'),
('c', 'i'),
('d', 'l'),
('dx', 'q'),
('e', 'B'),
('f', 'H'),
('g', 'I'),
('h', 'L'),
('hx', 'Q'),
('i', np.single),
('j', np.double),
('k', np.longdouble),
('ix', np.csingle),
('jx', np.cdouble),
('kx', np.clongdouble),
('l', 'S4'),
('m', 'U4'),
('n', 'V3'),
('o', '?'),
('p', np.half),
]
x = np.array(
[(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
asbytes('aaaa'), 'bbbb', asbytes(' '), True, 1.0)],
dtype=dt)
y = memoryview(x)
assert_equal(y.shape, (1,))
assert_equal(y.ndim, 1)
assert_equal(y.suboffsets, EMPTY)
sz = sum([dtype(b).itemsize for a, b in dt])
if dtype('l').itemsize == 4:
assert_equal(y.format, 'T{b:a:=h:b:i:c:l:d:q:dx:B:e:@H:f:=I:g:L:h:Q:hx:f:i:d:j:^g:k:=Zf:ix:Zd:jx:^Zg:kx:4s:l:=4w:m:3x:n:?:o:@e:p:}')
else:
assert_equal(y.format, 'T{b:a:=h:b:i:c:q:d:q:dx:B:e:@H:f:=I:g:Q:h:Q:hx:f:i:d:j:^g:k:=Zf:ix:Zd:jx:^Zg:kx:4s:l:=4w:m:3x:n:?:o:@e:p:}')
# Cannot test if NPY_RELAXED_STRIDES_CHECKING changes the strides
if not (np.ones(1).strides[0] == np.iinfo(np.intp).max):
assert_equal(y.strides, (sz,))
assert_equal(y.itemsize, sz)
def test_export_subarray(self):
x = np.array(([[1, 2], [3, 4]],), dtype=[('a', ('i', (2, 2)))])
y = memoryview(x)
assert_equal(y.format, 'T{(2,2)i:a:}')
assert_equal(y.shape, EMPTY)
assert_equal(y.ndim, 0)
assert_equal(y.strides, EMPTY)
assert_equal(y.suboffsets, EMPTY)
assert_equal(y.itemsize, 16)
def test_export_endian(self):
x = np.array([1, 2, 3], dtype='>i')
y = memoryview(x)
if sys.byteorder == 'little':
assert_equal(y.format, '>i')
else:
assert_equal(y.format, 'i')
x = np.array([1, 2, 3], dtype='<i')
y = memoryview(x)
if sys.byteorder == 'little':
assert_equal(y.format, 'i')
else:
assert_equal(y.format, '<i')
def test_export_flags(self):
# Check SIMPLE flag, see also gh-3613 (exception should be BufferError)
assert_raises(ValueError, get_buffer_info, np.arange(5)[::2], ('SIMPLE',))
def test_padding(self):
for j in range(8):
x = np.array([(1,), (2,)], dtype={'f0': (int, j)})
self._check_roundtrip(x)
def test_reference_leak(self):
count_1 = sys.getrefcount(np.core._internal)
a = np.zeros(4)
b = memoryview(a)
c = np.asarray(b)
count_2 = sys.getrefcount(np.core._internal)
assert_equal(count_1, count_2)
def test_padded_struct_array(self):
dt1 = np.dtype(
[('a', 'b'), ('b', 'i'), ('sub', np.dtype('b,i')), ('c', 'i')],
align=True)
x1 = np.arange(dt1.itemsize, dtype=np.int8).view(dt1)
self._check_roundtrip(x1)
dt2 = np.dtype(
[('a', 'b'), ('b', 'i'), ('c', 'b', (3,)), ('d', 'i')],
align=True)
x2 = np.arange(dt2.itemsize, dtype=np.int8).view(dt2)
self._check_roundtrip(x2)
dt3 = np.dtype(
[('a', 'b'), ('b', 'i'), ('c', 'b'), ('d', 'b'),
('e', 'b'), ('sub', np.dtype('b,i', align=True))])
x3 = np.arange(dt3.itemsize, dtype=np.int8).view(dt3)
self._check_roundtrip(x3)
def test_relaxed_strides(self):
# Test that relaxed strides are converted to non-relaxed
c = np.ones((1, 10, 10), dtype='i8')
# Check for NPY_RELAXED_STRIDES_CHECKING:
if np.ones((10, 1), order="C").flags.f_contiguous:
c.strides = (-1, 80, 8)
assert memoryview(c).strides == (800, 80, 8)
# Writing C-contiguous data to a BytesIO buffer should work
fd = io.BytesIO()
fd.write(c.data)
fortran = c.T
assert memoryview(fortran).strides == (8, 80, 800)
arr = np.ones((1, 10))
if arr.flags.f_contiguous:
shape, strides = get_buffer_info(arr, ['F_CONTIGUOUS'])
assert_(strides[0] == 8)
arr = np.ones((10, 1), order='F')
shape, strides = get_buffer_info(arr, ['C_CONTIGUOUS'])
assert_(strides[-1] == 8)
class TestArrayAttributeDeletion(object):
def test_multiarray_writable_attributes_deletion(self):
"""ticket #2046, should not seqfault, raise AttributeError"""
a = np.ones(2)
attr = ['shape', 'strides', 'data', 'dtype', 'real', 'imag', 'flat']
for s in attr:
assert_raises(AttributeError, delattr, a, s)
def test_multiarray_not_writable_attributes_deletion(self):
a = np.ones(2)
attr = ["ndim", "flags", "itemsize", "size", "nbytes", "base",
"ctypes", "T", "__array_interface__", "__array_struct__",
"__array_priority__", "__array_finalize__"]
for s in attr:
assert_raises(AttributeError, delattr, a, s)
def test_multiarray_flags_writable_attribute_deletion(self):
a = np.ones(2).flags
attr = ['updateifcopy', 'aligned', 'writeable']
for s in attr:
assert_raises(AttributeError, delattr, a, s)
def test_multiarray_flags_not_writable_attribute_deletion(self):
a = np.ones(2).flags
attr = ["contiguous", "c_contiguous", "f_contiguous", "fortran",
"owndata", "fnc", "forc", "behaved", "carray", "farray",
"num"]
for s in attr:
assert_raises(AttributeError, delattr, a, s)
def test_array_interface():
# Test scalar coercion within the array interface
class Foo(object):
def __init__(self, value):
self.value = value
self.iface = {'typestr' : '=f8'}
def __float__(self):
return float(self.value)
@property
def __array_interface__(self):
return self.iface
f = Foo(0.5)
assert_equal(np.array(f), 0.5)
assert_equal(np.array([f]), [0.5])
assert_equal(np.array([f, f]), [0.5, 0.5])
assert_equal(np.array(f).dtype, np.dtype('=f8'))
# Test various shape definitions
f.iface['shape'] = ()
assert_equal(np.array(f), 0.5)
f.iface['shape'] = None
assert_raises(TypeError, np.array, f)
f.iface['shape'] = (1, 1)
assert_equal(np.array(f), [[0.5]])
f.iface['shape'] = (2,)
assert_raises(ValueError, np.array, f)
# test scalar with no shape
class ArrayLike(object):
array = np.array(1)
__array_interface__ = array.__array_interface__
assert_equal(np.array(ArrayLike()), 1)
def test_flat_element_deletion():
it = np.ones(3).flat
try:
del it[1]
del it[1:2]
except TypeError:
pass
except:
raise AssertionError
def test_scalar_element_deletion():
a = np.zeros(2, dtype=[('x', 'int'), ('y', 'int')])
assert_raises(ValueError, a[0].__delitem__, 'x')
class TestMemEventHook(TestCase):
def test_mem_seteventhook(self):
# The actual tests are within the C code in
# multiarray/multiarray_tests.c.src
test_pydatamem_seteventhook_start()
# force an allocation and free of a numpy array
# needs to be larger then limit of small memory cacher in ctors.c
a = np.zeros(1000)
del a
test_pydatamem_seteventhook_end()
class TestMapIter(TestCase):
def test_mapiter(self):
# The actual tests are within the C code in
# multiarray/multiarray_tests.c.src
a = arange(12).reshape((3, 4)).astype(float)
index = ([1, 1, 2, 0],
[0, 0, 2, 3])
vals = [50, 50, 30, 16]
test_inplace_increment(a, index, vals)
assert_equal(a, [[ 0., 1., 2., 19.,],
[ 104., 5., 6., 7.,],
[ 8., 9., 40., 11.,]])
b = arange(6).astype(float)
index = (array([1, 2, 0]),)
vals = [50, 4, 100.1]
test_inplace_increment(b, index, vals)
assert_equal(b, [ 100.1, 51., 6., 3., 4., 5. ])
class TestAsCArray(TestCase):
def test_1darray(self):
array = np.arange(24, dtype=np.double)
from_c = test_as_c_array(array, 3)
assert_equal(array[3], from_c)
def test_2darray(self):
array = np.arange(24, dtype=np.double).reshape(3, 8)
from_c = test_as_c_array(array, 2, 4)
assert_equal(array[2, 4], from_c)
def test_3darray(self):
array = np.arange(24, dtype=np.double).reshape(2, 3, 4)
from_c = test_as_c_array(array, 1, 2, 3)
assert_equal(array[1, 2, 3], from_c)
class PriorityNdarray():
__array_priority__ = 1000
def __init__(self, array):
self.array = array
def __lt__(self, array):
if isinstance(array, PriorityNdarray):
array = array.array
return PriorityNdarray(self.array < array)
def __gt__(self, array):
if isinstance(array, PriorityNdarray):
array = array.array
return PriorityNdarray(self.array > array)
def __le__(self, array):
if isinstance(array, PriorityNdarray):
array = array.array
return PriorityNdarray(self.array <= array)
def __ge__(self, array):
if isinstance(array, PriorityNdarray):
array = array.array
return PriorityNdarray(self.array >= array)
def __eq__(self, array):
if isinstance(array, PriorityNdarray):
array = array.array
return PriorityNdarray(self.array == array)
def __ne__(self, array):
if isinstance(array, PriorityNdarray):
array = array.array
return PriorityNdarray(self.array != array)
class TestArrayPriority(TestCase):
def test_lt(self):
l = np.asarray([0., -1., 1.], dtype=dtype)
r = np.asarray([0., 1., -1.], dtype=dtype)
lp = PriorityNdarray(l)
rp = PriorityNdarray(r)
res1 = l < r
res2 = l < rp
res3 = lp < r
res4 = lp < rp
assert_array_equal(res1, res2.array)
assert_array_equal(res1, res3.array)
assert_array_equal(res1, res4.array)
assert_(isinstance(res1, np.ndarray))
assert_(isinstance(res2, PriorityNdarray))
assert_(isinstance(res3, PriorityNdarray))
assert_(isinstance(res4, PriorityNdarray))
def test_gt(self):
l = np.asarray([0., -1., 1.], dtype=dtype)
r = np.asarray([0., 1., -1.], dtype=dtype)
lp = PriorityNdarray(l)
rp = PriorityNdarray(r)
res1 = l > r
res2 = l > rp
res3 = lp > r
res4 = lp > rp
assert_array_equal(res1, res2.array)
assert_array_equal(res1, res3.array)
assert_array_equal(res1, res4.array)
assert_(isinstance(res1, np.ndarray))
assert_(isinstance(res2, PriorityNdarray))
assert_(isinstance(res3, PriorityNdarray))
assert_(isinstance(res4, PriorityNdarray))
def test_le(self):
l = np.asarray([0., -1., 1.], dtype=dtype)
r = np.asarray([0., 1., -1.], dtype=dtype)
lp = PriorityNdarray(l)
rp = PriorityNdarray(r)
res1 = l <= r
res2 = l <= rp
res3 = lp <= r
res4 = lp <= rp
assert_array_equal(res1, res2.array)
assert_array_equal(res1, res3.array)
assert_array_equal(res1, res4.array)
assert_(isinstance(res1, np.ndarray))
assert_(isinstance(res2, PriorityNdarray))
assert_(isinstance(res3, PriorityNdarray))
assert_(isinstance(res4, PriorityNdarray))
def test_ge(self):
l = np.asarray([0., -1., 1.], dtype=dtype)
r = np.asarray([0., 1., -1.], dtype=dtype)
lp = PriorityNdarray(l)
rp = PriorityNdarray(r)
res1 = l >= r
res2 = l >= rp
res3 = lp >= r
res4 = lp >= rp
assert_array_equal(res1, res2.array)
assert_array_equal(res1, res3.array)
assert_array_equal(res1, res4.array)
assert_(isinstance(res1, np.ndarray))
assert_(isinstance(res2, PriorityNdarray))
assert_(isinstance(res3, PriorityNdarray))
assert_(isinstance(res4, PriorityNdarray))
def test_eq(self):
l = np.asarray([0., -1., 1.], dtype=dtype)
r = np.asarray([0., 1., -1.], dtype=dtype)
lp = PriorityNdarray(l)
rp = PriorityNdarray(r)
res1 = l == r
res2 = l == rp
res3 = lp == r
res4 = lp == rp
assert_array_equal(res1, res2.array)
assert_array_equal(res1, res3.array)
assert_array_equal(res1, res4.array)
assert_(isinstance(res1, np.ndarray))
assert_(isinstance(res2, PriorityNdarray))
assert_(isinstance(res3, PriorityNdarray))
assert_(isinstance(res4, PriorityNdarray))
def test_ne(self):
l = np.asarray([0., -1., 1.], dtype=dtype)
r = np.asarray([0., 1., -1.], dtype=dtype)
lp = PriorityNdarray(l)
rp = PriorityNdarray(r)
res1 = l != r
res2 = l != rp
res3 = lp != r
res4 = lp != rp
assert_array_equal(res1, res2.array)
assert_array_equal(res1, res3.array)
assert_array_equal(res1, res4.array)
assert_(isinstance(res1, np.ndarray))
assert_(isinstance(res2, PriorityNdarray))
assert_(isinstance(res3, PriorityNdarray))
assert_(isinstance(res4, PriorityNdarray))
class TestConversion(TestCase):
def test_array_scalar_relational_operation(self):
#All integer
for dt1 in np.typecodes['AllInteger']:
assert_(1 > np.array(0, dtype=dt1), "type %s failed" % (dt1,))
assert_(not 1 < np.array(0, dtype=dt1), "type %s failed" % (dt1,))
for dt2 in np.typecodes['AllInteger']:
assert_(np.array(1, dtype=dt1) > np.array(0, dtype=dt2),
"type %s and %s failed" % (dt1, dt2))
assert_(not np.array(1, dtype=dt1) < np.array(0, dtype=dt2),
"type %s and %s failed" % (dt1, dt2))
#Unsigned integers
for dt1 in 'BHILQP':
assert_(-1 < np.array(1, dtype=dt1), "type %s failed" % (dt1,))
assert_(not -1 > np.array(1, dtype=dt1), "type %s failed" % (dt1,))
assert_(-1 != np.array(1, dtype=dt1), "type %s failed" % (dt1,))
#unsigned vs signed
for dt2 in 'bhilqp':
assert_(np.array(1, dtype=dt1) > np.array(-1, dtype=dt2),
"type %s and %s failed" % (dt1, dt2))
assert_(not np.array(1, dtype=dt1) < np.array(-1, dtype=dt2),
"type %s and %s failed" % (dt1, dt2))
assert_(np.array(1, dtype=dt1) != np.array(-1, dtype=dt2),
"type %s and %s failed" % (dt1, dt2))
#Signed integers and floats
for dt1 in 'bhlqp' + np.typecodes['Float']:
assert_(1 > np.array(-1, dtype=dt1), "type %s failed" % (dt1,))
assert_(not 1 < np.array(-1, dtype=dt1), "type %s failed" % (dt1,))
assert_(-1 == np.array(-1, dtype=dt1), "type %s failed" % (dt1,))
for dt2 in 'bhlqp' + np.typecodes['Float']:
assert_(np.array(1, dtype=dt1) > np.array(-1, dtype=dt2),
"type %s and %s failed" % (dt1, dt2))
assert_(not np.array(1, dtype=dt1) < np.array(-1, dtype=dt2),
"type %s and %s failed" % (dt1, dt2))
assert_(np.array(-1, dtype=dt1) == np.array(-1, dtype=dt2),
"type %s and %s failed" % (dt1, dt2))
class TestWhere(TestCase):
def test_basic(self):
dts = [np.bool, np.int16, np.int32, np.int64, np.double, np.complex128,
np.longdouble, np.clongdouble]
for dt in dts:
c = np.ones(53, dtype=np.bool)
assert_equal(np.where( c, dt(0), dt(1)), dt(0))
assert_equal(np.where(~c, dt(0), dt(1)), dt(1))
assert_equal(np.where(True, dt(0), dt(1)), dt(0))
assert_equal(np.where(False, dt(0), dt(1)), dt(1))
d = np.ones_like(c).astype(dt)
e = np.zeros_like(d)
r = d.astype(dt)
c[7] = False
r[7] = e[7]
assert_equal(np.where(c, e, e), e)
assert_equal(np.where(c, d, e), r)
assert_equal(np.where(c, d, e[0]), r)
assert_equal(np.where(c, d[0], e), r)
assert_equal(np.where(c[::2], d[::2], e[::2]), r[::2])
assert_equal(np.where(c[1::2], d[1::2], e[1::2]), r[1::2])
assert_equal(np.where(c[::3], d[::3], e[::3]), r[::3])
assert_equal(np.where(c[1::3], d[1::3], e[1::3]), r[1::3])
assert_equal(np.where(c[::-2], d[::-2], e[::-2]), r[::-2])
assert_equal(np.where(c[::-3], d[::-3], e[::-3]), r[::-3])
assert_equal(np.where(c[1::-3], d[1::-3], e[1::-3]), r[1::-3])
def test_exotic(self):
# object
assert_array_equal(np.where(True, None, None), np.array(None))
# zero sized
m = np.array([], dtype=bool).reshape(0, 3)
b = np.array([], dtype=np.float64).reshape(0, 3)
assert_array_equal(np.where(m, 0, b), np.array([]).reshape(0, 3))
# object cast
d = np.array([-1.34, -0.16, -0.54, -0.31, -0.08, -0.95, 0.000, 0.313,
0.547, -0.18, 0.876, 0.236, 1.969, 0.310, 0.699, 1.013,
1.267, 0.229, -1.39, 0.487])
nan = float('NaN')
e = np.array(['5z', '0l', nan, 'Wz', nan, nan, 'Xq', 'cs', nan, nan,
'QN', nan, nan, 'Fd', nan, nan, 'kp', nan, '36', 'i1'],
dtype=object);
m = np.array([0,0,1,0,1,1,0,0,1,1,0,1,1,0,1,1,0,1,0,0], dtype=bool)
r = e[:]
r[np.where(m)] = d[np.where(m)]
assert_array_equal(np.where(m, d, e), r)
r = e[:]
r[np.where(~m)] = d[np.where(~m)]
assert_array_equal(np.where(m, e, d), r)
assert_array_equal(np.where(m, e, e), e)
# minimal dtype result with NaN scalar (e.g required by pandas)
d = np.array([1., 2.], dtype=np.float32)
e = float('NaN')
assert_equal(np.where(True, d, e).dtype, np.float32)
e = float('Infinity')
assert_equal(np.where(True, d, e).dtype, np.float32)
e = float('-Infinity')
assert_equal(np.where(True, d, e).dtype, np.float32)
# also check upcast
e = float(1e150)
assert_equal(np.where(True, d, e).dtype, np.float64)
def test_ndim(self):
c = [True, False]
a = np.zeros((2, 25))
b = np.ones((2, 25))
r = np.where(np.array(c)[:,np.newaxis], a, b)
assert_array_equal(r[0], a[0])
assert_array_equal(r[1], b[0])
a = a.T
b = b.T
r = np.where(c, a, b)
assert_array_equal(r[:,0], a[:,0])
assert_array_equal(r[:,1], b[:,0])
def test_dtype_mix(self):
c = np.array([False, True, False, False, False, False, True, False,
False, False, True, False])
a = np.uint32(1)
b = np.array([5., 0., 3., 2., -1., -4., 0., -10., 10., 1., 0., 3.],
dtype=np.float64)
r = np.array([5., 1., 3., 2., -1., -4., 1., -10., 10., 1., 1., 3.],
dtype=np.float64)
assert_equal(np.where(c, a, b), r)
a = a.astype(np.float32)
b = b.astype(np.int64)
assert_equal(np.where(c, a, b), r)
# non bool mask
c = c.astype(np.int)
c[c != 0] = 34242324
assert_equal(np.where(c, a, b), r)
# invert
tmpmask = c != 0
c[c == 0] = 41247212
c[tmpmask] = 0
assert_equal(np.where(c, b, a), r)
def test_foreign(self):
c = np.array([False, True, False, False, False, False, True, False,
False, False, True, False])
r = np.array([5., 1., 3., 2., -1., -4., 1., -10., 10., 1., 1., 3.],
dtype=np.float64)
a = np.ones(1, dtype='>i4')
b = np.array([5., 0., 3., 2., -1., -4., 0., -10., 10., 1., 0., 3.],
dtype=np.float64)
assert_equal(np.where(c, a, b), r)
b = b.astype('>f8')
assert_equal(np.where(c, a, b), r)
a = a.astype('<i4')
assert_equal(np.where(c, a, b), r)
c = c.astype('>i4')
assert_equal(np.where(c, a, b), r)
def test_error(self):
c = [True, True]
a = np.ones((4, 5))
b = np.ones((5, 5))
assert_raises(ValueError, np.where, c, a, a)
assert_raises(ValueError, np.where, c[0], a, b)
def test_string(self):
# gh-4778 check strings are properly filled with nulls
a = np.array("abc")
b = np.array("x" * 753)
assert_equal(np.where(True, a, b), "abc")
assert_equal(np.where(False, b, a), "abc")
# check native datatype sized strings
a = np.array("abcd")
b = np.array("x" * 8)
assert_equal(np.where(True, a, b), "abcd")
assert_equal(np.where(False, b, a), "abcd")
class TestSizeOf(TestCase):
def test_empty_array(self):
x = np.array([])
assert_(sys.getsizeof(x) > 0)
def check_array(self, dtype):
elem_size = dtype(0).itemsize
for length in [10, 50, 100, 500]:
x = np.arange(length, dtype=dtype)
assert_(sys.getsizeof(x) > length * elem_size)
def test_array_int32(self):
self.check_array(np.int32)
def test_array_int64(self):
self.check_array(np.int64)
def test_array_float32(self):
self.check_array(np.float32)
def test_array_float64(self):
self.check_array(np.float64)
def test_view(self):
d = np.ones(100)
assert_(sys.getsizeof(d[...]) < sys.getsizeof(d))
def test_reshape(self):
d = np.ones(100)
assert_(sys.getsizeof(d) < sys.getsizeof(d.reshape(100, 1, 1).copy()))
def test_resize(self):
d = np.ones(100)
old = sys.getsizeof(d)
d.resize(50)
assert_(old > sys.getsizeof(d))
d.resize(150)
assert_(old < sys.getsizeof(d))
def test_error(self):
d = np.ones(100)
assert_raises(TypeError, d.__sizeof__, "a")
class TestHashing(TestCase):
def test_collections_hashable(self):
x = np.array([])
self.assertFalse(isinstance(x, collections.Hashable))
from numpy.core._internal import _view_is_safe
class TestObjViewSafetyFuncs(TestCase):
def test_view_safety(self):
psize = dtype('p').itemsize
# creates dtype but with extra character code - for missing 'p' fields
def mtype(s):
n, offset, fields = 0, 0, []
for c in s.split(','): #subarrays won't work
if c != '-':
fields.append(('f{0}'.format(n), c, offset))
n += 1
offset += dtype(c).itemsize if c != '-' else psize
names, formats, offsets = zip(*fields)
return dtype({'names': names, 'formats': formats,
'offsets': offsets, 'itemsize': offset})
# test nonequal itemsizes with objects:
# these should succeed:
_view_is_safe(dtype('O,p,O,p'), dtype('O,p,O,p,O,p'))
_view_is_safe(dtype('O,O'), dtype('O,O,O'))
# these should fail:
assert_raises(TypeError, _view_is_safe, dtype('O,O,p'), dtype('O,O'))
assert_raises(TypeError, _view_is_safe, dtype('O,O,p'), dtype('O,p'))
assert_raises(TypeError, _view_is_safe, dtype('O,O,p'), dtype('p,O'))
# test nonequal itemsizes with missing fields:
# these should succeed:
_view_is_safe(mtype('-,p,-,p'), mtype('-,p,-,p,-,p'))
_view_is_safe(dtype('p,p'), dtype('p,p,p'))
# these should fail:
assert_raises(TypeError, _view_is_safe, mtype('p,p,-'), mtype('p,p'))
assert_raises(TypeError, _view_is_safe, mtype('p,p,-'), mtype('p,-'))
assert_raises(TypeError, _view_is_safe, mtype('p,p,-'), mtype('-,p'))
# scans through positions at which we can view a type
def scanView(d1, otype):
goodpos = []
for shift in range(d1.itemsize - dtype(otype).itemsize+1):
d2 = dtype({'names': ['f0'], 'formats': [otype],
'offsets': [shift], 'itemsize': d1.itemsize})
try:
_view_is_safe(d1, d2)
except TypeError:
pass
else:
goodpos.append(shift)
return goodpos
# test partial overlap with object field
assert_equal(scanView(dtype('p,O,p,p,O,O'), 'p'),
[0] + list(range(2*psize, 3*psize+1)))
assert_equal(scanView(dtype('p,O,p,p,O,O'), 'O'),
[psize, 4*psize, 5*psize])
# test partial overlap with missing field
assert_equal(scanView(mtype('p,-,p,p,-,-'), 'p'),
[0] + list(range(2*psize, 3*psize+1)))
# test nested structures with objects:
nestedO = dtype([('f0', 'p'), ('f1', 'p,O,p')])
assert_equal(scanView(nestedO, 'p'), list(range(psize+1)) + [3*psize])
assert_equal(scanView(nestedO, 'O'), [2*psize])
# test nested structures with missing fields:
nestedM = dtype([('f0', 'p'), ('f1', mtype('p,-,p'))])
assert_equal(scanView(nestedM, 'p'), list(range(psize+1)) + [3*psize])
# test subarrays with objects
subarrayO = dtype('p,(2,3)O,p')
assert_equal(scanView(subarrayO, 'p'), [0, 7*psize])
assert_equal(scanView(subarrayO, 'O'),
list(range(psize, 6*psize+1, psize)))
#test dtype with overlapping fields
overlapped = dtype({'names': ['f0', 'f1', 'f2', 'f3'],
'formats': ['p', 'p', 'p', 'p'],
'offsets': [0, 1, 3*psize-1, 3*psize],
'itemsize': 4*psize})
assert_equal(scanView(overlapped, 'p'), [0, 1, 3*psize-1, 3*psize])
class TestArrayPriority(TestCase):
# This will go away when __array_priority__ is settled, meanwhile
# it serves to check unintended changes.
op = operator
binary_ops = [
op.pow, op.add, op.sub, op.mul, op.floordiv, op.truediv, op.mod,
op.and_, op.or_, op.xor, op.lshift, op.rshift, op.mod, op.gt,
op.ge, op.lt, op.le, op.ne, op.eq
]
if sys.version_info[0] < 3:
binary_ops.append(op.div)
class Foo(np.ndarray):
__array_priority__ = 100.
def __new__(cls, *args, **kwargs):
return np.array(*args, **kwargs).view(cls)
class Bar(np.ndarray):
__array_priority__ = 101.
def __new__(cls, *args, **kwargs):
return np.array(*args, **kwargs).view(cls)
class Other(object):
__array_priority__ = 1000.
def _all(self, other):
return self.__class__()
__add__ = __radd__ = _all
__sub__ = __rsub__ = _all
__mul__ = __rmul__ = _all
__pow__ = __rpow__ = _all
__div__ = __rdiv__ = _all
__mod__ = __rmod__ = _all
__truediv__ = __rtruediv__ = _all
__floordiv__ = __rfloordiv__ = _all
__and__ = __rand__ = _all
__xor__ = __rxor__ = _all
__or__ = __ror__ = _all
__lshift__ = __rlshift__ = _all
__rshift__ = __rrshift__ = _all
__eq__ = _all
__ne__ = _all
__gt__ = _all
__ge__ = _all
__lt__ = _all
__le__ = _all
def test_ndarray_subclass(self):
a = np.array([1, 2])
b = self.Bar([1, 2])
for f in self.binary_ops:
msg = repr(f)
assert_(isinstance(f(a, b), self.Bar), msg)
assert_(isinstance(f(b, a), self.Bar), msg)
def test_ndarray_other(self):
a = np.array([1, 2])
b = self.Other()
for f in self.binary_ops:
msg = repr(f)
assert_(isinstance(f(a, b), self.Other), msg)
assert_(isinstance(f(b, a), self.Other), msg)
def test_subclass_subclass(self):
a = self.Foo([1, 2])
b = self.Bar([1, 2])
for f in self.binary_ops:
msg = repr(f)
assert_(isinstance(f(a, b), self.Bar), msg)
assert_(isinstance(f(b, a), self.Bar), msg)
def test_subclass_other(self):
a = self.Foo([1, 2])
b = self.Other()
for f in self.binary_ops:
msg = repr(f)
assert_(isinstance(f(a, b), self.Other), msg)
assert_(isinstance(f(b, a), self.Other), msg)
class TestBytestringArrayNonzero(TestCase):
def test_empty_bstring_array_is_falsey(self):
self.assertFalse(np.array([''], dtype=np.str))
def test_whitespace_bstring_array_is_falsey(self):
a = np.array(['spam'], dtype=np.str)
a[0] = ' \0\0'
self.assertFalse(a)
def test_all_null_bstring_array_is_falsey(self):
a = np.array(['spam'], dtype=np.str)
a[0] = '\0\0\0\0'
self.assertFalse(a)
def test_null_inside_bstring_array_is_truthy(self):
a = np.array(['spam'], dtype=np.str)
a[0] = ' \0 \0'
self.assertTrue(a)
class TestUnicodeArrayNonzero(TestCase):
def test_empty_ustring_array_is_falsey(self):
self.assertFalse(np.array([''], dtype=np.unicode))
def test_whitespace_ustring_array_is_falsey(self):
a = np.array(['eggs'], dtype=np.unicode)
a[0] = ' \0\0'
self.assertFalse(a)
def test_all_null_ustring_array_is_falsey(self):
a = np.array(['eggs'], dtype=np.unicode)
a[0] = '\0\0\0\0'
self.assertFalse(a)
def test_null_inside_ustring_array_is_truthy(self):
a = np.array(['eggs'], dtype=np.unicode)
a[0] = ' \0 \0'
self.assertTrue(a)
if __name__ == "__main__":
run_module_suite()
| bsd-3-clause |
qianfengzh/ML-source-code | algorithms/decisionTrees/treePlotter.py | 1 | 1851 | #-*-coding=utf-8-*-
#-----------------------
# Named: Decision Trees Plotter
# Created: 2016-07-10
# @Author: Qianfeng
#-----------------------
import matplotlib.pyplot as plt
decisionNode = dict(boxstyle='sawtooth', fc='0.8')
leafNode = dict(boxstyle='round4', fc='0.8')
arrow_args = dict(arrowstyle='<-')
def plotNode(nodeTxt, centerPt, parentPt, nodeType):
createPlot.ax1.annotate(nodeTxt, xy=parentPt, xycoords='axes fraction', \
xytext=centerPt, textcoords='axes fraction', va='center', ha='center', \
bbox=nodeType, arrowprops=arrow_args)
def createPlot():
fig = plt.figure(1, facecolor='white')
fig.clf()
createPlot.ax1 = plt.subplot(111, frameon=False)
plotNode(U'决策节点', (0.5, 0.1), (0.1, 0.5), decisionNode)
plotNode(U'叶节点', (0.8, 0.1), (0.3, 0.8), leafNode)
plt.show()
#-------------------------------------------------------------------
# 构造注解树
# 双递归，探测树深度
def getNumLeafs(myTree):
"""
递归获取叶节点总数
"""
numLeafs = 0
firstStr = myTree.keys()[0]
secondDict = myTree[firstStr]
for key in secondDict.keys():
if type(secondDict[key]).__name__ == 'dict':
numLeafs += getNumLeafs(secondDict[key])
else:
numLeafs += 1
return numLeafs
def getTreeDepth(myTree):
"""
递归获取树深度
"""
maxDepth = 0
firstStr = myTree.keys()[0]
secondDict = myTree[firstStr]
for key in secondDict.keys():
if type(secondDict[key]).__name__ == 'dict':
thisDepth = 1+ getTreeDepth(secondDict[key])
else:
thisDepth = 1
if thisDepth > maxDepth:
maxDepth = thisDepth
return maxDepth
def retrieveTree(i):
listOfTrees = [{'no surfacing': {0: 'no', 1: {'flippers': {0: 'no', 1: 'yes'}}}},\
{'no surfacing': {0: 'no', 1: {'flippers': {0: {'head': {0: 'no', 1: 'yes'}}, 1: 'no'}}}}
]
return listOfTrees[i]
| gpl-2.0 |
aewhatley/scikit-learn | examples/cluster/plot_segmentation_toy.py | 258 | 3336 | """
===========================================
Spectral clustering for image segmentation
===========================================
In this example, an image with connected circles is generated and
spectral clustering is used to separate the circles.
In these settings, the :ref:`spectral_clustering` approach solves the problem
know as 'normalized graph cuts': the image is seen as a graph of
connected voxels, and the spectral clustering algorithm amounts to
choosing graph cuts defining regions while minimizing the ratio of the
gradient along the cut, and the volume of the region.
As the algorithm tries to balance the volume (ie balance the region
sizes), if we take circles with different sizes, the segmentation fails.
In addition, as there is no useful information in the intensity of the image,
or its gradient, we choose to perform the spectral clustering on a graph
that is only weakly informed by the gradient. This is close to performing
a Voronoi partition of the graph.
In addition, we use the mask of the objects to restrict the graph to the
outline of the objects. In this example, we are interested in
separating the objects one from the other, and not from the background.
"""
print(__doc__)
# Authors: Emmanuelle Gouillart <[email protected]>
# Gael Varoquaux <[email protected]>
# License: BSD 3 clause
import numpy as np
import matplotlib.pyplot as plt
from sklearn.feature_extraction import image
from sklearn.cluster import spectral_clustering
###############################################################################
l = 100
x, y = np.indices((l, l))
center1 = (28, 24)
center2 = (40, 50)
center3 = (67, 58)
center4 = (24, 70)
radius1, radius2, radius3, radius4 = 16, 14, 15, 14
circle1 = (x - center1[0]) ** 2 + (y - center1[1]) ** 2 < radius1 ** 2
circle2 = (x - center2[0]) ** 2 + (y - center2[1]) ** 2 < radius2 ** 2
circle3 = (x - center3[0]) ** 2 + (y - center3[1]) ** 2 < radius3 ** 2
circle4 = (x - center4[0]) ** 2 + (y - center4[1]) ** 2 < radius4 ** 2
###############################################################################
# 4 circles
img = circle1 + circle2 + circle3 + circle4
mask = img.astype(bool)
img = img.astype(float)
img += 1 + 0.2 * np.random.randn(*img.shape)
# Convert the image into a graph with the value of the gradient on the
# edges.
graph = image.img_to_graph(img, mask=mask)
# Take a decreasing function of the gradient: we take it weakly
# dependent from the gradient the segmentation is close to a voronoi
graph.data = np.exp(-graph.data / graph.data.std())
# Force the solver to be arpack, since amg is numerically
# unstable on this example
labels = spectral_clustering(graph, n_clusters=4, eigen_solver='arpack')
label_im = -np.ones(mask.shape)
label_im[mask] = labels
plt.matshow(img)
plt.matshow(label_im)
###############################################################################
# 2 circles
img = circle1 + circle2
mask = img.astype(bool)
img = img.astype(float)
img += 1 + 0.2 * np.random.randn(*img.shape)
graph = image.img_to_graph(img, mask=mask)
graph.data = np.exp(-graph.data / graph.data.std())
labels = spectral_clustering(graph, n_clusters=2, eigen_solver='arpack')
label_im = -np.ones(mask.shape)
label_im[mask] = labels
plt.matshow(img)
plt.matshow(label_im)
plt.show()
| bsd-3-clause |
jangorecki/h2o-3 | h2o-py/h2o/model/metrics_base.py | 1 | 26162 | # -*- encoding: utf-8 -*-
"""
Regression model.
:copyright: (c) 2016 H2O.ai
:license: Apache License Version 2.0 (see LICENSE for details)
"""
from __future__ import absolute_import, division, print_function, unicode_literals
import imp
from h2o.model.confusion_matrix import ConfusionMatrix
from h2o.utils.backward_compatibility import backwards_compatible
from h2o.utils.compatibility import * # NOQA
from h2o.utils.typechecks import assert_is_type, assert_satisfies, numeric
class MetricsBase(backwards_compatible()):
"""
A parent class to house common metrics available for the various Metrics types.
The methods here are available across different model categories, and so appear here.
"""
def __init__(self, metric_json, on=None, algo=""):
super(MetricsBase, self).__init__()
# Yep, it's messed up...
if isinstance(metric_json, MetricsBase): metric_json = metric_json._metric_json
self._metric_json = metric_json
# train and valid and xval are not mutually exclusive -- could have a test. train and
# valid only make sense at model build time.
self._on_train = False
self._on_valid = False
self._on_xval = False
self._algo = algo
if on == "training_metrics":
self._on_train = True
elif on == "validation_metrics":
self._on_valid = True
elif on == "cross_validation_metrics":
self._on_xval = True
elif on is None:
pass
else:
raise ValueError("on expected to be train,valid,or xval. Got: " + str(on))
@classmethod
def make(cls, kvs):
"""Factory method to instantiate a MetricsBase object from the list of key-value pairs."""
return cls(metric_json=dict(kvs))
def __repr__(self):
# FIXME !!! __repr__ should never print anything, but return a string
self.show()
return ""
# TODO: convert to actual fields list
def __getitem__(self, key):
return self._metric_json.get(key)
@staticmethod
def _has(dictionary, key):
return key in dictionary and dictionary[key] is not None
def show(self):
"""
Display a short summary of the metrics.
:return: None
"""
metric_type = self._metric_json['__meta']['schema_type']
types_w_glm = ['ModelMetricsRegressionGLM', 'ModelMetricsBinomialGLM']
types_w_clustering = ['ModelMetricsClustering']
types_w_mult = ['ModelMetricsMultinomial']
types_w_bin = ['ModelMetricsBinomial', 'ModelMetricsBinomialGLM']
types_w_r2 = ['ModelMetricsRegressionGLM']
types_w_mean_residual_deviance = ['ModelMetricsRegressionGLM', 'ModelMetricsRegression']
types_w_mean_absolute_error = ['ModelMetricsRegressionGLM', 'ModelMetricsRegression']
types_w_logloss = types_w_bin + types_w_mult
types_w_dim = ["ModelMetricsGLRM"]
print()
print(metric_type + ": " + self._algo)
reported_on = "** Reported on {} data. **"
if self._on_train:
print(reported_on.format("train"))
elif self._on_valid:
print(reported_on.format("validation"))
elif self._on_xval:
print(reported_on.format("cross-validation"))
else:
print(reported_on.format("test"))
print()
print("MSE: " + str(self.mse()))
print("RMSE: " + str(self.rmse()))
if metric_type in types_w_mean_absolute_error:
print("MAE: " + str(self.mae()))
print("RMSLE: " + str(self.rmsle()))
if metric_type in types_w_r2:
print("R^2: " + str(self.r2()))
if metric_type in types_w_mean_residual_deviance:
print("Mean Residual Deviance: " + str(self.mean_residual_deviance()))
if metric_type in types_w_logloss:
print("LogLoss: " + str(self.logloss()))
if metric_type == 'ModelMetricsBinomial':
# second element for first threshold is the actual mean per class error
print("Mean Per-Class Error: %s" % self.mean_per_class_error()[0][1])
if metric_type == 'ModelMetricsMultinomial':
print("Mean Per-Class Error: " + str(self.mean_per_class_error()))
if metric_type in types_w_glm:
print("Null degrees of freedom: " + str(self.null_degrees_of_freedom()))
print("Residual degrees of freedom: " + str(self.residual_degrees_of_freedom()))
print("Null deviance: " + str(self.null_deviance()))
print("Residual deviance: " + str(self.residual_deviance()))
print("AIC: " + str(self.aic()))
if metric_type in types_w_bin:
print("AUC: " + str(self.auc()))
print("Gini: " + str(self.gini()))
self.confusion_matrix().show()
self._metric_json["max_criteria_and_metric_scores"].show()
if self.gains_lift():
print(self.gains_lift())
if metric_type in types_w_mult:
self.confusion_matrix().show()
self.hit_ratio_table().show()
if metric_type in types_w_clustering:
print("Total Within Cluster Sum of Square Error: " + str(self.tot_withinss()))
print("Total Sum of Square Error to Grand Mean: " + str(self.totss()))
print("Between Cluster Sum of Square Error: " + str(self.betweenss()))
self._metric_json['centroid_stats'].show()
if metric_type in types_w_dim:
print("Sum of Squared Error (Numeric): " + str(self.num_err()))
print("Misclassification Error (Categorical): " + str(self.cat_err()))
def r2(self):
"""The R^2 coefficient."""
return self._metric_json["r2"]
def logloss(self):
"""Log loss."""
return self._metric_json["logloss"]
def nobs(self):
"""
:return: Retrieve the number of observations.
"""
return self._metric_json["nobs"]
def mean_residual_deviance(self):
"""
:return: Retrieve the mean residual deviance for this set of metrics.
"""
return self._metric_json["mean_residual_deviance"]
def auc(self):
"""
:return: Retrieve the AUC for this set of metrics.
"""
return self._metric_json['AUC']
def aic(self):
"""
:return: Retrieve the AIC for this set of metrics.
"""
return self._metric_json['AIC']
def gini(self):
"""Gini coefficient."""
return self._metric_json['Gini']
def mse(self):
"""
:return: Retrieve the MSE for this set of metrics
"""
return self._metric_json['MSE']
def rmse(self):
"""
:return: Retrieve the RMSE for this set of metrics
"""
return self._metric_json['RMSE']
def mae(self):
"""
:return: Retrieve the MAE for this set of metrics
"""
return self._metric_json['mae']
def rmsle(self):
"""
:return: Retrieve the RMSLE for this set of metrics
"""
return self._metric_json['rmsle']
def residual_deviance(self):
"""
:return: the residual deviance if the model has residual deviance, or None if no residual deviance.
"""
if MetricsBase._has(self._metric_json, "residual_deviance"):
return self._metric_json["residual_deviance"]
return None
def residual_degrees_of_freedom(self):
"""
:return: the residual dof if the model has residual deviance, or None if no residual dof.
"""
if MetricsBase._has(self._metric_json, "residual_degrees_of_freedom"):
return self._metric_json["residual_degrees_of_freedom"]
return None
def null_deviance(self):
"""
:return: the null deviance if the model has residual deviance, or None if no null deviance.
"""
if MetricsBase._has(self._metric_json, "null_deviance"):
return self._metric_json["null_deviance"]
return None
def null_degrees_of_freedom(self):
"""
:return: the null dof if the model has residual deviance, or None if no null dof.
"""
if MetricsBase._has(self._metric_json, "null_degrees_of_freedom"):
return self._metric_json["null_degrees_of_freedom"]
return None
def mean_per_class_error(self):
"""
Retrieve the mean per class error.
"""
return self._metric_json['mean_per_class_error']
# Deprecated functions; left here for backward compatibility
_bcim = {
"giniCoef": lambda self, *args, **kwargs: self.gini(*args, **kwargs)
}
class H2ORegressionModelMetrics(MetricsBase):
"""
This class provides an API for inspecting the metrics returned by a regression model.
It is possible to retrieve the R^2 (1 - MSE/variance) and MSE
"""
def __init__(self, metric_json, on=None, algo=""):
super(H2ORegressionModelMetrics, self).__init__(metric_json, on, algo)
class H2OClusteringModelMetrics(MetricsBase):
def __init__(self, metric_json, on=None, algo=""):
super(H2OClusteringModelMetrics, self).__init__(metric_json, on, algo)
def tot_withinss(self):
"""
:return: the Total Within Cluster Sum-of-Square Error, or None if not present.
"""
if MetricsBase._has(self._metric_json, "tot_withinss"):
return self._metric_json["tot_withinss"]
return None
def totss(self):
"""
:return: the Total Sum-of-Square Error to Grand Mean, or None if not present.
"""
if MetricsBase._has(self._metric_json, "totss"):
return self._metric_json["totss"]
return None
def betweenss(self):
"""
:return: the Between Cluster Sum-of-Square Error, or None if not present.
"""
if MetricsBase._has(self._metric_json, "betweenss"):
return self._metric_json["betweenss"]
return None
class H2OMultinomialModelMetrics(MetricsBase):
def __init__(self, metric_json, on=None, algo=""):
super(H2OMultinomialModelMetrics, self).__init__(metric_json, on, algo)
def confusion_matrix(self):
"""
Returns a confusion matrix based of H2O's default prediction threshold for a dataset
"""
return self._metric_json['cm']['table']
def hit_ratio_table(self):
"""
Retrieve the Hit Ratios
"""
return self._metric_json['hit_ratio_table']
class H2OBinomialModelMetrics(MetricsBase):
"""
This class is essentially an API for the AUC object.
This class contains methods for inspecting the AUC for different criteria.
To input the different criteria, use the static variable `criteria`
"""
def __init__(self, metric_json, on=None, algo=""):
"""
Create a new Binomial Metrics object (essentially a wrapper around some json)
:param metric_json: A blob of json holding all of the needed information
:param on_train: Metrics built on training data (default is False)
:param on_valid: Metrics built on validation data (default is False)
:param on_xval: Metrics built on cross validation data (default is False)
:param algo: The algorithm the metrics are based off of (e.g. deeplearning, gbm, etc.)
:return: A new H2OBinomialModelMetrics object.
"""
super(H2OBinomialModelMetrics, self).__init__(metric_json, on, algo)
def F1(self, thresholds=None):
"""
:param thresholds: thresholds parameter must be a list (i.e. [0.01, 0.5, 0.99]). If None, then the thresholds in this set of metrics will be used.
:return: The F1 for the given set of thresholds.
"""
return self.metric("f1", thresholds=thresholds)
def F2(self, thresholds=None):
"""
:param thresholds: thresholds parameter must be a list (i.e. [0.01, 0.5, 0.99]). If None, then the thresholds in this set of metrics will be used.
:return: The F2 for this set of metrics and thresholds
"""
return self.metric("f2", thresholds=thresholds)
def F0point5(self, thresholds=None):
"""
:param thresholds: thresholds parameter must be a list (i.e. [0.01, 0.5, 0.99]). If None, then the thresholds in this set of metrics will be used.
:return: The F0point5 for this set of metrics and thresholds.
"""
return self.metric("f0point5", thresholds=thresholds)
def accuracy(self, thresholds=None):
"""
:param thresholds: thresholds parameter must be a list (i.e. [0.01, 0.5, 0.99]). If None, then the thresholds in this set of metrics will be used.
:return: The accuracy for this set of metrics and thresholds
"""
return self.metric("accuracy", thresholds=thresholds)
def error(self, thresholds=None):
"""
:param thresholds: thresholds parameter must be a list (i.e. [0.01, 0.5, 0.99]). If None, then the thresholds in this set of metrics will be used.
:return: The error for this set of metrics and thresholds.
"""
return 1 - self.metric("accuracy", thresholds=thresholds)
def precision(self, thresholds=None):
"""
:param thresholds: thresholds parameter must be a list (i.e. [0.01, 0.5, 0.99]). If None, then the thresholds in this set of metrics will be used.
:return: The precision for this set of metrics and thresholds.
"""
return self.metric("precision", thresholds=thresholds)
def tpr(self, thresholds=None):
"""
:param thresholds: thresholds parameter must be a list (i.e. [0.01, 0.5, 0.99]). If None, then the thresholds in this set of metrics will be used.
:return: The True Postive Rate
"""
return self.metric("tpr", thresholds=thresholds)
def tnr(self, thresholds=None):
"""
:param thresholds: thresholds parameter must be a list (i.e. [0.01, 0.5, 0.99]). If None, then the thresholds in this set of metrics will be used.
:return: The True Negative Rate
"""
return self.metric("tnr", thresholds=thresholds)
def fnr(self, thresholds=None):
"""
:param thresholds: thresholds parameter must be a list (i.e. [0.01, 0.5, 0.99]). If None, then the thresholds in this set of metrics will be used.
:return: The False Negative Rate
"""
return self.metric("fnr", thresholds=thresholds)
def fpr(self, thresholds=None):
"""
:param thresholds: thresholds parameter must be a list (i.e. [0.01, 0.5, 0.99]). If None, then the thresholds in this set of metrics will be used.
:return: The False Positive Rate
"""
return self.metric("fpr", thresholds=thresholds)
def recall(self, thresholds=None):
"""
:param thresholds: thresholds parameter must be a list (i.e. [0.01, 0.5, 0.99]). If None, then the thresholds in this set of metrics will be used.
:return: Recall for this set of metrics and thresholds
"""
return self.metric("tpr", thresholds=thresholds)
def sensitivity(self, thresholds=None):
"""
:param thresholds: thresholds parameter must be a list (i.e. [0.01, 0.5, 0.99]). If None, then the thresholds in this set of metrics will be used.
:return: Sensitivity or True Positive Rate for this set of metrics and thresholds
"""
return self.metric("tpr", thresholds=thresholds)
def fallout(self, thresholds=None):
"""
:param thresholds: thresholds parameter must be a list (i.e. [0.01, 0.5, 0.99]). If None, then the thresholds in this set of metrics will be used.
:return: The fallout or False Positive Rate for this set of metrics and thresholds
"""
return self.metric("fpr", thresholds=thresholds)
def missrate(self, thresholds=None):
"""
:param thresholds: thresholds parameter must be a list (i.e. [0.01, 0.5, 0.99]). If None, then the thresholds in this set of metrics will be used.
:return: THe missrate or False Negative Rate.
"""
return self.metric("fnr", thresholds=thresholds)
def specificity(self, thresholds=None):
"""
:param thresholds: thresholds parameter must be a list (i.e. [0.01, 0.5, 0.99]). If None, then the thresholds in this set of metrics will be used.
:return: The specificity or True Negative Rate.
"""
return self.metric("tnr", thresholds=thresholds)
def mcc(self, thresholds=None):
"""
:param thresholds: thresholds parameter must be a list (i.e. [0.01, 0.5, 0.99]). If None, then the thresholds in this set of metrics will be used.
:return: The absolute MCC (a value between 0 and 1, 0 being totally dissimilar, 1 being identical)
"""
return self.metric("absolute_mcc", thresholds=thresholds)
def max_per_class_error(self, thresholds=None):
"""
:param thresholds: thresholds parameter must be a list (i.e. [0.01, 0.5, 0.99]). If None, then the thresholds in this set of metrics will be used.
:return: Return 1 - min_per_class_accuracy
"""
return 1 - self.metric("min_per_class_accuracy", thresholds=thresholds)
def mean_per_class_error(self, thresholds=None):
"""
:param thresholds: thresholds parameter must be a list (i.e. [0.01, 0.5, 0.99]). If None, then the thresholds in this set of metrics will be used.
:return: Return mean_per_class_error
"""
return [[x[0], 1 - x[1]] for x in self.metric("mean_per_class_accuracy", thresholds=thresholds)]
def metric(self, metric, thresholds=None):
"""
:param metric: The desired metric
:param thresholds: thresholds parameter must be a list (i.e. [0.01, 0.5, 0.99]). If None, then
the thresholds in this set of metrics will be used.
:return: The set of metrics for the list of thresholds
"""
assert_is_type(thresholds, None, [numeric])
if not thresholds: thresholds = [self.find_threshold_by_max_metric(metric)]
thresh2d = self._metric_json['thresholds_and_metric_scores']
metrics = []
for t in thresholds:
idx = self.find_idx_by_threshold(t)
metrics.append([t, thresh2d[metric][idx]])
return metrics
def plot(self, type="roc", server=False):
"""
Produce the desired metric plot
:param type: the type of metric plot (currently, only ROC supported)
:param show: if False, the plot is not shown. matplotlib show method is blocking.
:return: None
"""
# TODO: add more types (i.e. cutoffs)
assert_is_type(type, "roc")
# check for matplotlib. exit if absent.
try:
imp.find_module('matplotlib')
import matplotlib
if server: matplotlib.use('Agg', warn=False)
import matplotlib.pyplot as plt
except ImportError:
print("matplotlib is required for this function!")
return
if type == "roc":
plt.xlabel('False Positive Rate (FPR)')
plt.ylabel('True Positive Rate (TPR)')
plt.title('ROC Curve')
plt.text(0.5, 0.5, r'AUC={0:.4f}'.format(self._metric_json["AUC"]))
plt.plot(self.fprs, self.tprs, 'b--')
plt.axis([0, 1, 0, 1])
if not server: plt.show()
@property
def fprs(self):
"""
Return all false positive rates for all threshold values.
:return: a list of false positive rates.
"""
return self._metric_json["thresholds_and_metric_scores"]["fpr"]
@property
def tprs(self):
"""
Return all true positive rates for all threshold values.
:return: a list of true positive rates.
"""
return self._metric_json["thresholds_and_metric_scores"]["tpr"]
def confusion_matrix(self, metrics=None, thresholds=None):
"""
Get the confusion matrix for the specified metric
:param metrics: A string (or list of strings) in {"min_per_class_accuracy", "absolute_mcc", "tnr", "fnr", "fpr",
"tpr", "precision", "accuracy", "f0point5", "f2", "f1","mean_per_class_accuracy"}
:param thresholds: A value (or list of values) between 0 and 1
:return: a list of ConfusionMatrix objects (if there are more than one to return), or a single ConfusionMatrix
(if there is only one)
"""
# make lists out of metrics and thresholds arguments
if metrics is None and thresholds is None: metrics = ["f1"]
if isinstance(metrics, list):
metrics_list = metrics
elif metrics is None:
metrics_list = []
else:
metrics_list = [metrics]
if isinstance(thresholds, list):
thresholds_list = thresholds
elif thresholds is None:
thresholds_list = []
else:
thresholds_list = [thresholds]
# error check the metrics_list and thresholds_list
assert_is_type(thresholds_list, [numeric])
assert_satisfies(thresholds_list, all(0 <= t <= 1 for t in thresholds_list))
if not all(m.lower() in ["min_per_class_accuracy", "absolute_mcc", "precision", "recall", "specificity", "accuracy",
"f0point5", "f2", "f1", "mean_per_class_accuracy"] for m in metrics_list):
raise ValueError(
"The only allowable metrics are min_per_class_accuracy, absolute_mcc, precision, accuracy, f0point5, "
"f2, f1, mean_per_class_accuracy")
# make one big list that combines the thresholds and metric-thresholds
metrics_thresholds = [self.find_threshold_by_max_metric(m) for m in metrics_list]
for mt in metrics_thresholds:
thresholds_list.append(mt)
thresh2d = self._metric_json['thresholds_and_metric_scores']
actual_thresholds = [float(e[0]) for i, e in enumerate(thresh2d.cell_values)]
cms = []
for t in thresholds_list:
idx = self.find_idx_by_threshold(t)
row = thresh2d.cell_values[idx]
tns = row[11]
fns = row[12]
fps = row[13]
tps = row[14]
p = tps + fns
n = tns + fps
c0 = n - fps
c1 = p - tps
if t in metrics_thresholds:
m = metrics_list[metrics_thresholds.index(t)]
table_header = "Confusion Matrix (Act/Pred) for max " + m + " @ threshold = " + str(
actual_thresholds[idx])
else:
table_header = "Confusion Matrix (Act/Pred) @ threshold = " + str(actual_thresholds[idx])
cms.append(ConfusionMatrix(cm=[[c0, fps], [c1, tps]], domains=self._metric_json['domain'],
table_header=table_header))
if len(cms) == 1:
return cms[0]
else:
return cms
def find_threshold_by_max_metric(self, metric):
"""
:param metric: A string in {"min_per_class_accuracy", "absolute_mcc", "precision", "recall", "specificity", "accuracy", "f0point5", "f2", "f1", "mean_per_class_accuracy"}
:return: the threshold at which the given metric is maximum.
"""
crit2d = self._metric_json['max_criteria_and_metric_scores']
for e in crit2d.cell_values:
if e[0] == "max " + metric.lower():
return e[1]
raise ValueError("No metric " + str(metric.lower()))
def find_idx_by_threshold(self, threshold):
"""
Retrieve the index in this metric's threshold list at which the given threshold is located.
:param threshold: Find the index of this input threshold.
:return: Return the index or throw a ValueError if no such index can be found.
"""
assert_is_type(threshold, numeric)
thresh2d = self._metric_json['thresholds_and_metric_scores']
for i, e in enumerate(thresh2d.cell_values):
t = float(e[0])
if abs(t - threshold) < 0.00000001 * max(t, threshold):
return i
if threshold >= 0 and threshold <= 1:
thresholds = [float(e[0]) for i, e in enumerate(thresh2d.cell_values)]
threshold_diffs = [abs(t - threshold) for t in thresholds]
closest_idx = threshold_diffs.index(min(threshold_diffs))
closest_threshold = thresholds[closest_idx]
print("Could not find exact threshold {0}; using closest threshold found {1}." \
.format(threshold, closest_threshold))
return closest_idx
raise ValueError("Threshold must be between 0 and 1, but got {0} ".format(threshold))
def gains_lift(self):
"""
Retrieve the Gains/Lift table
"""
if 'gains_lift_table' in self._metric_json:
return self._metric_json['gains_lift_table']
return None
class H2OAutoEncoderModelMetrics(MetricsBase):
def __init__(self, metric_json, on=None, algo=""):
super(H2OAutoEncoderModelMetrics, self).__init__(metric_json, on, algo)
class H2ODimReductionModelMetrics(MetricsBase):
def __init__(self, metric_json, on=None, algo=""):
super(H2ODimReductionModelMetrics, self).__init__(metric_json, on, algo)
def num_err(self):
"""
:return: the Sum of Squared Error over non-missing numeric entries, or None if not present.
"""
if MetricsBase._has(self._metric_json, "numerr"):
return self._metric_json["numerr"]
return None
def cat_err(self):
"""
:return: the Number of Misclassified categories over non-missing categorical entries, or None if not present.
"""
if MetricsBase._has(self._metric_json, "caterr"):
return self._metric_json["caterr"]
return None
| apache-2.0 |
sounay/flaminggo-test | onadata/libs/utils/export_tools.py | 3 | 40050 | import csv
from datetime import datetime, date
import json
import os
import re
import six
from urlparse import urlparse
from zipfile import ZipFile
from bson import json_util
from django.conf import settings
from django.core.files.base import File
from django.core.files.temp import NamedTemporaryFile
from django.core.files.storage import get_storage_class
from django.contrib.auth.models import User
from django.shortcuts import render_to_response
from openpyxl.date_time import SharedDate
from openpyxl.workbook import Workbook
from pyxform.question import Question
from pyxform.section import Section, RepeatingSection
from savReaderWriter import SavWriter
from json2xlsclient.client import Client
from onadata.apps.logger.models import Attachment, Instance, XForm
from onadata.apps.main.models.meta_data import MetaData
from onadata.apps.viewer.models.export import Export
from onadata.apps.viewer.models.parsed_instance import\
_is_invalid_for_mongo, _encode_for_mongo, dict_for_mongo,\
_decode_from_mongo
from onadata.libs.utils.viewer_tools import create_attachments_zipfile,\
image_urls
from onadata.libs.utils.common_tags import (
ID, XFORM_ID_STRING, STATUS, ATTACHMENTS, GEOLOCATION, BAMBOO_DATASET_ID,
DELETEDAT, USERFORM_ID, INDEX, PARENT_INDEX, PARENT_TABLE_NAME,
SUBMISSION_TIME, UUID, TAGS, NOTES, VERSION)
from onadata.libs.exceptions import J2XException
# this is Mongo Collection where we will store the parsed submissions
xform_instances = settings.MONGO_DB.instances
QUESTION_TYPES_TO_EXCLUDE = [
u'note',
]
# the bind type of select multiples that we use to compare
MULTIPLE_SELECT_BIND_TYPE = u"select"
GEOPOINT_BIND_TYPE = u"geopoint"
def encode_if_str(row, key, encode_dates=False):
val = row.get(key)
if isinstance(val, six.string_types):
return val.encode('utf-8')
if encode_dates and isinstance(val, datetime):
return val.strftime('%Y-%m-%dT%H:%M:%S%z').encode('utf-8')
if encode_dates and isinstance(val, date):
return val.strftime('%Y-%m-%d').encode('utf-8')
return val
def question_types_to_exclude(_type):
return _type in QUESTION_TYPES_TO_EXCLUDE
class DictOrganizer(object):
def set_dict_iterator(self, dict_iterator):
self._dict_iterator = dict_iterator
# Every section will get its own table
# I need to think of an easy way to flatten out a dictionary
# parent name, index, table name, data
def _build_obs_from_dict(self, d, obs, table_name,
parent_table_name, parent_index):
if table_name not in obs:
obs[table_name] = []
this_index = len(obs[table_name])
obs[table_name].append({
u"_parent_table_name": parent_table_name,
u"_parent_index": parent_index,
})
for k, v in d.items():
if type(v) != dict and type(v) != list:
assert k not in obs[table_name][-1]
obs[table_name][-1][k] = v
obs[table_name][-1][u"_index"] = this_index
for k, v in d.items():
if type(v) == dict:
kwargs = {
"d": v,
"obs": obs,
"table_name": k,
"parent_table_name": table_name,
"parent_index": this_index
}
self._build_obs_from_dict(**kwargs)
if type(v) == list:
for i, item in enumerate(v):
kwargs = {
"d": item,
"obs": obs,
"table_name": k,
"parent_table_name": table_name,
"parent_index": this_index,
}
self._build_obs_from_dict(**kwargs)
return obs
def get_observation_from_dict(self, d):
result = {}
assert len(d.keys()) == 1
root_name = d.keys()[0]
kwargs = {
"d": d[root_name],
"obs": result,
"table_name": root_name,
"parent_table_name": u"",
"parent_index": -1,
}
self._build_obs_from_dict(**kwargs)
return result
def dict_to_joined_export(data, index, indices, name):
"""
Converts a dict into one or more tabular datasets
"""
output = {}
# TODO: test for _geolocation and attachment lists
if isinstance(data, dict):
for key, val in data.iteritems():
if isinstance(val, list) and key not in [NOTES, TAGS]:
output[key] = []
for child in val:
if key not in indices:
indices[key] = 0
indices[key] += 1
child_index = indices[key]
new_output = dict_to_joined_export(
child, child_index, indices, key)
d = {INDEX: child_index, PARENT_INDEX: index,
PARENT_TABLE_NAME: name}
# iterate over keys within new_output and append to
# main output
for out_key, out_val in new_output.iteritems():
if isinstance(out_val, list):
if out_key not in output:
output[out_key] = []
output[out_key].extend(out_val)
else:
d.update(out_val)
output[key].append(d)
else:
if name not in output:
output[name] = {}
if key in [TAGS]:
output[name][key] = ",".join(val)
elif key in [NOTES]:
output[name][key] = "\r\n".join(
[v['note'] for v in val])
else:
output[name][key] = val
return output
class ExportBuilder(object):
IGNORED_COLUMNS = [XFORM_ID_STRING, STATUS, ATTACHMENTS, GEOLOCATION,
BAMBOO_DATASET_ID, DELETEDAT]
# fields we export but are not within the form's structure
EXTRA_FIELDS = [ID, UUID, SUBMISSION_TIME, INDEX, PARENT_TABLE_NAME,
PARENT_INDEX, TAGS, NOTES, VERSION]
SPLIT_SELECT_MULTIPLES = True
BINARY_SELECT_MULTIPLES = False
# column group delimiters
GROUP_DELIMITER_SLASH = '/'
GROUP_DELIMITER_DOT = '.'
GROUP_DELIMITER = GROUP_DELIMITER_SLASH
GROUP_DELIMITERS = [GROUP_DELIMITER_SLASH, GROUP_DELIMITER_DOT]
TYPES_TO_CONVERT = ['int', 'decimal', 'date'] # , 'dateTime']
CONVERT_FUNCS = {
'int': lambda x: int(x),
'decimal': lambda x: float(x),
'date': lambda x: ExportBuilder.string_to_date_with_xls_validation(x),
'dateTime': lambda x: datetime.strptime(x[:19], '%Y-%m-%dT%H:%M:%S')
}
XLS_SHEET_NAME_MAX_CHARS = 31
@classmethod
def string_to_date_with_xls_validation(cls, date_str):
date_obj = datetime.strptime(date_str, '%Y-%m-%d').date()
try:
SharedDate().datetime_to_julian(date_obj)
except ValueError:
return date_str
else:
return date_obj
@classmethod
def format_field_title(cls, abbreviated_xpath, field_delimiter):
if field_delimiter != '/':
return field_delimiter.join(abbreviated_xpath.split('/'))
return abbreviated_xpath
def set_survey(self, survey):
# TODO resolve circular import
from onadata.apps.viewer.models.data_dictionary import\
DataDictionary
def build_sections(
current_section, survey_element, sections, select_multiples,
gps_fields, encoded_fields, field_delimiter='/'):
for child in survey_element.children:
current_section_name = current_section['name']
# if a section, recurs
if isinstance(child, Section):
# if its repeating, build a new section
if isinstance(child, RepeatingSection):
# section_name in recursive call changes
section = {
'name': child.get_abbreviated_xpath(),
'elements': []}
self.sections.append(section)
build_sections(
section, child, sections, select_multiples,
gps_fields, encoded_fields, field_delimiter)
else:
# its a group, recurs using the same section
build_sections(
current_section, child, sections, select_multiples,
gps_fields, encoded_fields, field_delimiter)
elif isinstance(child, Question) and child.bind.get(u"type")\
not in QUESTION_TYPES_TO_EXCLUDE:
# add to survey_sections
if isinstance(child, Question):
child_xpath = child.get_abbreviated_xpath()
current_section['elements'].append({
'title': ExportBuilder.format_field_title(
child.get_abbreviated_xpath(),
field_delimiter),
'xpath': child_xpath,
'type': child.bind.get(u"type")
})
if _is_invalid_for_mongo(child_xpath):
if current_section_name not in encoded_fields:
encoded_fields[current_section_name] = {}
encoded_fields[current_section_name].update(
{child_xpath: _encode_for_mongo(child_xpath)})
# if its a select multiple, make columns out of its choices
if child.bind.get(u"type") == MULTIPLE_SELECT_BIND_TYPE\
and self.SPLIT_SELECT_MULTIPLES:
for c in child.children:
_xpath = c.get_abbreviated_xpath()
_title = ExportBuilder.format_field_title(
_xpath, field_delimiter)
choice = {
'title': _title,
'xpath': _xpath,
'type': 'string'
}
if choice not in current_section['elements']:
current_section['elements'].append(choice)
_append_xpaths_to_section(
current_section_name, select_multiples,
child.get_abbreviated_xpath(),
[c.get_abbreviated_xpath()
for c in child.children])
# split gps fields within this section
if child.bind.get(u"type") == GEOPOINT_BIND_TYPE:
# add columns for geopoint components
xpaths = DataDictionary.get_additional_geopoint_xpaths(
child.get_abbreviated_xpath())
current_section['elements'].extend(
[
{
'title': ExportBuilder.format_field_title(
xpath, field_delimiter),
'xpath': xpath,
'type': 'decimal'
}
for xpath in xpaths
])
_append_xpaths_to_section(
current_section_name, gps_fields,
child.get_abbreviated_xpath(), xpaths)
def _append_xpaths_to_section(current_section_name, field_list, xpath,
xpaths):
if current_section_name not in field_list:
field_list[current_section_name] = {}
field_list[
current_section_name][xpath] = xpaths
self.survey = survey
self.select_multiples = {}
self.gps_fields = {}
self.encoded_fields = {}
main_section = {'name': survey.name, 'elements': []}
self.sections = [main_section]
build_sections(
main_section, self.survey, self.sections,
self.select_multiples, self.gps_fields, self.encoded_fields,
self.GROUP_DELIMITER)
def section_by_name(self, name):
matches = filter(lambda s: s['name'] == name, self.sections)
assert(len(matches) == 1)
return matches[0]
@classmethod
def split_select_multiples(cls, row, select_multiples):
# for each select_multiple, get the associated data and split it
for xpath, choices in select_multiples.iteritems():
# get the data matching this xpath
data = row.get(xpath)
selections = []
if data:
selections = [
u'{0}/{1}'.format(
xpath, selection) for selection in data.split()]
if not cls.BINARY_SELECT_MULTIPLES:
row.update(dict(
[(choice, choice in selections if selections else None)
for choice in choices]))
else:
YES = 1
NO = 0
row.update(dict(
[(choice, YES if choice in selections else NO)
for choice in choices]))
return row
@classmethod
def split_gps_components(cls, row, gps_fields):
# for each gps_field, get associated data and split it
for xpath, gps_components in gps_fields.iteritems():
data = row.get(xpath)
if data:
gps_parts = data.split()
if len(gps_parts) > 0:
row.update(zip(gps_components, gps_parts))
return row
@classmethod
def decode_mongo_encoded_fields(cls, row, encoded_fields):
for xpath, encoded_xpath in encoded_fields.iteritems():
if row.get(encoded_xpath):
val = row.pop(encoded_xpath)
row.update({xpath: val})
return row
@classmethod
def decode_mongo_encoded_section_names(cls, data):
return dict([(_decode_from_mongo(k), v) for k, v in data.iteritems()])
@classmethod
def convert_type(cls, value, data_type):
"""
Convert data to its native type e.g. string '1' to int 1
@param value: the string value to convert
@param data_type: the native data type to convert to
@return: the converted value
"""
func = ExportBuilder.CONVERT_FUNCS.get(data_type, lambda x: x)
try:
return func(value)
except ValueError:
return value
def pre_process_row(self, row, section):
"""
Split select multiples, gps and decode . and $
"""
section_name = section['name']
# first decode fields so that subsequent lookups
# have decoded field names
if section_name in self.encoded_fields:
row = ExportBuilder.decode_mongo_encoded_fields(
row, self.encoded_fields[section_name])
if self.SPLIT_SELECT_MULTIPLES and\
section_name in self.select_multiples:
row = ExportBuilder.split_select_multiples(
row, self.select_multiples[section_name])
if section_name in self.gps_fields:
row = ExportBuilder.split_gps_components(
row, self.gps_fields[section_name])
# convert to native types
for elm in section['elements']:
# only convert if its in our list and its not empty, just to
# optimize
value = row.get(elm['xpath'])
if elm['type'] in ExportBuilder.TYPES_TO_CONVERT\
and value is not None and value != '':
row[elm['xpath']] = ExportBuilder.convert_type(
value, elm['type'])
return row
def to_zipped_csv(self, path, data, *args):
def write_row(row, csv_writer, fields):
csv_writer.writerow(
[encode_if_str(row, field) for field in fields])
csv_defs = {}
for section in self.sections:
csv_file = NamedTemporaryFile(suffix=".csv")
csv_writer = csv.writer(csv_file)
csv_defs[section['name']] = {
'csv_file': csv_file, 'csv_writer': csv_writer}
# write headers
for section in self.sections:
fields = [element['title'] for element in section['elements']]\
+ self.EXTRA_FIELDS
csv_defs[section['name']]['csv_writer'].writerow(
[f.encode('utf-8') for f in fields])
index = 1
indices = {}
survey_name = self.survey.name
for d in data:
# decode mongo section names
joined_export = dict_to_joined_export(d, index, indices,
survey_name)
output = ExportBuilder.decode_mongo_encoded_section_names(
joined_export)
# attach meta fields (index, parent_index, parent_table)
# output has keys for every section
if survey_name not in output:
output[survey_name] = {}
output[survey_name][INDEX] = index
output[survey_name][PARENT_INDEX] = -1
for section in self.sections:
# get data for this section and write to csv
section_name = section['name']
csv_def = csv_defs[section_name]
fields = [
element['xpath'] for element in
section['elements']] + self.EXTRA_FIELDS
csv_writer = csv_def['csv_writer']
# section name might not exist within the output, e.g. data was
# not provided for said repeat - write test to check this
row = output.get(section_name, None)
if type(row) == dict:
write_row(
self.pre_process_row(row, section),
csv_writer, fields)
elif type(row) == list:
for child_row in row:
write_row(
self.pre_process_row(child_row, section),
csv_writer, fields)
index += 1
# write zipfile
with ZipFile(path, 'w') as zip_file:
for section_name, csv_def in csv_defs.iteritems():
csv_file = csv_def['csv_file']
csv_file.seek(0)
zip_file.write(
csv_file.name, "_".join(section_name.split("/")) + ".csv")
# close files when we are done
for section_name, csv_def in csv_defs.iteritems():
csv_def['csv_file'].close()
@classmethod
def get_valid_sheet_name(cls, desired_name, existing_names):
# a sheet name has to be <= 31 characters and not a duplicate of an
# existing sheet
# truncate sheet_name to XLSDataFrameBuilder.SHEET_NAME_MAX_CHARS
new_sheet_name = \
desired_name[:cls.XLS_SHEET_NAME_MAX_CHARS]
# make sure its unique within the list
i = 1
generated_name = new_sheet_name
while generated_name in existing_names:
digit_length = len(str(i))
allowed_name_len = cls.XLS_SHEET_NAME_MAX_CHARS - \
digit_length
# make name the required len
if len(generated_name) > allowed_name_len:
generated_name = generated_name[:allowed_name_len]
generated_name = "{0}{1}".format(generated_name, i)
i += 1
return generated_name
def to_xls_export(self, path, data, *args):
def write_row(data, work_sheet, fields, work_sheet_titles):
# update parent_table with the generated sheet's title
data[PARENT_TABLE_NAME] = work_sheet_titles.get(
data.get(PARENT_TABLE_NAME))
work_sheet.append([data.get(f) for f in fields])
wb = Workbook(optimized_write=True)
work_sheets = {}
# map of section_names to generated_names
work_sheet_titles = {}
for section in self.sections:
section_name = section['name']
work_sheet_title = ExportBuilder.get_valid_sheet_name(
"_".join(section_name.split("/")), work_sheet_titles.values())
work_sheet_titles[section_name] = work_sheet_title
work_sheets[section_name] = wb.create_sheet(
title=work_sheet_title)
# write the headers
for section in self.sections:
section_name = section['name']
headers = [
element['title'] for element in
section['elements']] + self.EXTRA_FIELDS
# get the worksheet
ws = work_sheets[section_name]
ws.append(headers)
index = 1
indices = {}
survey_name = self.survey.name
for d in data:
joined_export = dict_to_joined_export(d, index, indices,
survey_name)
output = ExportBuilder.decode_mongo_encoded_section_names(
joined_export)
# attach meta fields (index, parent_index, parent_table)
# output has keys for every section
if survey_name not in output:
output[survey_name] = {}
output[survey_name][INDEX] = index
output[survey_name][PARENT_INDEX] = -1
for section in self.sections:
# get data for this section and write to xls
section_name = section['name']
fields = [
element['xpath'] for element in
section['elements']] + self.EXTRA_FIELDS
ws = work_sheets[section_name]
# section might not exist within the output, e.g. data was
# not provided for said repeat - write test to check this
row = output.get(section_name, None)
if type(row) == dict:
write_row(
self.pre_process_row(row, section),
ws, fields, work_sheet_titles)
elif type(row) == list:
for child_row in row:
write_row(
self.pre_process_row(child_row, section),
ws, fields, work_sheet_titles)
index += 1
wb.save(filename=path)
def to_flat_csv_export(
self, path, data, username, id_string, filter_query):
# TODO resolve circular import
from onadata.apps.viewer.pandas_mongo_bridge import\
CSVDataFrameBuilder
csv_builder = CSVDataFrameBuilder(
username, id_string, filter_query, self.GROUP_DELIMITER,
self.SPLIT_SELECT_MULTIPLES, self.BINARY_SELECT_MULTIPLES)
csv_builder.export_to(path)
def to_zipped_sav(self, path, data, *args):
def write_row(row, csv_writer, fields):
sav_writer.writerow(
[encode_if_str(row, field, True) for field in fields])
sav_defs = {}
# write headers
for section in self.sections:
fields = [element['title'] for element in section['elements']]\
+ self.EXTRA_FIELDS
c = 0
var_labels = {}
var_names = []
tmp_k = {}
for field in fields:
c += 1
var_name = 'var%d' % c
var_labels[var_name] = field
var_names.append(var_name)
tmp_k[field] = var_name
var_types = dict(
[(tmp_k[element['title']],
0 if element['type'] in ['decimal', 'int'] else 255)
for element in section['elements']]
+ [(tmp_k[item],
0 if item in ['_id', '_index', '_parent_index'] else 255)
for item in self.EXTRA_FIELDS]
)
sav_file = NamedTemporaryFile(suffix=".sav")
sav_writer = SavWriter(sav_file.name, varNames=var_names,
varTypes=var_types,
varLabels=var_labels, ioUtf8=True)
sav_defs[section['name']] = {
'sav_file': sav_file, 'sav_writer': sav_writer}
index = 1
indices = {}
survey_name = self.survey.name
for d in data:
# decode mongo section names
joined_export = dict_to_joined_export(d, index, indices,
survey_name)
output = ExportBuilder.decode_mongo_encoded_section_names(
joined_export)
# attach meta fields (index, parent_index, parent_table)
# output has keys for every section
if survey_name not in output:
output[survey_name] = {}
output[survey_name][INDEX] = index
output[survey_name][PARENT_INDEX] = -1
for section in self.sections:
# get data for this section and write to csv
section_name = section['name']
sav_def = sav_defs[section_name]
fields = [
element['xpath'] for element in
section['elements']] + self.EXTRA_FIELDS
sav_writer = sav_def['sav_writer']
row = output.get(section_name, None)
if type(row) == dict:
write_row(
self.pre_process_row(row, section),
sav_writer, fields)
elif type(row) == list:
for child_row in row:
write_row(
self.pre_process_row(child_row, section),
sav_writer, fields)
index += 1
for section_name, sav_def in sav_defs.iteritems():
sav_def['sav_writer'].closeSavFile(
sav_def['sav_writer'].fh, mode='wb')
# write zipfile
with ZipFile(path, 'w') as zip_file:
for section_name, sav_def in sav_defs.iteritems():
sav_file = sav_def['sav_file']
sav_file.seek(0)
zip_file.write(
sav_file.name, "_".join(section_name.split("/")) + ".sav")
# close files when we are done
for section_name, sav_def in sav_defs.iteritems():
sav_def['sav_file'].close()
def dict_to_flat_export(d, parent_index=0):
pass
def generate_export(export_type, extension, username, id_string,
export_id=None, filter_query=None, group_delimiter='/',
split_select_multiples=True,
binary_select_multiples=False):
"""
Create appropriate export object given the export type
"""
# TODO resolve circular import
from onadata.apps.viewer.models.export import Export
export_type_func_map = {
Export.XLS_EXPORT: 'to_xls_export',
Export.CSV_EXPORT: 'to_flat_csv_export',
Export.CSV_ZIP_EXPORT: 'to_zipped_csv',
Export.SAV_ZIP_EXPORT: 'to_zipped_sav',
}
xform = XForm.objects.get(
user__username__iexact=username, id_string__iexact=id_string)
# query mongo for the cursor
records = query_mongo(username, id_string, filter_query)
export_builder = ExportBuilder()
export_builder.GROUP_DELIMITER = group_delimiter
export_builder.SPLIT_SELECT_MULTIPLES = split_select_multiples
export_builder.BINARY_SELECT_MULTIPLES = binary_select_multiples
export_builder.set_survey(xform.data_dictionary().survey)
temp_file = NamedTemporaryFile(suffix=("." + extension))
# get the export function by export type
func = getattr(export_builder, export_type_func_map[export_type])
func.__call__(
temp_file.name, records, username, id_string, filter_query)
# generate filename
basename = "%s_%s" % (
id_string, datetime.now().strftime("%Y_%m_%d_%H_%M_%S"))
filename = basename + "." + extension
# check filename is unique
while not Export.is_filename_unique(xform, filename):
filename = increment_index_in_filename(filename)
file_path = os.path.join(
username,
'exports',
id_string,
export_type,
filename)
# TODO: if s3 storage, make private - how will we protect local storage??
storage = get_storage_class()()
# seek to the beginning as required by storage classes
temp_file.seek(0)
export_filename = storage.save(
file_path,
File(temp_file, file_path))
temp_file.close()
dir_name, basename = os.path.split(export_filename)
# get or create export object
if export_id:
export = Export.objects.get(id=export_id)
else:
export = Export(xform=xform, export_type=export_type)
export.filedir = dir_name
export.filename = basename
export.internal_status = Export.SUCCESSFUL
# dont persist exports that have a filter
if filter_query is None:
export.save()
return export
def query_mongo(username, id_string, query=None, hide_deleted=True):
query = json.loads(query, object_hook=json_util.object_hook)\
if query else {}
query = dict_for_mongo(query)
query[USERFORM_ID] = u'{0}_{1}'.format(username, id_string)
if hide_deleted:
# display only active elements
# join existing query with deleted_at_query on an $and
query = {"$and": [query, {"_deleted_at": None}]}
return xform_instances.find(query)
def should_create_new_export(xform, export_type):
# TODO resolve circular import
from onadata.apps.viewer.models.export import Export
if Export.objects.filter(
xform=xform, export_type=export_type).count() == 0\
or Export.exports_outdated(xform, export_type=export_type):
return True
return False
def newset_export_for(xform, export_type):
"""
Make sure you check that an export exists before calling this,
it will a DoesNotExist exception otherwise
"""
# TODO resolve circular import
from onadata.apps.viewer.models.export import Export
return Export.objects.filter(xform=xform, export_type=export_type)\
.latest('created_on')
def increment_index_in_filename(filename):
"""
filename should be in the form file.ext or file-2.ext - we check for the
dash and index and increment appropriately
"""
# check for an index i.e. dash then number then dot extension
regex = re.compile(r"(.+?)\-(\d+)(\..+)")
match = regex.match(filename)
if match:
basename = match.groups()[0]
index = int(match.groups()[1]) + 1
ext = match.groups()[2]
else:
index = 1
# split filename from ext
basename, ext = os.path.splitext(filename)
new_filename = "%s-%d%s" % (basename, index, ext)
return new_filename
def generate_attachments_zip_export(
export_type, extension, username, id_string, export_id=None,
filter_query=None):
# TODO resolve circular import
from onadata.apps.viewer.models.export import Export
xform = XForm.objects.get(user__username=username, id_string=id_string)
attachments = Attachment.objects.filter(instance__xform=xform)
basename = "%s_%s" % (id_string,
datetime.now().strftime("%Y_%m_%d_%H_%M_%S"))
filename = basename + "." + extension
file_path = os.path.join(
username,
'exports',
id_string,
export_type,
filename)
storage = get_storage_class()()
zip_file = None
try:
zip_file = create_attachments_zipfile(attachments)
try:
temp_file = open(zip_file.name)
export_filename = storage.save(
file_path,
File(temp_file, file_path))
finally:
temp_file.close()
finally:
zip_file and zip_file.close()
dir_name, basename = os.path.split(export_filename)
# get or create export object
if(export_id):
export = Export.objects.get(id=export_id)
else:
export = Export.objects.create(xform=xform, export_type=export_type)
export.filedir = dir_name
export.filename = basename
export.internal_status = Export.SUCCESSFUL
export.save()
return export
def generate_kml_export(
export_type, extension, username, id_string, export_id=None,
filter_query=None):
# TODO resolve circular import
from onadata.apps.viewer.models.export import Export
user = User.objects.get(username=username)
xform = XForm.objects.get(user__username=username, id_string=id_string)
response = render_to_response(
'survey.kml', {'data': kml_export_data(id_string, user)})
basename = "%s_%s" % (id_string,
datetime.now().strftime("%Y_%m_%d_%H_%M_%S"))
filename = basename + "." + extension
file_path = os.path.join(
username,
'exports',
id_string,
export_type,
filename)
storage = get_storage_class()()
temp_file = NamedTemporaryFile(suffix=extension)
temp_file.write(response.content)
temp_file.seek(0)
export_filename = storage.save(
file_path,
File(temp_file, file_path))
temp_file.close()
dir_name, basename = os.path.split(export_filename)
# get or create export object
if(export_id):
export = Export.objects.get(id=export_id)
else:
export = Export.objects.create(xform=xform, export_type=export_type)
export.filedir = dir_name
export.filename = basename
export.internal_status = Export.SUCCESSFUL
export.save()
return export
def kml_export_data(id_string, user):
# TODO resolve circular import
from onadata.apps.viewer.models.data_dictionary import DataDictionary
dd = DataDictionary.objects.get(id_string=id_string, user=user)
instances = Instance.objects.filter(
xform__user=user, xform__id_string=id_string, geom__isnull=False
).order_by('id')
data_for_template = []
labels = {}
def cached_get_labels(xpath):
if xpath in labels.keys():
return labels[xpath]
labels[xpath] = dd.get_label(xpath)
return labels[xpath]
for instance in instances:
# read the survey instances
data_for_display = instance.get_dict()
xpaths = data_for_display.keys()
xpaths.sort(cmp=instance.xform.data_dictionary().get_xpath_cmp())
label_value_pairs = [
(cached_get_labels(xpath), data_for_display[xpath]) for xpath in
xpaths if not xpath.startswith(u"_")]
table_rows = ['<tr><td>%s</td><td>%s</td></tr>' % (k, v) for k, v
in label_value_pairs]
img_urls = image_urls(instance)
img_url = img_urls[0] if img_urls else ""
point = instance.point
if point:
data_for_template.append({
'name': id_string,
'id': instance.id,
'lat': point.y,
'lng': point.x,
'image_urls': img_urls,
'table': '<table border="1"><a href="#"><img width="210" '
'class="thumbnail" src="%s" alt=""></a>%s'
'</table>' % (img_url, ''.join(table_rows))})
return data_for_template
def _get_records(instances):
return [clean_keys_of_slashes(instance.get_dict())
for instance in instances]
def clean_keys_of_slashes(record):
"""
Replaces the slashes found in a dataset keys with underscores
:param record: list containing a couple of dictionaries
:return: record with keys without slashes
"""
for key in record:
value = record[key]
if '/' in key:
# replace with _
record[key.replace('/', '_')]\
= record.pop(key)
# Check if the value is a list containing nested dict and apply same
if value:
if isinstance(value, list) and isinstance(value[0], dict):
for v in value:
clean_keys_of_slashes(v)
return record
def _get_server_from_metadata(xform, meta, token):
report_templates = MetaData.external_export(xform)
if meta:
try:
int(meta)
except ValueError:
raise Exception(u"Invalid metadata pk {0}".format(meta))
# Get the external server from the metadata
result = report_templates.get(pk=meta)
server = result.external_export_url
name = result.external_export_name
elif token:
server = token
name = None
else:
# Take the latest value in the metadata
if not report_templates:
raise Exception(
u"Could not find the template token: Please upload template.")
server = report_templates[0].external_export_url
name = report_templates[0].external_export_name
return server, name
def generate_external_export(
export_type, username, id_string, export_id=None, token=None,
filter_query=None, meta=None):
xform = XForm.objects.get(
user__username__iexact=username, id_string__iexact=id_string)
user = User.objects.get(username=username)
server, name = _get_server_from_metadata(xform, meta, token)
# dissect the url
parsed_url = urlparse(server)
token = parsed_url.path[5:]
ser = parsed_url.scheme + '://' + parsed_url.netloc
records = _get_records(Instance.objects.filter(
xform__user=user, xform__id_string=id_string))
status_code = 0
if records and server:
try:
client = Client(ser)
response = client.xls.create(token, json.dumps(records))
if hasattr(client.xls.conn, 'last_response'):
status_code = client.xls.conn.last_response.status_code
except Exception as e:
raise J2XException(
u"J2X client could not generate report. Server -> {0},"
u" Error-> {1}".format(server, e)
)
else:
if not server:
raise J2XException(u"External server not set")
elif not records:
raise J2XException(
u"No record to export. Form -> {0}".format(id_string)
)
# get or create export object
if export_id:
export = Export.objects.get(id=export_id)
else:
export = Export.objects.create(xform=xform, export_type=export_type)
export.export_url = response
if status_code == 201:
export.internal_status = Export.SUCCESSFUL
export.filename = name + '-' + response[5:] if name else response[5:]
export.export_url = ser + response
else:
export.internal_status = Export.FAILED
export.save()
return export
def upload_template_for_external_export(server, file_obj):
try:
client = Client(server)
response = client.template.create(template_file=file_obj)
if hasattr(client.template.conn, 'last_response'):
status_code = client.template.conn.last_response.status_code
except Exception as e:
response = str(e)
status_code = 500
return str(status_code) + '|' + response
| bsd-2-clause |
all-umass/superman | superman/preprocess/steps.py | 1 | 5980 | from __future__ import absolute_import, division, print_function
import numpy as np
import scipy.signal
import scipy.sparse
from sklearn.preprocessing import normalize
from sklearn.decomposition import PCA
from .utils import libs_norm3, cumulative_norm
__all__ = [
'BandNormalize', 'BezierSquash', 'CosineSquash', 'CumulativeNormalize',
'HingeSquash', 'L1Normalize', 'L2Normalize', 'LibsNormalize', 'LogSquash',
'MaxNormalize', 'MinZeroNormalize', 'Offset', 'PolynomialSquash',
'PrincipalComponents', 'SavitzkyGolayDerivative', 'SavitzkyGolaySmooth',
'SqrtSquash', 'TanhSquash'
]
class Preprocessor(object):
arg_type = float
def apply(self, spectra, wavelengths):
''' S, w = apply(S, w)'''
raise NotImplementedError('Subclasses must implement apply_vector.')
@classmethod
def from_string(cls, s):
if not s:
return cls()
args = map(cls.arg_type, s.split(':'))
return cls(*args)
class PrincipalComponents(Preprocessor):
name = 'pca'
def __init__(self, num_pcs):
# Hack: may be float in (0,1] or positive int. We'll assume 1-D in the case
# of 1.0, as that's more common.
if num_pcs >= 1:
assert num_pcs - int(num_pcs) == 0
num_pcs = int(num_pcs)
self.model = PCA(n_components=num_pcs)
def apply(self, spectra, wavelengths):
pcs = self.model.fit_transform(spectra)
return pcs, wavelengths
class PolynomialSquash(Preprocessor):
'''Generalized polynomial squashing function.
Derived from a normal cubic polynomial with f(0) = 0 and f(1) = 1.
We also enforce d/dx => 0 and d2/d2x <= 0, for a concave shape.
This constrains -0.5 < a < 1, and -2a-1 < b < min(-3a, 0).
'''
name = 'poly'
def __init__(self, a, b):
assert -0.5 < a < 1
assert -2*a - 1 < b < min(-3*a, 0)
c = 1 - a - b
self.poly = np.poly1d([a, b, c, 0])
def apply(self, spectra, wavelengths):
x = spectra / np.max(spectra, axis=1, keepdims=True)
p = self.poly(x)
return normalize(p, norm='l2', copy=False), wavelengths
class BezierSquash(Preprocessor):
'''Bezier squashing function.
Derived from a bezier curve with control points at [(0,0), (a,b), (1,1)]
Constraints are 0 < a < 1, 0 < b < 1, b > a (upper left of y = x line).
'''
name = 'bezier'
def __init__(self, a, b):
assert 0 < a < 1
assert 0 < b < 1
assert b > a
twoa = 2*a
twob = 2*b
if twoa == 1:
a += 1e-5
twoa = 2*a
self.args = (a, b, twoa, twob)
def apply(self, spectra, wavelengths):
x = spectra / np.max(spectra, axis=1, keepdims=True)
a, b, twoa, twob = self.args
tmp = np.sqrt(a*a-twoa*x+x)
foo = x * (1 - twob)
top = -twoa*(tmp+foo+b) + twob*tmp + foo + twoa*a
p = top / (1-twoa)**2
return normalize(p, norm='l2', copy=False), wavelengths
class HingeSquash(Preprocessor):
name = 'squash:hinge'
def __init__(self, h):
self.hinge = h
def apply(self, spectra, wavelengths):
return np.minimum(spectra, self.hinge), wavelengths
class CosineSquash(Preprocessor):
name = 'squash:cos'
def apply(self, spectra, wavelengths):
np.maximum(spectra, 1e-10, out=spectra) # Hack: fix NaN issues
s = (1 - np.cos(np.pi * spectra)) / 2.0
return s, wavelengths
def _generic_squash(numpy_func_name):
fn = getattr(np, numpy_func_name)
class _GenericSquash(Preprocessor):
name = 'squash:' + numpy_func_name
def apply(self, spectra, wavelengths):
return fn(spectra), wavelengths
_GenericSquash.__name__ = numpy_func_name.title() + 'Squash'
return _GenericSquash
TanhSquash = _generic_squash('tanh')
SqrtSquash = _generic_squash('sqrt')
LogSquash = _generic_squash('log')
class LibsNormalize(Preprocessor):
name = 'normalize:norm3'
def apply(self, spectra, wavelengths):
s = libs_norm3(spectra, wavelengths=wavelengths, copy=False)
return s, wavelengths
class CumulativeNormalize(Preprocessor):
name = 'normalize:cum'
def apply(self, spectra, wavelengths):
return cumulative_norm(spectra), wavelengths
class MinZeroNormalize(Preprocessor):
name = 'normalize:min'
def apply(self, spectra, wavelengths):
spectra -= spectra.min(axis=1)[:, None]
return spectra, wavelengths
class BandNormalize(Preprocessor):
name = 'normalize:band'
def __init__(self, loc):
self.loc = loc
def apply(self, spectra, wavelengths):
idx = np.searchsorted(wavelengths, self.loc)
a = max(0, idx - 2)
b = min(len(wavelengths), idx + 3)
x = spectra[:, a:b].max(axis=1)
spectra /= x[:,None]
return spectra, wavelengths
def _generic_norm(norm):
assert norm in ('max', 'l1', 'l2')
class _GenericNorm(Preprocessor):
name = 'normalize:' + norm
def apply(self, spectra, wavelengths):
return normalize(spectra, norm=norm, copy=False), wavelengths
_GenericNorm.__name__ = norm.title() + 'Normalize'
return _GenericNorm
MaxNormalize = _generic_norm('max')
L1Normalize = _generic_norm('l1')
L2Normalize = _generic_norm('l2')
class SavitzkyGolayDerivative(Preprocessor):
name = 'deriv'
arg_type = int
def __init__(self, window, order):
self.window = window
self.order = order
def apply(self, spectra, wavelengths):
assert not scipy.sparse.issparse(spectra)
d = scipy.signal.savgol_filter(spectra, self.window, self.order, deriv=1)
return d, wavelengths
class SavitzkyGolaySmooth(SavitzkyGolayDerivative):
name = 'smooth'
def apply(self, spectra, wavelengths):
assert not scipy.sparse.issparse(spectra)
d = scipy.signal.savgol_filter(spectra, self.window, self.order, deriv=0)
return d, wavelengths
class Offset(Preprocessor):
name = 'offset'
def __init__(self, x, y=0):
self.intensity_offset = y
self.wavelength_offset = x
def apply(self, spectra, wavelengths):
if self.intensity_offset != 0:
spectra += self.intensity_offset
if self.wavelength_offset != 0:
wavelengths += self.wavelength_offset
return spectra, wavelengths
| mit |
alpatania/MapperTools | inside_mapper.py | 1 | 8292 | # MapperTools -- a Python library that computes a mapper graph
# @author: Alice Patania <[email protected]>
#
# MapperTools/inside_mapper.py
# function called in the 2d mapper
from collections import defaultdict
import pandas as pd
import numpy as np
from .clusters import compute_bic, cluster_affinity, cluster_kmeans, cluster_DBSCAN, cluster_HDBSCAN, cluster_spectral, cluster_agglomerative
def inside_mapper_pb(idx, data_dict, level_idx, node_info, method, level, b, verb=False):
'''
Computes the partions and node_info dictionary entries for the new nodes in the graph.
Parameters
----------
idx : set
Set of indices in data_dict
data_dict: pandas.DataFame
level_idx: dict
dictionary containing the nodes constructed at each level
node_info: dict
dictionary containing the attributes of the nodes in the graph
method : str
Clustering method to be used. Can be {"kmeans","affinity","HDBSCAN"}.
Refer to sklearn documentation for more information on the differences between the methods.
level : tuple
level indices of the bins, int()
b : tuple
extreme values of the bins
Returns
-------
node_info, level_idx[level] udated versions of the dictionaries in input
'''
num_points = len(idx)
if num_points<5:
if verb: print "\x1b[31m less 5 points \x1b[0m"
if verb: print ("\tEstimated number of clusters: %d' "% num_points)
now=data_dict.ix[idx]
X=now.as_matrix()
node_colors = np.array(range(len(idx)))
num_colors = len(set(node_colors))
num_graph_nodes = len(node_info)
level_idx[level] = []
if level[0]>level[1]:
for k in set(node_colors):
curr_color = k + num_graph_nodes
level_idx[level].append(curr_color)
#node_info[curr_color]['level'] = level;
#node_info[curr_color]['fnval'] = b;
node_info[curr_color]['set'] = set(now.index[node_colors == k]);
#node_info[curr_color]['data'] = X[node_colors == k,:];
else:
for k in set(node_colors):
curr_color = k + num_graph_nodes
level_idx[level].append(curr_color)
#node_info[curr_color]['level'] = level;
#node_info[curr_color]['fnval'] = b;
node_info[curr_color]['set'] = set(now.index[node_colors == k]);
#node_info[curr_color]['data'] = X[node_colors == k,:];
return node_info,level_idx[level]
#-----------------
dam=0.8
#print idx
#print data_dict.index
now=data_dict.ix[idx]
X=now.as_matrix()
if method== "kmeans":
labels, n_clusters_=cluster_kmeans(now,range(1,5))
elif method== "affinity":
labels, n_clusters_=cluster_affinity(X)
elif method == "HDBSCAN":
labels, n_clusters_=cluster_HDBSCAN(X)
else:
raise ValueError('The chosen clustering method is not valid')
if n_clusters_==0:
if verb: print "\x1b[31m no cluster \x1b[0m"
return 0
#--------------------
if verb: print ("\tEstimated number of clusters: %d' "% n_clusters_)
node_colors = labels
num_colors = len(set(node_colors))
num_graph_nodes = len(node_info)
#--------------------
level_idx[level] = []
if level[0]>level[1]:
for k in set(node_colors):
curr_color = k + num_graph_nodes
level_idx[level].append(curr_color)
#node_info[curr_color]['level'] = level;
#node_info[curr_color]['fnval'] = b;
node_info[curr_color]['set'] = set(now.index[node_colors == k]);
#node_info[curr_color]['data'] = X[node_colors == k,:];
else:
for k in set(node_colors):
curr_color = k + num_graph_nodes
level_idx[level].append(curr_color)
#node_info[curr_color]['level'] = level;
#node_info[curr_color]['fnval'] = b;
node_info[curr_color]['set'] = set(now.index[node_colors == k]);
#node_info[curr_color]['data'] = X[node_colors == k,:];
return node_info,level_idx[level]
def creat_adja(node_info, level, adja, level_idx, direction="both"):
'''
Computes the edges of the graph connecting the new created nodes with the existing ones
Parameters
----------
node_info : defaultdict(dict)
dictionary containing the attributes of the nodes in the graph (output from inside_mapper or inside_mapper_pb)
level : tuple
level indices of the bins, int()
adja : defaultdict(dict)
the keys are tuples (i,j) where i,j is and existing edge in the graph. the values are the wights of the edges computed as the numer of elements in common between clusters i and j.
level_idx: dict
dictionary containing the nodes constructed at each level
direction : str
Can be: {"both","left","down"}.
"both" : runs creat_adja with `direction`="left". Runs again creat_adja with adja updated and `direction`="down".
"left" : computes the edges considering the overlap between bin (i,j) and (i,j-1).
"down" : computes the edges considering the overlap between bin (i,j) and (i-1,j).
Returns
-------
adja : defaultdict(dict)
updated versions of the dictionary in input
'''
if adja == 0:
return 0
if direction == "both":
result = creat_adja(node_info, level, adja.copy(), level_idx, "left")
matrix = creat_adja(node_info, level, result.copy(), level_idx, "down")
if matrix == 0:
return 0
else:
return matrix
elif direction == "left":
prev_lvl_idx = level_idx[(level[0],level[1] - 1)]
this_lvl_idx = level_idx[level]
elif direction == "down":
prev_lvl_idx = level_idx[(level[0] - 1, level[1])]
this_lvl_idx = level_idx[level]
for i1 in prev_lvl_idx:
for i2 in this_lvl_idx:
a = int(i1)
b = int(i2)
if len(node_info[a]['set'] & node_info[b]['set']) > 0:
adja[(a, b)] = len(node_info[a]['set'] & node_info[b]['set'])
adja[(b, a)] = len(node_info[a]['set'] & node_info[b]['set'])
return adja.copy()
def creat_adja_notefficient(node_info, adja, level_idx):
'''
Computes the edges of the graph connecting the new created nodes with the existing ones
Parameters
----------
node_info : defaultdict(dict)
dictionary containing the attributes of the nodes in the graph (output from inside_mapper or inside_mapper_pb)
adja : defaultdict(dict)
the keys are tuples (i,j) where i,j is and existing edge in the graph. the values are the wights of the edges computed as the numer of elements in common between clusters i and j.
level_idx: dict
dictionary containing the nodes constructed at each level
direction : str
Can be: {"both","left","down"}.
"both" : runs creat_adja with `direction`="left". Runs again creat_adja with adja updated and `direction`="down".
"left" : computes the edges considering the overlap between bin (i,j) and (i,j-1).
"down" : computes the edges considering the overlap between bin (i,j) and (i-1,j).
Returns
-------
adja : defaultdict(dict)
updated versions of the dictionary in input
'''
if adja == 0:
return 0
for i,j in level_idx.keys():
for i_compare in [i-1,i,i+1]:
if i_compare<0:
continue
for j_compare in [j-1,j,j+1]:
if j_compare<0:
continue
this_lvl_node = level_idx[(i,j)]
compare_lvl_node = level_idx[(i_compare,j_compare)]
for i1 in compare_lvl_node:
for i2 in this_lvl_node:
a = int(i1)
b = int(i2)
if len(node_info[a]['set'] & node_info[b]['set']) > 0:
adja[(a, b)] = len(node_info[a]['set'] & node_info[b]['set'])
adja[(b, a)] = len(node_info[a]['set'] & node_info[b]['set'])
return adja.copy() | gpl-3.0 |
Bismarrck/tensorflow | tensorflow/contrib/learn/python/learn/estimators/estimator_input_test.py | 46 | 13101 | # Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Tests for Estimator input."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import functools
import tempfile
import numpy as np
from tensorflow.python.training import training_util
from tensorflow.contrib.layers.python.layers import optimizers
from tensorflow.contrib.learn.python.learn import metric_spec
from tensorflow.contrib.learn.python.learn import models
from tensorflow.contrib.learn.python.learn.datasets import base
from tensorflow.contrib.learn.python.learn.estimators import _sklearn
from tensorflow.contrib.learn.python.learn.estimators import estimator
from tensorflow.contrib.learn.python.learn.estimators import model_fn
from tensorflow.contrib.metrics.python.ops import metric_ops
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import dtypes
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import data_flow_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.platform import test
from tensorflow.python.training import input as input_lib
from tensorflow.python.training import queue_runner_impl
_BOSTON_INPUT_DIM = 13
_IRIS_INPUT_DIM = 4
def boston_input_fn(num_epochs=None):
boston = base.load_boston()
features = input_lib.limit_epochs(
array_ops.reshape(
constant_op.constant(boston.data), [-1, _BOSTON_INPUT_DIM]),
num_epochs=num_epochs)
labels = array_ops.reshape(constant_op.constant(boston.target), [-1, 1])
return features, labels
def boston_input_fn_with_queue(num_epochs=None):
features, labels = boston_input_fn(num_epochs=num_epochs)
# Create a minimal queue runner.
fake_queue = data_flow_ops.FIFOQueue(30, dtypes.int32)
queue_runner = queue_runner_impl.QueueRunner(fake_queue,
[constant_op.constant(0)])
queue_runner_impl.add_queue_runner(queue_runner)
return features, labels
def iris_input_fn():
iris = base.load_iris()
features = array_ops.reshape(
constant_op.constant(iris.data), [-1, _IRIS_INPUT_DIM])
labels = array_ops.reshape(constant_op.constant(iris.target), [-1])
return features, labels
def iris_input_fn_labels_dict():
iris = base.load_iris()
features = array_ops.reshape(
constant_op.constant(iris.data), [-1, _IRIS_INPUT_DIM])
labels = {
'labels': array_ops.reshape(constant_op.constant(iris.target), [-1])
}
return features, labels
def boston_eval_fn():
boston = base.load_boston()
n_examples = len(boston.target)
features = array_ops.reshape(
constant_op.constant(boston.data), [n_examples, _BOSTON_INPUT_DIM])
labels = array_ops.reshape(
constant_op.constant(boston.target), [n_examples, 1])
return array_ops.concat([features, features],
0), array_ops.concat([labels, labels], 0)
def extract(data, key):
if isinstance(data, dict):
assert key in data
return data[key]
else:
return data
def linear_model_params_fn(features, labels, mode, params):
features = extract(features, 'input')
labels = extract(labels, 'labels')
assert mode in (model_fn.ModeKeys.TRAIN, model_fn.ModeKeys.EVAL,
model_fn.ModeKeys.INFER)
prediction, loss = (models.linear_regression_zero_init(features, labels))
train_op = optimizers.optimize_loss(
loss,
training_util.get_global_step(),
optimizer='Adagrad',
learning_rate=params['learning_rate'])
return prediction, loss, train_op
def linear_model_fn(features, labels, mode):
features = extract(features, 'input')
labels = extract(labels, 'labels')
assert mode in (model_fn.ModeKeys.TRAIN, model_fn.ModeKeys.EVAL,
model_fn.ModeKeys.INFER)
if isinstance(features, dict):
(_, features), = features.items()
prediction, loss = (models.linear_regression_zero_init(features, labels))
train_op = optimizers.optimize_loss(
loss,
training_util.get_global_step(),
optimizer='Adagrad',
learning_rate=0.1)
return prediction, loss, train_op
def linear_model_fn_with_model_fn_ops(features, labels, mode):
"""Same as linear_model_fn, but returns `ModelFnOps`."""
assert mode in (model_fn.ModeKeys.TRAIN, model_fn.ModeKeys.EVAL,
model_fn.ModeKeys.INFER)
prediction, loss = (models.linear_regression_zero_init(features, labels))
train_op = optimizers.optimize_loss(
loss,
training_util.get_global_step(),
optimizer='Adagrad',
learning_rate=0.1)
return model_fn.ModelFnOps(
mode=mode, predictions=prediction, loss=loss, train_op=train_op)
def logistic_model_no_mode_fn(features, labels):
features = extract(features, 'input')
labels = extract(labels, 'labels')
labels = array_ops.one_hot(labels, 3, 1, 0)
prediction, loss = (models.logistic_regression_zero_init(features, labels))
train_op = optimizers.optimize_loss(
loss,
training_util.get_global_step(),
optimizer='Adagrad',
learning_rate=0.1)
return {
'class': math_ops.argmax(prediction, 1),
'prob': prediction
}, loss, train_op
VOCAB_FILE_CONTENT = 'emerson\nlake\npalmer\n'
EXTRA_FILE_CONTENT = 'kermit\npiggy\nralph\n'
class EstimatorInputTest(test.TestCase):
def testContinueTrainingDictionaryInput(self):
boston = base.load_boston()
output_dir = tempfile.mkdtemp()
est = estimator.Estimator(model_fn=linear_model_fn, model_dir=output_dir)
boston_input = {'input': boston.data}
float64_target = {'labels': boston.target.astype(np.float64)}
est.fit(x=boston_input, y=float64_target, steps=50)
scores = est.evaluate(
x=boston_input,
y=float64_target,
metrics={
'MSE': metric_ops.streaming_mean_squared_error
})
del est
# Create another estimator object with the same output dir.
est2 = estimator.Estimator(model_fn=linear_model_fn, model_dir=output_dir)
# Check we can evaluate and predict.
scores2 = est2.evaluate(
x=boston_input,
y=float64_target,
metrics={
'MSE': metric_ops.streaming_mean_squared_error
})
self.assertAllClose(scores2['MSE'], scores['MSE'])
predictions = np.array(list(est2.predict(x=boston_input)))
other_score = _sklearn.mean_squared_error(predictions,
float64_target['labels'])
self.assertAllClose(other_score, scores['MSE'])
def testBostonAll(self):
boston = base.load_boston()
est = estimator.SKCompat(estimator.Estimator(model_fn=linear_model_fn))
float64_labels = boston.target.astype(np.float64)
est.fit(x=boston.data, y=float64_labels, steps=100)
scores = est.score(
x=boston.data,
y=float64_labels,
metrics={
'MSE': metric_ops.streaming_mean_squared_error
})
predictions = np.array(list(est.predict(x=boston.data)))
other_score = _sklearn.mean_squared_error(predictions, boston.target)
self.assertAllClose(scores['MSE'], other_score)
self.assertTrue('global_step' in scores)
self.assertEqual(100, scores['global_step'])
def testBostonAllDictionaryInput(self):
boston = base.load_boston()
est = estimator.Estimator(model_fn=linear_model_fn)
boston_input = {'input': boston.data}
float64_target = {'labels': boston.target.astype(np.float64)}
est.fit(x=boston_input, y=float64_target, steps=100)
scores = est.evaluate(
x=boston_input,
y=float64_target,
metrics={
'MSE': metric_ops.streaming_mean_squared_error
})
predictions = np.array(list(est.predict(x=boston_input)))
other_score = _sklearn.mean_squared_error(predictions, boston.target)
self.assertAllClose(other_score, scores['MSE'])
self.assertTrue('global_step' in scores)
self.assertEqual(scores['global_step'], 100)
def testIrisAll(self):
iris = base.load_iris()
est = estimator.SKCompat(
estimator.Estimator(model_fn=logistic_model_no_mode_fn))
est.fit(iris.data, iris.target, steps=100)
scores = est.score(
x=iris.data,
y=iris.target,
metrics={
('accuracy', 'class'): metric_ops.streaming_accuracy
})
predictions = est.predict(x=iris.data)
predictions_class = est.predict(x=iris.data, outputs=['class'])['class']
self.assertEqual(predictions['prob'].shape[0], iris.target.shape[0])
self.assertAllClose(predictions['class'], predictions_class)
self.assertAllClose(predictions['class'],
np.argmax(predictions['prob'], axis=1))
other_score = _sklearn.accuracy_score(iris.target, predictions['class'])
self.assertAllClose(scores['accuracy'], other_score)
self.assertTrue('global_step' in scores)
self.assertEqual(100, scores['global_step'])
def testIrisAllDictionaryInput(self):
iris = base.load_iris()
est = estimator.Estimator(model_fn=logistic_model_no_mode_fn)
iris_data = {'input': iris.data}
iris_target = {'labels': iris.target}
est.fit(iris_data, iris_target, steps=100)
scores = est.evaluate(
x=iris_data,
y=iris_target,
metrics={
('accuracy', 'class'): metric_ops.streaming_accuracy
})
predictions = list(est.predict(x=iris_data))
predictions_class = list(est.predict(x=iris_data, outputs=['class']))
self.assertEqual(len(predictions), iris.target.shape[0])
classes_batch = np.array([p['class'] for p in predictions])
self.assertAllClose(classes_batch,
np.array([p['class'] for p in predictions_class]))
self.assertAllClose(classes_batch,
np.argmax(
np.array([p['prob'] for p in predictions]), axis=1))
other_score = _sklearn.accuracy_score(iris.target, classes_batch)
self.assertAllClose(other_score, scores['accuracy'])
self.assertTrue('global_step' in scores)
self.assertEqual(scores['global_step'], 100)
def testIrisInputFn(self):
iris = base.load_iris()
est = estimator.Estimator(model_fn=logistic_model_no_mode_fn)
est.fit(input_fn=iris_input_fn, steps=100)
_ = est.evaluate(input_fn=iris_input_fn, steps=1)
predictions = list(est.predict(x=iris.data))
self.assertEqual(len(predictions), iris.target.shape[0])
def testIrisInputFnLabelsDict(self):
iris = base.load_iris()
est = estimator.Estimator(model_fn=logistic_model_no_mode_fn)
est.fit(input_fn=iris_input_fn_labels_dict, steps=100)
_ = est.evaluate(
input_fn=iris_input_fn_labels_dict,
steps=1,
metrics={
'accuracy':
metric_spec.MetricSpec(
metric_fn=metric_ops.streaming_accuracy,
prediction_key='class',
label_key='labels')
})
predictions = list(est.predict(x=iris.data))
self.assertEqual(len(predictions), iris.target.shape[0])
def testTrainInputFn(self):
est = estimator.Estimator(model_fn=linear_model_fn)
est.fit(input_fn=boston_input_fn, steps=1)
_ = est.evaluate(input_fn=boston_eval_fn, steps=1)
def testPredictInputFn(self):
est = estimator.Estimator(model_fn=linear_model_fn)
boston = base.load_boston()
est.fit(input_fn=boston_input_fn, steps=1)
input_fn = functools.partial(boston_input_fn, num_epochs=1)
output = list(est.predict(input_fn=input_fn))
self.assertEqual(len(output), boston.target.shape[0])
def testPredictInputFnWithQueue(self):
est = estimator.Estimator(model_fn=linear_model_fn)
boston = base.load_boston()
est.fit(input_fn=boston_input_fn, steps=1)
input_fn = functools.partial(boston_input_fn_with_queue, num_epochs=2)
output = list(est.predict(input_fn=input_fn))
self.assertEqual(len(output), boston.target.shape[0] * 2)
def testPredictConstInputFn(self):
est = estimator.Estimator(model_fn=linear_model_fn)
boston = base.load_boston()
est.fit(input_fn=boston_input_fn, steps=1)
def input_fn():
features = array_ops.reshape(
constant_op.constant(boston.data), [-1, _BOSTON_INPUT_DIM])
labels = array_ops.reshape(constant_op.constant(boston.target), [-1, 1])
return features, labels
output = list(est.predict(input_fn=input_fn))
self.assertEqual(len(output), boston.target.shape[0])
if __name__ == '__main__':
test.main()
| apache-2.0 |
rstoneback/pysat | demo/cnofs_vefi_dc_b_orbit_plots.py | 2 | 3127 | """
Demonstrates iterating over an instrument data set by orbit and plotting the
results.
"""
import os
import pysat
import matplotlib.pyplot as plt
# set the directory to save plots to
results_dir = ''
# select vefi dc magnetometer data, use longitude to determine where
# there are changes in the orbit (local time info not in file)
orbit_info = {'index': 'longitude', 'kind': 'longitude'}
vefi = pysat.Instrument(platform='cnofs', name='vefi', tag='dc_b',
clean_level=None, orbit_info=orbit_info)
# set limits on dates analysis will cover, inclusive
start = pysat.datetime(2010, 5, 9)
stop = pysat.datetime(2010, 5, 12)
# if there is no vefi dc magnetometer data on your system, then run command
# below where start and stop are pandas datetimes (from above)
# pysat will automatically register the addition of this data at the end of
# download
vefi.download(start, stop)
# leave bounds unassigned to cover the whole dataset (comment out lines below)
vefi.bounds = (start, stop)
for orbit_count, vefi in enumerate(vefi.orbits):
# for each loop pysat puts a copy of the next available orbit into
# vefi.data
# changing .data at this level does not alter other orbits
# reloading the same orbit will erase any changes made
# satellite data can have time gaps, which leads to plots
# with erroneous lines connecting measurements on both sides of the gap
# command below fills in any data gaps using a 1-second cadence with NaNs
# see pandas documentation for more info
vefi.data = vefi.data.resample('1S', fill_method='ffill', limit=1,
label='left')
f, ax = plt.subplots(7, sharex=True, figsize=(8.5, 11))
ax[0].plot(vefi['longitude'], vefi['B_flag'])
ax[0].set_title(vefi.data.index[0].ctime() +
' - '+vefi.data.index[-1].ctime())
ax[0].set_ylabel('Interp. Flag')
ax[0].set_ylim((0, 2))
ax[1].plot(vefi['longitude'], vefi['B_north'])
ax[1].set_title(vefi.meta['B_north'].long_name)
ax[1].set_ylabel(vefi.meta['B_north'].units)
ax[2].plot(vefi['longitude'], vefi['B_up'])
ax[2].set_title(vefi.meta['B_up'].long_name)
ax[2].set_ylabel(vefi.meta['B_up'].units)
ax[3].plot(vefi['longitude'], vefi['B_west'])
ax[3].set_title(vefi.meta['B_west'].long_name)
ax[3].set_ylabel(vefi.meta['B_west'].units)
ax[4].plot(vefi['longitude'], vefi['dB_mer'])
ax[4].set_title(vefi.meta['dB_mer'].long_name)
ax[4].set_ylabel(vefi.meta['dB_mer'].units)
ax[5].plot(vefi['longitude'], vefi['dB_par'])
ax[5].set_title(vefi.meta['dB_par'].long_name)
ax[5].set_ylabel(vefi.meta['dB_par'].units)
ax[6].plot(vefi['longitude'], vefi['dB_zon'])
ax[6].set_title(vefi.meta['dB_zon'].long_name)
ax[6].set_ylabel(vefi.meta['dB_zon'].units)
ax[6].set_xlabel(vefi.meta['longitude'].long_name)
ax[6].set_xticks([0, 60, 120, 180, 240, 300, 360])
ax[6].set_xlim((0, 360))
f.tight_layout()
plt.savefig(os.path.join(results_dir,
'orbit_{num:05}.png').format(num=orbit_count))
plt.close()
| bsd-3-clause |
mdbartos/RIPS | network_model.py | 1 | 11127 | import numpy as np
import pandas as pd
import geopandas as gpd
class network_model():
def __init__(self, lines, subs, util, gen, bbox=None, loads=None, pop_dens=None):
self.lines = gpd.read_file(lines)
self.subs = gpd.read_file(subs)
self.util = gpd.read_file(util)
self.gen = gpd.read_file(gen)
if loads is None:
if pop_dens is not None:
loads = self.partition_loads(self.construct_voronoi(), pop_dens)
if edges is None:
edges = self.line_to_sub()
if node_gen is None:
node_gen = self.gen_to_sub()
self.net = pd.concat([loads.groupby('SUB_ID').sum()['summer_loa'], gen.groupby('SUB_ID').sum()['S_CAP_MW'].fillna(0)], axis=1, join='outer')[['summer_loa', 'S_CAP_MW']].fillna(0)
self.net = self.net['S_CAP_MW'] - self.net['summer_loa']
if bbox is not None:
self.nodes, self.edges, self.loads = self.set_bbox(*bbox)
# Set up graph
self.G = nx.Graph()
for i in self.loads.index:
self.G.add_node(i, load=self.loads[i])
for i in self.edges.index:
row = self.edges.loc[i]
self.G.add_edge(*tuple(row[['SUB_1', 'SUB_2']].astype(int).values),
tot_kv=row['TOT_CAP_KV'],
num_lines=int(row['NUM_LINES']),
length=row['LENGTH'])
def construct_voronoi(self):
from scipy import spatial
from geopandas import tools
from shapely import geometry
util = self.util
sub = self.sub
# Fix utility service areas with invalid geometry
invalid_util = util[~util['geometry'].apply(lambda x: x.is_valid)]
util.loc[invalid_util.index, 'geometry'] = util.loc[invalid_util.index, 'geometry'].apply(lambda x: x.buffer(0))
# Spatially join substations with utility service territories
sub_util = tools.sjoin(sub, util, op='within', how='left')
# Construct voronoi polygons for each substation
sub_xy = np.vstack(sub['geometry'].apply(lambda u: np.concatenate(u.xy)).values)
vor = spatial.Voronoi(sub_xy)
reg, vert = self.voronoi_finite_polygons_2d(vor,1)
# Convert voronoi diagram to polygons and insert into GeoDataFrame
v_poly = gpd.GeoSeries(pd.Series(reg).apply(lambda x: geometry.Polygon(vert[x])))
v_gdf = gpd.GeoDataFrame(pd.concat([sub.drop('geometry', axis=1), v_poly], axis=1)).rename(columns={0:'geometry'})
v_gdf.crs = sub.crs
# Spatially join utility service areas with voronoi polygons
j = tools.sjoin(util, v_gdf, op='intersects')
j['right_geom'] = j['UNIQUE_ID_right'].map(v_gdf.set_index('UNIQUE_ID')['geometry'])
j = j.dropna(subset=['geometry', 'right_geom']).set_index('UNIQUE_ID_left')
# Clip voronoi polygons to utility service area
j_inter = j.apply(lambda x: x['geometry'].intersection(x['right_geom']), axis=1)
# Create output GeoDataFrame with relevant fields
outdf = gpd.GeoDataFrame(pd.concat([j[['UNIQUE_ID_right', 'SUMMERPEAK', 'WINTERPEAK']].reset_index(), j_inter.reset_index()[0]], axis=1), crs=sub.crs).rename(columns={0:'geometry', 'UNIQUE_ID_left':'UTIL_ID', 'UNIQUE_ID_right':'SUB_ID'})
return outdf
def partition_loads(self, vor, pop_dens):
# voronoi_stats.shp
import rasterstats
import rasterio
#### FOR ICLUS, BEST PROJECTION IS EPSG 5070: NAD83/CONUS ALBERS
# vor = '/home/akagi/voronoi_intersect.shp'
# pop_dens = '/home/akagi/Desktop/rastercopy.tif'
# gdf = gpd.GeoDataFrame.from_file(vor)
# rast = rasterio.open(pop_dens)
if isinstance(vor, str):
gdf = gpd.read_file(vor)
if isinstance(pop_dens, str):
pop_dens = rasterio.open(pop_dens)
zones = gdf['geometry'].to_crs(pop_dens.crs)
rstats = pd.DataFrame.from_dict(rasterstats.zonal_stats(zones, pop_dens, stats=['sum', 'mean']))
util_stats = pd.concat([rstats, gdf], join='inner', axis=1)
tot_util = util_stats.groupby('UTIL_ID').sum()['sum']
util_stats['util_tot'] = util_stats['UTIL_ID'].map(tot_util)
util_stats['load_frac'] = util_stats['sum']/util_stats['util_tot']
util_stats['summer_load'] = util_stats['SUMMERPEAK']*util_stats['load_frac']
util_stats['winter_load'] = util_stats['WINTERPEAK']*util_stats['load_frac']
return util_stats
def gen_to_sub(self):
from shapely import geometry
from scipy import spatial
sub = self.sub
gen = self.gen
# Find nearest neighbors
tree = spatial.cKDTree(np.vstack(sub.geometry.apply(lambda x: x.coords[0]).values))
node_query = tree.query(np.vstack(gen.geometry.apply(lambda x: x.coords[0]).values))
crosswalk = pd.DataFrame(np.column_stack([gen[['UNIQUE_ID', 'S_CAP_MW']].values, s.iloc[node_query[1]]['UNIQUE_ID'].values.astype(int)]), columns=['GEN_ID', 'S_CAP_MW', 'SUB_ID'])
crosswalk = crosswalk[['GEN_ID', 'SUB_ID', 'S_CAP_MW']]
return crosswalk
#gen_to_sub_static.csv
def line_to_sub(self):
from shapely import geometry
from scipy import spatial
t = self.lines
s = self.subs
# Extract start and end nodes in networkK
start = t.geometry[t.geometry.type=='LineString'].apply(lambda x: np.array([x.xy[0][0], x.xy[1][0]])).append(t.geometry[t.geometry.type=='MultiLineString'].apply(lambda x: np.hstack([i.xy for i in x])[:,0])).sort_index()
end = t.geometry[t.geometry.type=='LineString'].apply(lambda x: np.array([x.xy[0][-1], x.xy[1][-1]])).append(t.geometry[t.geometry.type=='MultiLineString'].apply(lambda x: np.hstack([i.xy for i in x])[:,-1])).sort_index()
# Find nearest neighbors
tree = spatial.cKDTree(np.vstack(s.geometry.apply(lambda x: x.coords[0]).values))
start_node_query = tree.query(np.vstack(start.values))
end_node_query = tree.query(np.vstack(end.values))
# Create crosswalk table
crosswalk = pd.DataFrame(np.column_stack([t[['UNIQUE_ID', 'TOT_CAP_KV', 'NUM_LINES', 'Shape_Leng']].values, s.iloc[start_node_query[1]][['UNIQUE_ID', 'NAME']].values, start_node_query[0], s.iloc[end_node_query[1]][['UNIQUE_ID', 'NAME']].values, end_node_query[0]]), columns=['TRANS_ID', 'TOT_CAP_KV', 'NUM_LINES', 'LENGTH', 'SUB_1', 'NAME_1', 'ERR_1', 'SUB_2', 'NAME_2', 'ERR_2'])
crosswalk = crosswalk[['TRANS_ID', 'SUB_1', 'SUB_2', 'NAME_1', 'NAME_2', 'ERR_1', 'ERR_2', 'TOT_CAP_KV', 'NUM_LINES', 'LENGTH']]
return crosswalk
#edges.csv
def set_bbox(self, xmin, ymin, xmax, ymax):
t = self.lines
s = self.subs
edges = self.edges
net = self.net
bbox = tuple(xmin, ymin, xmax, ymax)
bbox_poly = shapely.geometry.MultiPoint(np.vstack(np.dstack(np.meshgrid(*np.hsplit(bbox, 2)))).tolist()).convex_hull
bbox_lines = t[t.intersects(bbox_poly)]['UNIQUE_ID'].astype(int).values
bbox_edges = edges[edges['TRANS_ID'].isin(bbox_lines)]
bbox_edges = bbox_edges[bbox_edges['SUB_1'] != bbox_edges['SUB_2']]
bbox_nodes = np.unique(bbox_edges[['SUB_1', 'SUB_2']].values.astype(int).ravel())
# Outer lines
edgesubs = pd.merge(t[t.intersects(bbox_poly.boundary)], edges, left_on='UNIQUE_ID', right_on='TRANS_ID')[['SUB_1_y', 'SUB_2_y']].values.ravel().astype(int)
# Nodes just outside of bbox (entering)
outer_nodes = np.unique(edgesubs[~np.in1d(edgesubs, s[s.within(bbox_poly)]['UNIQUE_ID'].values.astype(int))])
weights = s.loc[s['UNIQUE_ID'].astype(int).isin(edgesubs[~np.in1d(edgesubs, s[s.within(bbox_poly)]['UNIQUE_ID'].values.astype(int))])].set_index('UNIQUE_ID')['MAX_VOLT'].sort_index()
transfers = -net[bbox_nodes].sum()*(weights/weights.sum())
bbox_loads = net[bbox_nodes] + transfers.reindex(bbox_nodes).fillna(0)
return bbox_nodes, bbox_edges, bbox_loads
def voronoi_finite_polygons_2d(vor, radius=None):
"""
Reconstruct infinite voronoi regions in a 2D diagram to finite
regions.
Parameters
----------
vor : Voronoi
Input diagram
radius : float, optional
Distance to 'points at infinity'.
Returns
-------
regions : list of tuples
Indices of vertices in each revised Voronoi regions.
vertices : list of tuples
Coordinates for revised Voronoi vertices. Same as coordinates
of input vertices, with 'points at infinity' appended to the
end.
"""
if vor.points.shape[1] != 2:
raise ValueError("Requires 2D input")
new_regions = []
new_vertices = vor.vertices.tolist()
center = vor.points.mean(axis=0)
if radius is None:
radius = vor.points.ptp().max()*2
# Construct a map containing all ridges for a given point
all_ridges = {}
for (p1, p2), (v1, v2) in zip(vor.ridge_points, vor.ridge_vertices):
all_ridges.setdefault(p1, []).append((p2, v1, v2))
all_ridges.setdefault(p2, []).append((p1, v1, v2))
# Reconstruct infinite regions
for p1, region in enumerate(vor.point_region):
vertices = vor.regions[region]
if all([v >= 0 for v in vertices]):
# finite region
new_regions.append(vertices)
continue
# reconstruct a non-finite region
ridges = all_ridges[p1]
new_region = [v for v in vertices if v >= 0]
for p2, v1, v2 in ridges:
if v2 < 0:
v1, v2 = v2, v1
if v1 >= 0:
# finite ridge: already in the region
continue
# Compute the missing endpoint of an infinite ridge
t = vor.points[p2] - vor.points[p1] # tangent
t /= np.linalg.norm(t)
n = np.array([-t[1], t[0]]) # normal
midpoint = vor.points[[p1, p2]].mean(axis=0)
direction = np.sign(np.dot(midpoint - center, n)) * n
far_point = vor.vertices[v2] + direction * radius
new_region.append(len(new_vertices))
new_vertices.append(far_point.tolist())
# sort region counterclockwise
vs = np.asarray([new_vertices[v] for v in new_region])
c = vs.mean(axis=0)
angles = np.arctan2(vs[:,1] - c[1], vs[:,0] - c[0])
new_region = np.array(new_region)[np.argsort(angles)]
# finish
new_regions.append(new_region.tolist())
return new_regions, np.asarray(new_vertices)
| mit |
supriyantomaftuh/innstereo | innstereo/rotation_dialog.py | 1 | 22040 | #!/usr/bin/python3
"""
This module stores the RotationDialog class which controls the rotation dialog.
The module contains only the RotationDialog class. It controls the behaviour
of the data-rotation dialog.
"""
from gi.repository import Gtk
from matplotlib.figure import Figure
from matplotlib.gridspec import GridSpec
from matplotlib.backends.backend_gtk3cairo import (FigureCanvasGTK3Cairo
as FigureCanvas)
import numpy as np
import mplstereonet
import os
class RotationDialog(object):
"""
This class controls the appearance and signals of the data-rotation dialog.
This class pulls the rotation dialog from the Glade file, intilizes the
widgets and has methods for the signals defined in Glade.
"""
def __init__(self, main_window, settings, data, add_layer_dataset, add_feature, redraw_main):
"""
Initializes the RotationDialog class.
Requires the main_window object, the settings object (PlotSettings
class) and the data rows to initialize. All the necessary widgets are
loaded from the Glade file. A matplotlib figure is set up and added
to the scrolledwindow. Two axes are set up that show the original and
rotated data.
"""
self.builder = Gtk.Builder()
script_dir = os.path.dirname(__file__)
rel_path = "gui_layout.glade"
abs_path = os.path.join(script_dir, rel_path)
self.builder.add_objects_from_file(abs_path,
("dialog_rotation", "adjustment_rotation_dipdir",
"adjustment_rotation_dip", "adjustment_rotation_angle"))
self.dialog = self.builder.get_object("dialog_rotation")
self.dialog.set_transient_for(main_window)
self.settings = settings
self.data = data
self.trans = self.settings.get_transform()
self.add_layer_dataset = add_layer_dataset
self.add_feature = add_feature
self.redraw_main = redraw_main
self.adjustment_rotation_dipdir = self.builder.get_object("adjustment_rotation_dipdir")
self.adjustment_rotation_dip = self.builder.get_object("adjustment_rotation_dip")
self.adjustment_rotation_angle = self.builder.get_object("adjustment_rotation_angle")
self.spinbutton_rotation_dipdir = self.builder.get_object("spinbutton_rotation_dipdir")
self.spinbutton_rotation_dip = self.builder.get_object("spinbutton_rotation_dip")
self.spinbutton_rotation_angle = self.builder.get_object("spinbutton_rotation_angle")
self.scrolledwindow_rotate = self.builder.get_object("scrolledwindow_rotate")
self.fig = Figure(dpi=self.settings.get_pixel_density())
self.canvas = FigureCanvas(self.fig)
self.scrolledwindow_rotate.add_with_viewport(self.canvas)
gridspec = GridSpec(1, 2)
original_sp = gridspec.new_subplotspec((0, 0),
rowspan=1, colspan=1)
rotated_sp = gridspec.new_subplotspec((0, 1),
rowspan=1, colspan=1)
self.original_ax = self.fig.add_subplot(original_sp,
projection=self.settings.get_projection())
self.rotated_ax = self.fig.add_subplot(rotated_sp,
projection=self.settings.get_projection())
self.canvas.draw()
self.redraw_plot()
self.dialog.show_all()
self.builder.connect_signals(self)
def run(self):
"""
Runs the dialog.
Called from the MainWindow class. Initializes and shows the dialog.
"""
self.dialog.run()
def on_dialog_rotation_destroy(self, widget):
"""
Hides the dialog on destroy.
When the dialog is destroyed it is hidden.
"""
self.dialog.hide()
def on_button_cancel_rotation_clicked(self, button):
"""
Exits the rotation dialog and makes no changes to the project.
When the user clicks on Cancel the dialog is hidden, and no changes
are made to the project structure.
"""
self.dialog.hide()
def on_button_apply_rotate_clicked(self, button):
"""
Adds the rotated layers to the project.
When the user clicks on "apply the rotation", the rotated data is
added to the project as new datasets.
"""
raxis_dipdir = self.spinbutton_rotation_dipdir.get_value()
raxis_dip = self.spinbutton_rotation_dip.get_value()
raxis = [raxis_dipdir, raxis_dip]
raxis_angle = self.spinbutton_rotation_angle.get_value()
for lyr_obj in self.data:
lyr_type = lyr_obj.get_layer_type()
lyr_store = lyr_obj.get_data_treestore()
if lyr_type == "plane":
dipdir_org, dips_org, dipdir_lst, dips_lst, strat, dipdir_az = \
self.parse_plane(lyr_store, raxis, raxis_angle)
store, new_lyr_obj = self.add_layer_dataset("plane")
for dipdir, dip, strt in zip(dipdir_az, dips_lst, strat):
self.add_feature("plane", store, dipdir, dip, strt)
elif lyr_type == "line":
ldipdir_org, ldips_org, ldipdir_lst, ldips_lst, sense = \
self.parse_line(lyr_store, raxis, raxis_angle)
store, new_lyr_obj = self.add_layer_dataset("line")
for dipdir, dip, sns in zip(ldipdir_lst, ldips_lst, sense):
self.add_feature("line", store, dipdir, dip, sns)
elif lyr_type == "smallcircle":
ldipdir_org, ldips_org, ldipdir_lst, ldips_lst, angle = \
self.parse_line(lyr_store, raxis, raxis_angle)
store, new_lyr_obj = self.add_layer_dataset("smallcircle")
for dipdir, dip, ang in zip(ldipdir_lst, ldips_lst, angle):
self.add_feature("smallcircle", store, dipdir, dip, ang)
elif lyr_type == "faultplane":
rtrn = self.parse_faultplane(lyr_store, raxis, raxis_angle)
dipdir_org, dips_org, dipdir_lst, dips_lst, ldipdir_org, \
ldips_org, ldipdir_lst, ldips_lst, sense, dipdir_az = rtrn[0], \
rtrn[1], rtrn[2], rtrn[3], rtrn[4], rtrn[5], rtrn[6], rtrn[7], \
rtrn[8], rtrn[9]
store, new_lyr_obj = self.add_layer_dataset("faultplane")
for dipdir, dip, ldipdir, ldip, sns in zip(dipdir_az, dips_lst,
ldipdir_lst, ldips_lst, sense):
self.add_feature("faultplane", store, dipdir, dip, ldipdir, ldip, sns)
new_lyr_obj.set_properties(lyr_obj.get_properties())
self.dialog.hide()
self.redraw_main()
def on_spinbutton_rotation_dipdir_value_changed(self, spinbutton):
"""
Redraws the plot.
When the value of the spinbutton is changed, the redraw_plot method
is called, which rotates the data according to the new setting.
"""
self.redraw_plot()
def on_spinbutton_rotation_dip_value_changed(self, spinbutton):
"""
Redraws the plot.
When the value of the spinbutton is changed, the redraw_plot method
is called, which rotates the data according to the new setting.
"""
self.redraw_plot()
def on_spinbutton_rotation_angle_value_changed(self, spinbutton):
"""
Redraws the plot.
When the value of the spinbutton is changed, the redraw_plot method
is called, which rotates the data according to the new setting.
"""
self.redraw_plot()
def convert_lonlat_to_dipdir(self, lon, lat):
"""
Converts lat-lon data to dip-direction and dip.
Expects a longitude and a latitude value. The measurment is forward
transformed into stereonet-space. Then the azimut (dip-direction) and
diping angle are calculated. Returns two values: dip-direction and dip.
"""
#The longitude and latitude have to be forward-transformed to get
#the corect azimuth angle
xy = np.array([[lon, lat]])
xy_trans = self.trans.transform(xy)
x = float(xy_trans[0,0:1])
y = float(xy_trans[0,1:2])
alpha = np.arctan2(x, y)
alpha_deg = np.degrees(alpha)
if alpha_deg < 0:
alpha_deg += 360
#Longitude and Latitude don't need to be converted for rotation.
#The correct dip is the array[1] value once the vector has been
#rotated in north-south position.
array = mplstereonet.stereonet_math._rotate(np.degrees(lon),
np.degrees(lat),
alpha_deg * (-1))
gamma = float(array[1])
gamma_deg = 90 - np.degrees(gamma)
#If the longitude is larger or small than pi/2 the measurment lies
#on the upper hemisphere and needs to be corrected.
if lon > (np.pi / 2) or lon < (-np.pi / 2):
alpha_deg = alpha_deg + 180
return alpha_deg, gamma_deg
def rotate_data(self, raxis, raxis_angle, dipdir, dip):
"""
Rotates a measurment around a rotation axis a set number of degrees.
Expects a rotation-axis, a rotation-angle, a dip-direction and a
dip angle. The measurement is converted to latlot and then passed
to the mplstereonet rotate function.
"""
lonlat = mplstereonet.line(dip, dipdir)
#Rotation around x-axis until rotation-axis azimuth is east-west
rot1 = (90 - raxis[0])
lon1 = np.degrees(lonlat[0])
lat1 = np.degrees(lonlat[1])
lon_rot1, lat_rot1 = mplstereonet.stereonet_math._rotate(lon1, lat1,
theta=rot1, axis="x")
#Rotation around z-axis until rotation-axis dip is east-west
rot2 = -(90 - raxis[1])
lon2 = np.degrees(lon_rot1)
lat2 = np.degrees(lat_rot1)
lon_rot2, lat_rot2 = mplstereonet.stereonet_math._rotate(lon2, lat2,
theta=rot2, axis="z")
#Rotate around the x-axis for the specified rotation:
rot3 = raxis_angle
lon3 = np.degrees(lon_rot2)
lat3 = np.degrees(lat_rot2)
lon_rot3, lat_rot3 = mplstereonet.stereonet_math._rotate(lon3, lat3,
theta=rot3, axis="x")
#Undo the z-axis rotation
rot4 = -rot2
lon4 = np.degrees(lon_rot3)
lat4 = np.degrees(lat_rot3)
lon_rot4, lat_rot4 = mplstereonet.stereonet_math._rotate(lon4, lat4,
theta=rot4, axis="z")
#Undo the x-axis rotation
rot5 = -rot1
lon5 = np.degrees(lon_rot4)
lat5 = np.degrees(lat_rot4)
lon_rot5, lat_rot5 = mplstereonet.stereonet_math._rotate(lon5, lat5,
theta=rot5, axis="x")
dipdir5, dip5 = self.convert_lonlat_to_dipdir(lon_rot5, lat_rot5)
return dipdir5, dip5
def parse_plane(self, lyr_store, raxis, raxis_angle):
"""
Parses and rotates data of a plane layer.
Expects a TreeStore of a layer, the rotation axis and the
angle of rotation. The method returns each column unrotated and rotated.
"""
dipdir_org = []
dips_org = []
dipdir_lst = []
dips_lst = []
dipdir_az = []
strat = []
for row in lyr_store:
dipdir_org.append(row[0] - 90)
dips_org.append(row[1])
#Planes and faultplanes are rotated using their poles
dipdir, dip = self.rotate_data(raxis, raxis_angle, row[0] + 180,
90 - row[1])
dipdir_lst.append(dipdir + 90)
dipdir_az.append(dipdir + 180)
dips_lst.append(90 - dip)
strat.append(row[2])
return dipdir_org, dips_org, dipdir_lst, dips_lst, strat, dipdir_az
def parse_line(self, lyr_store, raxis, raxis_angle):
"""
Parses and rotates data of a linear or smallcircle layer.
Expects a TreeStore of a layer, the rotation axis and the
angle of rotation. The method returns each column unrotated and rotated.
"""
ldipdir_org = []
ldips_org = []
ldipdir_lst = []
ldips_lst = []
third_col = []
for row in lyr_store:
ldipdir_org.append(row[0])
ldips_org.append(row[1])
ldipdir, ldip = self.rotate_data(raxis, raxis_angle, row[0], row[1])
ldipdir_lst.append(ldipdir)
ldips_lst.append(ldip)
third_col.append(row[2])
return ldipdir_org, ldips_org, ldipdir_lst, ldips_lst, third_col
def parse_faultplane(self, lyr_store, raxis, raxis_angle):
"""
Parses and rotates data of a faultplane layer.
Expects a TreeStore of a faultplane layer, the rotation axis and the
angle of rotation. The method returns each column unrotated and rotated.
"""
dipdir_org = []
dips_org = []
dipdir_lst = []
dips_lst = []
ldipdir_org = []
ldips_org = []
ldipdir_lst = []
ldips_lst = []
dipdir_az = []
sense = []
for row in lyr_store:
dipdir_org.append(row[0] - 90)
dips_org.append(row[1])
#Planes and faultplanes are rotated using their poles
dipdir, dip = self.rotate_data(raxis, raxis_angle, row[0] + 180,
90 - row[1])
dipdir_lst.append(dipdir + 90)
dipdir_az.append(dipdir + 270)
dips_lst.append(90 - dip)
ldipdir_org.append(row[2])
ldips_org.append(row[3])
ldipdir, ldip = self.rotate_data(raxis, raxis_angle, row[2], row[3])
ldipdir_lst.append(ldipdir)
ldips_lst.append(ldip)
sense.append(row[4])
return (dipdir_org, dips_org, dipdir_lst, dips_lst, ldipdir_org,
ldips_org, ldipdir_lst, ldips_lst, sense, dipdir_az)
def redraw_plot(self):
"""
Redraws the plot using the current settings of the dialog's spinbuttons.
This method clears the two axes and adds the annotations. The current
values of the rotation axis and rotation angle spinbuttons are
retrieved. The data is parsed, and the features are then drawn.
In addition the rotation-axis is drawn.
"""
self.original_ax.cla()
self.rotated_ax.cla()
self.original_ax.grid(False)
self.rotated_ax.grid(False)
self.original_ax.set_azimuth_ticks([0], labels=["N"])
self.rotated_ax.set_azimuth_ticks([0], labels=["N"])
bar = 0.05
self.original_ax.annotate("", xy = (-bar, 0),
xytext = (bar, 0),
xycoords = "data",
arrowprops = dict(arrowstyle = "-",
connectionstyle = "arc3"))
self.original_ax.annotate("", xy = (0, -bar),
xytext = (0, bar),
xycoords = "data",
arrowprops = dict(arrowstyle = "-",
connectionstyle = "arc3"))
self.rotated_ax.annotate("", xy = (-bar, 0),
xytext = (bar, 0),
xycoords = "data",
arrowprops = dict(arrowstyle = "-",
connectionstyle = "arc3"))
self.rotated_ax.annotate("", xy = (0, -bar),
xytext = (0, bar),
xycoords = "data",
arrowprops = dict(arrowstyle = "-",
connectionstyle = "arc3"))
raxis_dipdir = self.spinbutton_rotation_dipdir.get_value()
raxis_dip = self.spinbutton_rotation_dip.get_value()
raxis = [raxis_dipdir, raxis_dip]
raxis_angle = self.spinbutton_rotation_angle.get_value()
for lyr_obj in self.data:
lyr_type = lyr_obj.get_layer_type()
lyr_store = lyr_obj.get_data_treestore()
if lyr_type == "plane":
dipdir_org, dips_org, dipdir_lst, dips_lst, strat, dipdir_az = \
self.parse_plane(lyr_store, raxis, raxis_angle)
self.original_ax.plane(dipdir_org, dips_org, color=lyr_obj.get_line_color(),
linewidth=lyr_obj.get_line_width(),
linestyle=lyr_obj.get_line_style(),
dash_capstyle=lyr_obj.get_capstyle(),
alpha=lyr_obj.get_line_alpha(), clip_on=False)
self.rotated_ax.plane(dipdir_lst, dips_lst, color=lyr_obj.get_line_color(),
linewidth=lyr_obj.get_line_width(),
linestyle=lyr_obj.get_line_style(),
dash_capstyle=lyr_obj.get_capstyle(),
alpha=lyr_obj.get_line_alpha(), clip_on=False)
elif lyr_type == "line":
ldipdir_org, ldips_org, ldipdir_lst, ldips_lst, sense = \
self.parse_line(lyr_store, raxis, raxis_angle)
self.original_ax.line(ldips_org, ldipdir_org,
marker=lyr_obj.get_marker_style(),
markersize=lyr_obj.get_marker_size(),
color=lyr_obj.get_marker_fill(),
markeredgewidth=lyr_obj.get_marker_edge_width(),
markeredgecolor=lyr_obj.get_marker_edge_color(),
alpha=lyr_obj.get_marker_alpha(), clip_on=False)
self.rotated_ax.line(ldips_lst, ldipdir_lst,
marker=lyr_obj.get_marker_style(),
markersize=lyr_obj.get_marker_size(),
color=lyr_obj.get_marker_fill(),
markeredgewidth=lyr_obj.get_marker_edge_width(),
markeredgecolor=lyr_obj.get_marker_edge_color(),
alpha=lyr_obj.get_marker_alpha(), clip_on=False)
elif lyr_type == "smallcircle":
ldipdir_org, ldips_org, ldipdir_lst, ldips_lst, angle = \
self.parse_line(lyr_store, raxis, raxis_angle)
self.original_ax.cone(ldips_org, ldipdir_org, angle, facecolor="None",
color=lyr_obj.get_line_color(),
linewidth=lyr_obj.get_line_width(),
label=lyr_obj.get_label(),
linestyle=lyr_obj.get_line_style())
self.rotated_ax.cone(ldips_lst, ldipdir_lst, angle, facecolor="None",
color=lyr_obj.get_line_color(),
linewidth=lyr_obj.get_line_width(),
label=lyr_obj.get_label(),
linestyle=lyr_obj.get_line_style())
elif lyr_type == "faultplane":
rtrn = self.parse_faultplane(lyr_store, raxis, raxis_angle)
dipdir_org, dips_org, dipdir_lst, dips_lst, ldipdir_org, \
ldips_org, ldipdir_lst, ldips_lst, sense = rtrn[0], rtrn[1], \
rtrn[2], rtrn[3], rtrn[4], rtrn[5], rtrn[6], rtrn[7], rtrn[8]
self.original_ax.plane(dipdir_org, dips_org, color=lyr_obj.get_line_color(),
linewidth=lyr_obj.get_line_width(),
linestyle=lyr_obj.get_line_style(),
dash_capstyle=lyr_obj.get_capstyle(),
alpha=lyr_obj.get_line_alpha(), clip_on=False)
self.rotated_ax.plane(dipdir_lst, dips_lst, color=lyr_obj.get_line_color(),
linewidth=lyr_obj.get_line_width(),
linestyle=lyr_obj.get_line_style(),
dash_capstyle=lyr_obj.get_capstyle(),
alpha=lyr_obj.get_line_alpha(), clip_on=False)
self.original_ax.line(ldips_org, ldipdir_org,
marker=lyr_obj.get_marker_style(),
markersize=lyr_obj.get_marker_size(),
color=lyr_obj.get_marker_fill(),
markeredgewidth=lyr_obj.get_marker_edge_width(),
markeredgecolor=lyr_obj.get_marker_edge_color(),
alpha=lyr_obj.get_marker_alpha(), clip_on=False)
self.rotated_ax.line(ldips_lst, ldipdir_lst,
marker=lyr_obj.get_marker_style(),
markersize=lyr_obj.get_marker_size(),
color=lyr_obj.get_marker_fill(),
markeredgewidth=lyr_obj.get_marker_edge_width(),
markeredgecolor=lyr_obj.get_marker_edge_color(),
alpha=lyr_obj.get_marker_alpha(), clip_on=False)
#Plot rotation axis
self.original_ax.line(raxis_dip, raxis_dipdir, marker="o",
markersize=10, color="#ff0000",
markeredgewidth=1, markeredgecolor="#000000",
alpha=1, clip_on=False)
self.canvas.draw()
| gpl-2.0 |
MG-RAST/kmerspectrumanalyzer | pfge_analysis/PFGE_analysis.py | 2 | 12407 | #!/usr/bin/env python2.7
import os
import numpy as np
import matplotlib.pyplot as plt
from scipy.interpolate import spline
# from http://www.lfd.uci.edu/~gohlke/
try:
import tifffile
except ImportError:
print '''
The Tifffile module by Christoph Gohlke is required
for gel image analysis. Download from
http://www.lfd.uci.edu/~gohlke/, save in src/
(same folder as this script) and try again
(but save the rendered contents of the html
file tifffile.py.html as tifffile.py).
Warnings about additional modules can be
ignored after Tifffile installation.
'''
import sys
sys.exit()
def getLanes(imagefile,lanes,top,bottom):
'''Load pixel data summed by pixel rows down length of specified region corresponding to a gel lane'''
tif = tifffile.TiffFile(imagefile)
image = tif.asarray()
lanes1d = []
for l in lanes:
lanes1d += [np.sum(image[top:bottom,l[0]:l[1]],axis=1)]
return(lanes1d)
def getPerLaneLadder(ladder1x,ladder1peaks,ladder2x,ladder2peaks,samplelanes,sampleoffsets):
'''Linearly interpolate between corresponding bands in ladders on either side of samples for "per lane ladders"'''
ldiff = ladder2x - ladder1x
# take min peaks counting from small to big
nladderpeaks = min(len(ladder1peaks),len(ladder2peaks))
ladder1peaks = ladder1peaks[-nladderpeaks:]
ladder2peaks = ladder2peaks[-nladderpeaks:]
ladderbylanes = []
for n1,l in enumerate(samplelanes):
thesepeaks = []
soffset = sampleoffsets[n1] - ladder1x
for p in range(nladderpeaks):
peakdiff = ladder1peaks[p]-ladder2peaks[p]
thesepeaks += [ladder1peaks[p]-peakdiff*(soffset/float(ldiff))]
ladderbylanes += [np.array(thesepeaks)]
return(ladderbylanes)
def Quantify(ladderbylanes,laddersizes,allpeaks,samplenames,doplot,outfolder=None,prefix=None,desired_range=False,gelimagefile=None):
'''Interpolate ladder sizes versus migration distance with second order splines and quantify samples'''
sizes = {}
for n,ladder_peak_dists in enumerate(ladderbylanes):
sizes[samplenames[n]] = []
# interpolate and smooth between ladder peaks (which may not be linear or log-linear etc)
interpolated_lsizes = np.linspace(laddersizes.min(),laddersizes.max(),1000)
smoothed_lpeaks_dists = spline(laddersizes[::-1],ladder_peak_dists[::-1],interpolated_lsizes[::-1],order=2)[::-1]
if doplot:
fig = plt.figure(figsize=(12, 9))
axes = fig.add_subplot(111)
axes.plot(interpolated_lsizes,smoothed_lpeaks_dists,'-r',lw=0.7)
axes.plot(laddersizes,ladder_peak_dists,'og')
axes.set_xlabel('Fragment size (bp)')
axes.set_ylabel('Band migration (pixels)')
if desired_range:
drange = 'Desired range: %s bp, s'%desired_range
else:
drange = 'S'
#axes.set_title('Band fragment sizes by interpolation of ladder migration distances\n%sample lane %s, %s' % (drange,n+1,samplenames[n]))
plt.title('Band fragment sizes by interpolation of ladder migration distances\n%sample lane %s, %s, %s' % (drange,n+1,samplenames[n],gelimagefile))
for b,band in enumerate(ladder_peak_dists):
axes.annotate('%s bp' % laddersizes[b], xy=(laddersizes[b],band), xytext=(laddersizes[b]+5000,band), arrowprops=None)
# find where sample peaks intersect ladder spline and get corresponding DNA fragment size
xmin,xmax = axes.get_xlim()
ymin,ymax = axes.get_ylim()
for peak in allpeaks[n+1]:
s = [i for i,a in enumerate(smoothed_lpeaks_dists[1:]) if a < peak and smoothed_lpeaks_dists[i-1] >= peak][0]
sizes[samplenames[n]] += [int(round(interpolated_lsizes[s]))]
if doplot:
axes.axvline(x=interpolated_lsizes[s],ymax=(peak-ymin)/(ymax-ymin),color = 'purple',lw=1)
axes.axhline(y=peak,xmax=(interpolated_lsizes[s]-xmin)/(xmax-xmin),color = 'purple',lw=1)
if doplot:
axes.annotate('%s' % '\n'.join(['Quantification:']+[str(s)+' bp' for s in sorted(sizes[samplenames[n]])]), xy=((xmax-xmin)*0.6+xmin,(ymax-ymin)*0.8+ymin), xytext=((xmax-xmin)*0.6+xmin,(ymax-ymin)*0.8+ymin), arrowprops=None)
plt.savefig(outfolder+os.sep+prefix+'quantification_sample_lane_%s_%s%ssvg' % (n+1,samplenames[n],os.extsep), format='svg')
return(sizes)
def AnalyseGel(gelimagefile,samplenames,lane_x_ranges,top,bottom,allpeaks,laddersizes,outfolder,prefix,doplot,desired_range):
# get lane data from image file
lanes1d = getLanes(gelimagefile,lane_x_ranges,top,bottom)
if not os.path.exists(outfolder): os.mkdir(outfolder)
if doplot:
fig = plt.figure(figsize=(12, 9))
# plot ladder peak intensities
axes = fig.add_subplot(111)
axes.plot(lanes1d[0])
axes.set_xlabel('Distance along lane (pixels)')
axes.set_ylabel('Pixel row intensity')
plt.title('Lane intensities summed across pixel rows versus distance along ladder lane 1\n%s' % gelimagefile)
[axes.axvline(x=a,color = 'red') for a in allpeaks[0]]
plt.savefig(outfolder+os.sep+prefix+os.extsep.join(['ladder1','svg']), format='svg')
axes.clear()
axes.plot(lanes1d[-1])
axes.set_xlabel('Distance along lane (pixels)')
axes.set_ylabel('Pixel row intensity')
plt.title('Lane intensities summed across pixel rows versus distance along ladder lane 2\n%s' % gelimagefile)
[axes.axvline(x=a,color = 'red') for a in allpeaks[-1]]
plt.savefig(outfolder+os.sep+prefix+os.extsep.join(['ladder2','svg']), format='svg')
# plot samples
for n,lane in enumerate(lanes1d[1:-1]):
plt.cla()
axes.plot(lane)
axes.set_xlabel('Distance along lane (pixels)')
axes.set_ylabel('Pixel row intensity')
plt.title('Lane intensities summed across pixel rows versus distance along\nsample lane %s, %s, %s' % (n+1,samplenames[n],gelimagefile))
[axes.axvline(x=a,color = 'red') for a in allpeaks[n+1]]
plt.savefig(outfolder+os.sep+prefix+'intensities_sample_lane_%s_%s.svg' % ((n+1),samplenames[n]), format='svg')
# linear regress between ladder bands to create per lane ladders
ladder1x = sum(lane_x_ranges[0])/2
ladder2x = sum(lane_x_ranges[-1])/2
ladder1peaks = allpeaks[0]
ladder2peaks = allpeaks[-1]
samplepeaks = [np.array(a) for a in allpeaks[1:-1]]
sampleoffsets = [sum(n)/2 for n in lane_x_ranges[1:-1]]
ladderbylanes = getPerLaneLadder(ladder1x,ladder1peaks,ladder2x,ladder2peaks,samplepeaks,sampleoffsets)
# interpolate with a smoothed spline
sizes = Quantify(ladderbylanes,laddersizes,allpeaks,samplenames,doplot,outfolder,prefix,desired_range,gelimagefile)
# print sizes
for sample,thesesizes in sizes.items():
print 'Sample %s: %s' % (sample,', '.join([str(s) for s in sorted(thesesizes)]))
return(sizes)
# low range ladder
# https://www.neb.com/products/n0350-low-range-pfg-marker
lowrangeladder = [23100,9420,6550,4360,2320,2030]
# includes lambda ladder
# lambda ladder:
# http://www.neb.com/nebecomm/products/productn0340.asp
lambdaladder = [1018500,970000,921500,873000,824500,776000,727500, 679000, 630500, 582000, 533500, 485000, 436500, 388000, 339500, 291000, 242500, 194000, 145500, 97000, 48500]
# H. wingei chromosomes ladder:
# http://www.bio-rad.com/prd/en/US/adirect/biorad?cmd=catProductDetail&vertical=LSR&country=US&lang=en&productID=170-3667
Hwingeiladder = [3130000,2700000,2350000,1810000,1660000,1370000,1050000]
doplot = True
samples = ['E_1_37','A_3_34','C_4_22','D_4_27','B_4_28','K12']
outfolder = os.sep.join([os.pardir,'image','analysis'])
sizes = {}
def main():
## Small fragment ICeuI PFGE gel
desired_range = '< 145,500'
gelimagefile = os.sep.join([os.pardir,'image','gels',os.extsep.join(['ICeuI_small','tif'])])
laddersizes = np.array(lambdaladder[-4:]+lowrangeladder[:1])
## samples in left lanes
samplenames = samples[:3]
# x pixel ranges of lanes
lane_x_ranges = ((176,274),(334,434),(494,584),(642,726),(798,904))
# upper and lower pixels
(top,bottom) = (10,1128)
# band peak intensity distance down gel image -top
allpeaks = [np.array([ 25, 237, 549, 856, 1030]),
np.array([286, 415, 930]),
np.array([280, 428, 912]),
np.array([399, 512, 909]),
np.array([ 33, 243, 554, 862, 1032])]
prefix = 'ICeuI_small_123_'
print('\nAnalysing %s, plotting to %s%s%s*.svg\n' % (gelimagefile,outfolder,os.sep,prefix))
sizes[prefix] = AnalyseGel(gelimagefile,samplenames,lane_x_ranges,top,bottom,allpeaks,laddersizes,outfolder,prefix,doplot,desired_range)
## samples in right lanes
samplenames = samples[3:]
# x pixel ranges of lanes
lane_x_ranges = ((798,904),(962,1082),(1114,1224),(1264,1368),(1422,1530))
# band peak intensity distance down gel image -top
allpeaks = [np.array([ 33, 243, 554, 862, 1032]),
np.array([ 76, 408, 914]),
np.array([304, 575, 913]),
np.array([331, 588, 927]),
np.array([ 36, 255, 568, 879, 1053])]
prefix = 'ICeuI_small_456_'
print('\nAnalysing %s, plotting to %s%s%s*.svg\n' % (gelimagefile,outfolder,os.sep,prefix))
sizes[prefix] = AnalyseGel(gelimagefile,samplenames,lane_x_ranges,top,bottom,allpeaks,laddersizes,outfolder,prefix,doplot,desired_range)
## Mid fragment ICeuI PFGE gel
desired_range = '> 145,500 & < 1,000,000'
gelimagefile = os.sep.join([os.pardir,'image','gels',os.extsep.join(['ICeuI_medium','tif'])])
laddersizes = np.array(lambdaladder[-18:-9])
samplenames = samples
# x pixel ranges of lanes
lane_x_ranges = ((23,63),(96,148),(173,230),(255,305),(333,389),(415,466),(493,546),(584,622))
# upper and lower pixels
(top,bottom) = (0,260)
# band peak intensity distance down gel image -top
allpeaks = [np.array([ 8, 31, 55, 79, 106, 135, 167, 202, 240]),
np.array([ 38, 90, 193]),
np.array([105, 107, 210]),
np.array([ 79, 108, 207]),
np.array([ 32, 92, 202]),
np.array([ 88, 131, 202]),
np.array([ 99, 121, 212]),
np.array([ 17, 39, 62, 87, 113, 143, 175, 209, 247])]
prefix = 'ICeuI_medium_123456_'
print('\nAnalysing %s, plotting to %s%s%s*.svg\n' % (gelimagefile,outfolder,os.sep,prefix))
sizes[prefix] = AnalyseGel(gelimagefile,samplenames,lane_x_ranges,top,bottom,allpeaks,laddersizes,outfolder,prefix,doplot,desired_range)
## Large fragment ICeuI PFGE gel
desired_range = '> 2,000,000'
gelimagefile = os.sep.join([os.pardir,'image','gels',os.extsep.join(['ICeuI_large','tif'])])
laddersizes = np.array(Hwingeiladder[:3])
# upper and lower pixels
(top,bottom) = (130,482)
## sample in left lane
samplenames = samples[:1]
lane_x_ranges = ((162,204),(316,386),(472,514))
# band peak intensity distance down gel image -top
allpeaks = [np.array([ 84, 165, 263]), np.array([119]), np.array([ 87, 158, 254])]
prefix = 'ICeuI_large_1_'
print('\nAnalysing %s, plotting to %s%s%s*.svg\n' % (gelimagefile,outfolder,os.sep,prefix))
sizes[prefix] = AnalyseGel(gelimagefile,samplenames,lane_x_ranges,top,bottom,allpeaks,laddersizes,outfolder,prefix,doplot,desired_range)
## samples in middle lane
samplenames = samples[1:3]
lane_x_ranges = ((472,514),(598,660),(728,802),(878,922))
# band peak intensity distance down gel image -top
allpeaks = [np.array([ 87, 158, 248]), np.array([163]), np.array([116]), np.array([ 90, 163, 251])]
prefix = 'ICeuI_large_23_'
print('\nAnalysing %s, plotting to %s%s%s*.svg\n' % (gelimagefile,outfolder,os.sep,prefix))
sizes[prefix] = AnalyseGel(gelimagefile,samplenames,lane_x_ranges,top,bottom,allpeaks,laddersizes,outfolder,prefix,doplot,desired_range)
## samples in right lanes
samplenames = samples[3:]
lane_x_ranges = ((878,922),(1020,1076),(1160,1220),(1292,1362),(1436,1508))
# band peak intensity distance down gel image -top
allpeaks = [np.array([ 90, 163, 251]), np.array([107]), np.array([130]), np.array([145]), np.array([83, 166, 256])]
prefix = 'ICeuI_large_456_'
print('\nAnalysing %s, plotting to %s%s%s*.svg\n' % (gelimagefile,outfolder,os.sep,prefix))
sizes[prefix] = AnalyseGel(gelimagefile,samplenames,lane_x_ranges,top,bottom,allpeaks,laddersizes,outfolder,prefix,doplot,desired_range)
allsizes = {}
for sz in sizes.values():
for sm in samples:
if sm in sz:
if sm in allsizes:
allsizes[sm] += sz[sm]
else:
allsizes[sm] = sz[sm]
print('\n')
for sm,szs in sorted(allsizes.items()):
print('%s: %s' % (sm,sum(szs)))
print('\nSee ../image/analysis/ for analysis plots\n')
if __name__ == '__main__':
main()
| bsd-2-clause |
simudream/dask | dask/array/tests/test_percentiles.py | 8 | 1486 | import pytest
pytest.importorskip('numpy')
from dask.utils import skip
import dask.array as da
from dask.array.percentile import _percentile
import dask
import numpy as np
def eq(a, b):
if isinstance(a, da.Array):
a = a.compute(get=dask.get)
if isinstance(b, da.Array):
b = b.compute(get=dask.get)
c = a == b
if isinstance(c, np.ndarray):
c = c.all()
return c
def test_percentile():
d = da.ones((16,), chunks=(4,))
assert eq(da.percentile(d, [0, 50, 100]), [1, 1, 1])
x = np.array([0, 0, 5, 5, 5, 5, 20, 20])
d = da.from_array(x, chunks=(3,))
assert eq(da.percentile(d, [0, 50, 100]), [0, 5, 20])
x = np.array(['a', 'a', 'd', 'd', 'd', 'e'])
d = da.from_array(x, chunks=(3,))
assert eq(da.percentile(d, [0, 50, 100]), ['a', 'd', 'e'])
@skip
def test_percentile_with_categoricals():
try:
import pandas as pd
except ImportError:
return
x0 = pd.Categorical(['Alice', 'Bob', 'Charlie', 'Dennis', 'Alice', 'Alice'])
x1 = pd.Categorical(['Alice', 'Bob', 'Charlie', 'Dennis', 'Alice', 'Alice'])
dsk = {('x', 0): x0, ('x', 1): x1}
x = da.Array(dsk, 'x', chunks=((6, 6),))
p = da.percentile(x, [50])
assert (p.compute().categories == x0.categories).all()
assert (p.compute().codes == [0]).all()
def test_percentiles_with_empty_arrays():
x = da.ones(10, chunks=((5, 0, 5),))
assert da.percentile(x, [10, 50, 90]).compute().tolist() == [1, 1, 1]
| bsd-3-clause |
JamesSample/nirams_ii | Code/niramsii/nirams.py | 1 | 18049 | #-------------------------------------------------------------------------------
# Name: nirams.py
# Purpose: The main script for NIRAMS II.
#
# Author: James Sample
#
# Created: 18/01/2012
# Copyright: (c) James Sample and JHI, 2012
# Licence: <your licence>
#-------------------------------------------------------------------------------
""" This is the main or parent script for NIRAMS II.
"""
def run_model(param_xls, run_type):
""" Run the NIRAMS II model wtht he specified parameters and options.
Args:
param_xls: Path to complete Excel template for model setup.
run_type: The name of the worksheet to read data from in 'param_xls'.
Either 'single_run' for a single model run with the
specified parameter set or 'param_combos' for multiple
model runs.
"""
import time, input_output as io
st_time = time.time()
# Check run_type is valid
assert run_type in ('single_run', 'param_combos'), (
"The run_type must be either 'single_run' or 'param_combos'.\n"
"Please check and try again.")
# Read user input from specified sheet
user_dict = io.read_user_input(param_xls, run_type)
# Determine which version of NIRAMS to run
if run_type == 'single_run':
print 'Running NIRAMS II for a single parameter set...'
# Run single model
single_niramsii_run(user_dict)
else:
# run_type = 'param_combos'
import multiprocessing as mp, pandas as pd, os, shutil
import input_output as io
print 'Running NIRAMS II for multiple parameter sets...'
# Create temp folder for intermediate output
temp_path = os.path.split(user_dict['Output HDF5 path'])[0]
temp_fold = os.path.join(temp_path, 'temp')
os.makedirs(temp_fold)
# Calculate parameter combinations based on user input
param_combos = calculate_combinations(user_dict)
# Write param combos to CSV
df = pd.DataFrame(data=param_combos)
df.index = df['Run ID']
del df['Run ID']
# Re-order columns
col_order = ['Number of processors', 'Input HDF5 path', 'Parameter CSV',
'Output HDF5 path', 'Write GeoTiffs',
'Output GeoTiff folder', 'Start year', 'End year',
'xmin', 'xmax', 'ymin', 'ymax', 'Default PET to AET grid',
'Use IACS', 'T_snow', 'T_melt', 'Degree-day factor',
'Organic N factor', 'Mineralisation parameter',
'Denitrification parameter', 'N leaching parameter']
df[col_order].to_csv(user_dict['Parameter CSV'][0],
index_label='Run ID')
# Add the temp folder to each param_dict
for idx, param_dict in enumerate(param_combos):
param_dict['Output HDF5 folder'] = temp_fold
# Setup multiprocessing pool
pool = mp.Pool(user_dict['Number of processors'][0])
# Distribute runs to processors
pool.map(single_niramsii_run, param_combos)
pool.close()
# Merge outputs from each run to single HDF5
io.merge_hdf5(temp_fold, user_dict['Output HDF5 path'][0])
# Remove temp folder
shutil.rmtree(temp_fold)
end_time = time.time()
print 'Finished. Processing time: %.2f minutes.' % ((end_time-st_time)/60)
def single_niramsii_run(params_dict):
""" Run the NIRAMS II model with the specified parameters.
Args:
params_dict: Dict containing all model parameters and user-specified
options.
Returns:
None. Model outptus are written to the specified HDF5 file (and
GeoTiffs if specified).
"""
import input_output as io, snow as sn, drainage as dr
import nitrate as ni, calendar, numpy.ma as ma, os
# Paths to static nodes in the input HDF5 file
nodes_dict = {'land_props' : r'/one_km_grids/old_land_properties/',
'soil_props' : r'/one_km_grids/soil_properties/',
'met_data' : r'/five_km_grids/meteorological_data/',
'iacs_pet' : r'/one_km_grids/iacs_pet_facts/',
'or' : r'/one_km_grids/organic_n/',
'in' : r'/one_km_grids/inorganic_n/',
'up' : r'/one_km_grids/n_uptake/',
'n_dep' : r'/one_km_grids/n_deposition/',
'time_series': r'/time_series/'}
# Create output HDF5 file
io.create_output_h5(params_dict)
# Dicts storing number of days in each month (one for leap years; one for
# non-leap years)
days_in_month_dict = {1:31, 2:28, 3:31, 4:30, 5:31, 6:30, 7:31, 8:31, 9:30,
10:31, 11:30, 12:31}
days_in_month_lpyr_dict = {1:31, 2:29, 3:31, 4:30, 5:31, 6:30, 7:31, 8:31,
9:30, 10:31, 11:30, 12:31}
# Extract the grid indices for the bounding box into a dict
indices_dict = io.get_grid_indices(
params_dict['xmin'], params_dict['xmax'],
params_dict['ymin'], params_dict['ymax'])
# Extract the static grids from the HDF5 file
fc, sat, calibl, calibv = io.read_static_grids(
params_dict['Input HDF5 path'],
nodes_dict['soil_props'],
['fc', 'sat', 'calibl', 'calibv'],
indices_dict)
# Extract the PET to AET correction factor grid from the HDF5 file
default_pet_fact = io.read_static_grids(
params_dict['Input HDF5 path'],
nodes_dict['land_props'],
[params_dict['Default PET to AET grid'],],
indices_dict)[0]
# Set an initial water level halfway between field and saturation capacity
wat_lev = (fc + sat)/2
# Set an initial snow pack of zero
rows = (params_dict['ymax']-params_dict['ymin'])/1000
cols = (params_dict['xmax']-params_dict['xmin'])/1000
snow_pk = ma.zeros((rows,cols))
# Set the initial amount of available N using a simple annual balance for
# 2001
# Get the annual N grids for 2001 in a dict
n_bud_dict = io.read_annual_n_grids(params_dict['Input HDF5 path'],
nodes_dict,
2001,
indices_dict)
avail_n = ni.initial_n_budget(n_bud_dict, params_dict['Organic N factor'])
# Begin looping over time series data
for year in range(params_dict['Start year'], params_dict['End year']+1):
# Choose PET to AET conversion grids based on user input
if (params_dict['Use IACS'] == True) and (year in range(2001, 2011)):
# Get the iacs_pet_fact grid for this year
pet_fact = io.read_static_grids(params_dict['Input HDF5 path'],
nodes_dict['iacs_pet'],
['pet_fact_%s' % year,],
indices_dict)[0]
else:
# Use the default pet_fact grid
pet_fact = default_pet_fact
# Read the annual N grids
annual_n_dict = io.read_annual_n_grids(params_dict['Input HDF5 path'],
nodes_dict,
year,
indices_dict)
# Calculate daily n_dep rate for this year
if calendar.isleap(year) == True:
daily_n_dep = annual_n_dict['n_dep'] / 366.
else:
daily_n_dep = annual_n_dict['n_dep'] / 365.
# Keep track of annual totals
an_n_leach = ma.zeros((rows,cols))
an_ssf = ma.zeros((rows,cols))
an_gwf = ma.zeros((rows,cols))
an_of = ma.zeros((rows,cols))
# Loop over months
for month in range(1,13):
# Allow for leap years
if calendar.isleap(year) == True:
days_in_month = days_in_month_lpyr_dict[month]
else:
days_in_month = days_in_month_dict[month]
# Loop over days
for day in range(1, days_in_month+1):
# Get today's met data from the HDF5 file
pptn, t_min, t_max, pet = io.read_met_data(
params_dict['Input HDF5 path'],
nodes_dict['met_data'],
indices_dict,
year,
month,
day,
days_in_month)
# Convert PET to AET using pet_fact
aet = pet_fact*pet
# Where the ground is already covered in snow, set AET to zero
aet[snow_pk>0] = 0
# Reduce the AET if the soil is dry i.e. if wat_lev < 0.7*fc
aet = dr.reduce_aet_if_dry(aet, wat_lev, fc)
# Split today's pptn into rain and snow components
rain, snow = sn.estimate_snow_and_rain(pptn, t_min, t_max,
params_dict['T_snow'])
# Calculate today's snow melt
melt = sn.estimate_snow_melt(snow_pk, t_min, t_max,
params_dict['T_melt'],
params_dict['Degree-day factor'])
# Estimate temp and moisture factors
t_fact = ni.est_temp_factor(t_min, t_max)
moist_fact = ni.est_moisture_fact(wat_lev, fc)
# Calculate today's mineralisation
n_mineral = ni.est_mineralisation(
params_dict['Mineralisation parameter'],
t_fact,
moist_fact)
# Calculate today's denitrification
n_denit = ni.est_denitrification(
params_dict['Denitrification parameter'],
wat_lev,
t_fact,
moist_fact,
avail_n)
# Estimate amount of N added today
ts_row = io.read_ts_table(params_dict['Input HDF5 path'],
nodes_dict['time_series'],
day,
month)
n_added = ni.estimate_n_added(annual_n_dict,
daily_n_dep,
params_dict['Organic N factor'],
n_mineral,
n_denit,
ts_row)
# Calculate today's drainage grids
dr_list = dr.estimate_drainage(fc, sat, calibl, calibv,
wat_lev, snow_pk, rain, snow,
melt, aet)
snow_pk, wat_lev, surf_ro, lat_dr, vert_dr, tot_dr = dr_list
# Calculate today's N leaching
n_leach_list = ni.calculate_n_leaching(
avail_n,
n_added,
dr_list,
fc,
params_dict['N leaching parameter'])
leached_n, avail_n = n_leach_list
# Increment annual totals
an_n_leach += leached_n
an_gwf += vert_dr
an_ssf += lat_dr
an_of += surf_ro
# Calculate yearly drainage
an_drain = an_ssf+an_gwf+an_of
an_ss_drain = an_ssf+an_gwf
# Get path to output HDF5
hdf5_fold = params_dict['Output HDF5 folder']
run_id = params_dict['Run ID']
out_hdf5 = os.path.join(hdf5_fold, 'run_%03d.h5' % run_id)
# Write to output file
# Total drainage
io.write_array_to_h5(out_hdf5,
'/run_%03d' % run_id,
'total_drainage_%s' % year,
an_drain,
units='mm',
xmin=params_dict['xmin'],
xmax=params_dict['xmax'],
ymin=params_dict['ymin'],
ymax=params_dict['ymax'])
# Sub-surface drainage
io.write_array_to_h5(out_hdf5,
'/run_%03d' % run_id,
'sub-surface_drainage_%s' % year,
an_ss_drain,
units='mm',
xmin=params_dict['xmin'],
xmax=params_dict['xmax'],
ymin=params_dict['ymin'],
ymax=params_dict['ymax'])
# N leached
io.write_array_to_h5(out_hdf5,
'/run_%03d' % run_id,
'n_leached_%s' % year,
an_n_leach,
units='mm',
xmin=params_dict['xmin'],
xmax=params_dict['xmax'],
ymin=params_dict['ymin'],
ymax=params_dict['ymax'])
# Write to GTiff
if params_dict['Write GeoTiffs'] == True:
# Total drainage
tot_dr_path = os.path.join(params_dict['Output GeoTiff folder'],
'run_%03d_total_drainage_%s.tif'
% (run_id, year))
io.ma_to_gtiff(params_dict['xmin'], params_dict['ymax'], 1000,
tot_dr_path, an_drain)
# Sub-surface drainage
ss_dr_path = os.path.join(params_dict['Output GeoTiff folder'],
'run_%03d_sub-surface_drainage_%s.tif'
% (run_id, year))
io.ma_to_gtiff(params_dict['xmin'], params_dict['ymax'], 1000,
ss_dr_path, an_ss_drain)
# N leached
n_leach_path = os.path.join(params_dict['Output GeoTiff folder'],
'run_%03d_n_leached_%s.tif'
% (run_id, year))
io.ma_to_gtiff(params_dict['xmin'], params_dict['ymax'], 1000,
n_leach_path, an_n_leach)
def calculate_combinations(user_dict):
""" Read user inut from 'param_combos' sheet in input Excel file and
calculate all combnations of the parameters entered.
Args:
user_dict: Dict of user-defined input from the 'param_combos' sheet in
the input Excel template.
Returns:
List of dicts, where each dict is a valid input for
single_niramsii_run()
"""
import itertools as it
# List of params for which multiple parameter values can be entered
phys_params = ['T_snow', 'T_melt', 'Degree-day factor',
'Organic N factor', 'Mineralisation parameter',
'Denitrification parameter', 'N leaching parameter']
# To work with itertools, all dict elements must be in lists. Also need to
# Parse any comma separated lists entered for physical params
for key in user_dict.keys():
if key in phys_params:
if isinstance(user_dict[key], (float, int)):
# User has just entered a single value
user_dict[key] = [user_dict[key],]
else:
# User has entered a comma-separated list
user_dict[key] = [float(i) for i in
user_dict[key].split(',')]
else:
# Just add the param directly to a list
user_dict[key] = [user_dict[key],]
# Generate combinations. See
# http://stackoverflow.com/questions/3873654/combinations-from-dictionary-with-list-values-using-python
# for details
param_dicts = sorted(user_dict)
param_combos = [dict(zip(param_dicts, prod))
for prod in it.product(*(user_dict[param_dict]
for param_dict in param_dicts))]
# Check n_runs < 1000 (because my file naming is padded to 3 digits, so
# more than 999 runs won't work. Could be easily extended if necessary)
assert len(param_combos) < 1000, ('The maximum numbers of runs for this '
'code is 999.')
# Assign unique run IDs from 1 to n for n param combos
for idx, param_dict in enumerate(param_combos):
param_dict['Run ID'] = idx + 1
return param_combos | mit |
Anderson-Lab/anderson-lab.github.io | csc_466_2021_spring/MLCode/Ch16/Kalman_full.py | 3 | 5085 |
# Code from Chapter 16 of Machine Learning: An Algorithmic Perspective (2nd Edition)
# by Stephen Marsland (http://stephenmonika.net)
# You are free to use, change, or redistribute the code in any way you wish for
# non-commercial purposes, but please maintain the name of the original author.
# This code comes with no warranty of any kind.
# Stephen Marsland, 2008, 2014
import numpy as np
import pylab as pl
def Kalman_update(A,H,Q,R,y,x,Sig,B=None,u=None):
if B is None:
xpred = np.dot(A,x)
else:
xpred = np.dot(A,x) + np.dot(B,u)
SigPred = np.dot(A,np.dot(Sig,A.T)) + Q
e = y - np.dot(H,xpred)
Sinv = np.linalg.inv(np.dot(H,np.dot(SigPred,H.T)) + R)
K = np.dot(SigPred,np.dot(H.T,Sinv))
xnew = xpred + np.dot(K,e)
SigNew = np.dot((np.eye(np.shape(A)[0]) - np.dot(K,H)),SigPred)
return xnew.T,SigNew
def Kalman_smoother_update(A,Q,B,u,xs_t,Sigs_t,xfilt,Sigfilt,Sigfilt_t):
if B is None:
xpred = np.dot(A,xfilt)
else:
xpred = np.dot(A,xfilt) + np.dot(B,u)
SigPred = np.dot(A,np.dot(Sigfilt,A.T)) + Q
J = np.dot(Sigfilt,np.dot(A.T,np.linalg.inv(SigPred)))
xs = xfilt + np.dot(J,(xs_t - xpred))
Sigs = Sigfilt + np.dot(J,np.dot((Sigs_t - SigPred),J.T))
return xs.T, Sigs
def Kalman_filter(y,A,H,Q,R,x0,Sig0,B=None,u=None):
obs_size,T = np.shape(y)
state_size = np.shape(A)[0]
x = np.zeros((state_size,T))
Sig = np.zeros((state_size,state_size,T))
[x[:,0],Sig[:,:,0]] = Kalman_update(A,H,Q,R,y[:,0].reshape(len(y),1),x0,Sig0,B,u)
for t in range(1,T):
prevx = x[:,t-1].reshape(state_size,1)
prevSig = Sig[:,:,t-1]
[x[:,t],Sig[:,:,t]] = Kalman_update(A,H,Q,R,y[:,t].reshape(len(y),1),prevx,prevSig,B,u)
return x,Sig
def Kalman_smoother(y,A,H,Q,R,x0,Sig0,B=None,u=None):
obs_size,T = np.shape(y)
state_size = np.shape(A)[0]
xs = np.zeros((state_size,T))
Sigs = np.zeros((state_size,state_size,T))
[xfilt,Sigfilt] = Kalman_filter(y,A,H,Q,R,x0,Sig0,B,u)
xs[:,T-1] = xfilt[:,T-1]
Sigs[:,:,T-1] = Sigfilt[:,:,T-1]
for t in range(T-2,-1,-1):
[xs[:,t],Sigs[:,:,t]] = Kalman_smoother_update(A,Q,B,u,xs[:,t+1].reshape(len(xs),1),Sigs[:,:,t+1],xfilt[:,t].reshape(len(xfilt),1),Sigfilt[:,:,t],Sigfilt[:,:,t+1])
return xs,Sigs
def lds_sample(A,H,Q,R,state0,T):
# x(t+1) = Ax(t) + state_noise(t), state_noise ~ N(O,Q), x(0) = state0
# y(t) = Hx(t) + obs_noise(t), obs_noise~N(O,R)
state_noise_samples = np.random.multivariate_normal(np.zeros((len(Q))),Q,T).T
obs_noise_samples = np.random.multivariate_normal(np.zeros((len(R))),R,T).T
x = np.zeros((np.shape(H)[1],T))
y = np.zeros((np.shape(H)[0],T))
x[:,0] = state0.T
y[:,0] = np.dot(H,x[:,0]) + obs_noise_samples[:,0]
for t in range(1,T):
x[:,t] = np.dot(A,x[:,t-1]) + state_noise_samples[:,t]
y[:,t] = np.dot(H,x[:,t-1]) + obs_noise_samples[:,t]
return [x,y]
def Kalman_demo():
state_size = 4
observation_size = 2
A = np.array([[1,0,1,0],[0,1,0,1],[0,0,1,0],[0,0,0,1]],dtype=float)
H = np.array([[1,0,0,0],[0,1,0,0]],dtype=float)
Q = 0.1*np.eye((state_size))
R = np.eye(observation_size,dtype=float)
x0 = np.array([[10],[10],[1],[0]],dtype=float)
Sig0 = 10. * np.eye(state_size)
np.random.seed(3)
T = 15
[x,y] = lds_sample(A,H,Q,R,x0,T)
[xfilt,Sigfilt] = Kalman_filter(y,A,H,Q,R,x0,Sig0)
[xsmooth,Sigsmooth] = Kalman_smoother(y,A,H,Q,R,x0,Sig0)
dfilt = x[[0,1],:] - xfilt[[0,1],:]
mse_filt = np.sqrt(np.sum(dfilt**2))
dsmooth = x[[0,1],:] - xsmooth[[0,1],:]
mse_smooth = np.sqrt(np.sum(dsmooth**2))
plot_track(x,y,xfilt,Sigfilt)
plot_track(x,y,xsmooth,Sigsmooth)
def plot_track(x,y,Kx,Sig):
fig = pl.figure()
ax = fig.add_subplot(111, aspect='equal')
pl.plot(x[0,:],x[1,:],'ks-')
pl.plot(y[0,:],y[1,:],'k*')
pl.plot(Kx[0,:],Kx[1,:],'kx:')
pl.legend(('True','Observed','Filtered'))
obs_size,T = np.shape(y)
from matplotlib.patches import Ellipse
# Axes of ellipse are eigenvectors of covariance matrix, lengths are square roots of eigenvalues
ellsize = np.zeros((obs_size,T))
ellangle = np.zeros((T))
for t in range(T):
[evals,evecs] = np.linalg.eig(Sig[:2,:2,t])
ellsize[:,t] = np.sqrt(evals)
ellangle[t] = np.angle(evecs[0,0]+0.j*evecs[0,1])
ells = [Ellipse(xy=[Kx[0,t],Kx[1,t]] ,width=ellsize[0,t],height=ellsize[1,t], angle=ellangle[t]) for t in range(T)]
for e in ells:
ax.add_artist(e)
e.set_alpha(0.1)
e.set_facecolor([0.7,0.7,0.7])
pl.xlabel('x')
pl.ylabel('y')
def Kalman_demo1d():
x0 = np.array([-0.37727])
Sig0 = 0.1*np.ones((1))
T = 50
y = np.random.normal(x0,Sig0,(1,T))
A = np.eye(1)
H = np.eye(1)
Q = np.eye(1)*1e-5
R = np.eye(1)*0.01
xfilt = np.zeros((1,T),dtype=float)
Sigfilt = np.zeros((1,T),dtype=float)
[xfilt,Sigfilt] = Kalman_filter(y,A,H,Q,R,x0,Sig0)
xfilt = np.squeeze(xfilt)
Sigfilt = np.squeeze(Sigfilt)
pl.figure()
time = np.arange(T)
pl.plot(time,y[0,:],'ko',ms=6)
pl.plot(time,xfilt,'k-',lw=3)
pl.plot(time,xfilt+20*Sigfilt,'k--',lw=2)
pl.plot(time,xfilt-20*Sigfilt,'k--',lw=2)
pl.legend(['Noisy Datapoints','Kalman estimate','20*Covariance'])
pl.xlabel('Time')
| mit |
deo1/deo1 | KaggleKkboxChurn/preprocessor_classifier_config_dict.py | 2 | 2606 | import numpy as np
classifier_config_dict = {
# Preprocesssors
'sklearn.preprocessing.Binarizer': {
'threshold': np.arange(0.0, 1.01, 0.05)
},
'sklearn.decomposition.FastICA': {
'tol': np.arange(0.0, 1.01, 0.05)
},
'sklearn.cluster.FeatureAgglomeration': {
'linkage': ['ward', 'complete', 'average'],
'affinity': ['euclidean', 'l1', 'l2', 'manhattan', 'cosine', 'precomputed']
},
'sklearn.preprocessing.MaxAbsScaler': {
},
'sklearn.preprocessing.MinMaxScaler': {
},
'sklearn.preprocessing.Normalizer': {
'norm': ['l1', 'l2', 'max']
},
'sklearn.kernel_approximation.Nystroem': {
'kernel': ['rbf', 'cosine', 'chi2', 'laplacian', 'polynomial', 'poly', 'linear', 'additive_chi2', 'sigmoid'],
'gamma': np.arange(0.0, 1.01, 0.05),
'n_components': range(1, 11)
},
'sklearn.decomposition.PCA': {
'svd_solver': ['randomized'],
'iterated_power': range(1, 11)
},
'sklearn.preprocessing.PolynomialFeatures': {
'degree': [2],
'include_bias': [False],
'interaction_only': [False]
},
'sklearn.kernel_approximation.RBFSampler': {
'gamma': np.arange(0.0, 1.01, 0.05)
},
'sklearn.preprocessing.RobustScaler': {
},
'sklearn.preprocessing.StandardScaler': {
},
'tpot.builtins.ZeroCount': {
},
# Selectors
'sklearn.feature_selection.SelectFwe': {
'alpha': np.arange(0, 0.05, 0.001),
'score_func': {
'sklearn.feature_selection.f_classif': None
}
},
'sklearn.feature_selection.SelectPercentile': {
'percentile': range(1, 100),
'score_func': {
'sklearn.feature_selection.f_classif': None
}
},
'sklearn.feature_selection.VarianceThreshold': {
'threshold': np.arange(0.05, 1.01, 0.05)
},
'sklearn.feature_selection.RFE': {
'step': np.arange(0.05, 1.01, 0.05),
'estimator': {
'sklearn.ensemble.ExtraTreesClassifier': {
'n_estimators': [100],
'criterion': ['gini', 'entropy'],
'max_features': np.arange(0.05, 1.01, 0.05)
}
}
},
'sklearn.feature_selection.SelectFromModel': {
'threshold': np.arange(0, 1.01, 0.05),
'estimator': {
'sklearn.ensemble.ExtraTreesClassifier': {
'n_estimators': [100],
'criterion': ['gini', 'entropy'],
'max_features': np.arange(0.05, 1.01, 0.05)
}
}
}
} | mit |
fyffyt/scikit-learn | sklearn/datasets/twenty_newsgroups.py | 126 | 13591 | """Caching loader for the 20 newsgroups text classification dataset
The description of the dataset is available on the official website at:
http://people.csail.mit.edu/jrennie/20Newsgroups/
Quoting the introduction:
The 20 Newsgroups data set is a collection of approximately 20,000
newsgroup documents, partitioned (nearly) evenly across 20 different
newsgroups. To the best of my knowledge, it was originally collected
by Ken Lang, probably for his Newsweeder: Learning to filter netnews
paper, though he does not explicitly mention this collection. The 20
newsgroups collection has become a popular data set for experiments
in text applications of machine learning techniques, such as text
classification and text clustering.
This dataset loader will download the recommended "by date" variant of the
dataset and which features a point in time split between the train and
test sets. The compressed dataset size is around 14 Mb compressed. Once
uncompressed the train set is 52 MB and the test set is 34 MB.
The data is downloaded, extracted and cached in the '~/scikit_learn_data'
folder.
The `fetch_20newsgroups` function will not vectorize the data into numpy
arrays but the dataset lists the filenames of the posts and their categories
as target labels.
The `fetch_20newsgroups_vectorized` function will in addition do a simple
tf-idf vectorization step.
"""
# Copyright (c) 2011 Olivier Grisel <[email protected]>
# License: BSD 3 clause
import os
import logging
import tarfile
import pickle
import shutil
import re
import codecs
import numpy as np
import scipy.sparse as sp
from .base import get_data_home
from .base import Bunch
from .base import load_files
from ..utils import check_random_state
from ..feature_extraction.text import CountVectorizer
from ..preprocessing import normalize
from ..externals import joblib, six
if six.PY3:
from urllib.request import urlopen
else:
from urllib2 import urlopen
logger = logging.getLogger(__name__)
URL = ("http://people.csail.mit.edu/jrennie/"
"20Newsgroups/20news-bydate.tar.gz")
ARCHIVE_NAME = "20news-bydate.tar.gz"
CACHE_NAME = "20news-bydate.pkz"
TRAIN_FOLDER = "20news-bydate-train"
TEST_FOLDER = "20news-bydate-test"
def download_20newsgroups(target_dir, cache_path):
"""Download the 20 newsgroups data and stored it as a zipped pickle."""
archive_path = os.path.join(target_dir, ARCHIVE_NAME)
train_path = os.path.join(target_dir, TRAIN_FOLDER)
test_path = os.path.join(target_dir, TEST_FOLDER)
if not os.path.exists(target_dir):
os.makedirs(target_dir)
if os.path.exists(archive_path):
# Download is not complete as the .tar.gz file is removed after
# download.
logger.warning("Download was incomplete, downloading again.")
os.remove(archive_path)
logger.warning("Downloading dataset from %s (14 MB)", URL)
opener = urlopen(URL)
with open(archive_path, 'wb') as f:
f.write(opener.read())
logger.info("Decompressing %s", archive_path)
tarfile.open(archive_path, "r:gz").extractall(path=target_dir)
os.remove(archive_path)
# Store a zipped pickle
cache = dict(train=load_files(train_path, encoding='latin1'),
test=load_files(test_path, encoding='latin1'))
compressed_content = codecs.encode(pickle.dumps(cache), 'zlib_codec')
with open(cache_path, 'wb') as f:
f.write(compressed_content)
shutil.rmtree(target_dir)
return cache
def strip_newsgroup_header(text):
"""
Given text in "news" format, strip the headers, by removing everything
before the first blank line.
"""
_before, _blankline, after = text.partition('\n\n')
return after
_QUOTE_RE = re.compile(r'(writes in|writes:|wrote:|says:|said:'
r'|^In article|^Quoted from|^\||^>)')
def strip_newsgroup_quoting(text):
"""
Given text in "news" format, strip lines beginning with the quote
characters > or |, plus lines that often introduce a quoted section
(for example, because they contain the string 'writes:'.)
"""
good_lines = [line for line in text.split('\n')
if not _QUOTE_RE.search(line)]
return '\n'.join(good_lines)
def strip_newsgroup_footer(text):
"""
Given text in "news" format, attempt to remove a signature block.
As a rough heuristic, we assume that signatures are set apart by either
a blank line or a line made of hyphens, and that it is the last such line
in the file (disregarding blank lines at the end).
"""
lines = text.strip().split('\n')
for line_num in range(len(lines) - 1, -1, -1):
line = lines[line_num]
if line.strip().strip('-') == '':
break
if line_num > 0:
return '\n'.join(lines[:line_num])
else:
return text
def fetch_20newsgroups(data_home=None, subset='train', categories=None,
shuffle=True, random_state=42,
remove=(),
download_if_missing=True):
"""Load the filenames and data from the 20 newsgroups dataset.
Read more in the :ref:`User Guide <20newsgroups>`.
Parameters
----------
subset: 'train' or 'test', 'all', optional
Select the dataset to load: 'train' for the training set, 'test'
for the test set, 'all' for both, with shuffled ordering.
data_home: optional, default: None
Specify a download and cache folder for the datasets. If None,
all scikit-learn data is stored in '~/scikit_learn_data' subfolders.
categories: None or collection of string or unicode
If None (default), load all the categories.
If not None, list of category names to load (other categories
ignored).
shuffle: bool, optional
Whether or not to shuffle the data: might be important for models that
make the assumption that the samples are independent and identically
distributed (i.i.d.), such as stochastic gradient descent.
random_state: numpy random number generator or seed integer
Used to shuffle the dataset.
download_if_missing: optional, True by default
If False, raise an IOError if the data is not locally available
instead of trying to download the data from the source site.
remove: tuple
May contain any subset of ('headers', 'footers', 'quotes'). Each of
these are kinds of text that will be detected and removed from the
newsgroup posts, preventing classifiers from overfitting on
metadata.
'headers' removes newsgroup headers, 'footers' removes blocks at the
ends of posts that look like signatures, and 'quotes' removes lines
that appear to be quoting another post.
'headers' follows an exact standard; the other filters are not always
correct.
"""
data_home = get_data_home(data_home=data_home)
cache_path = os.path.join(data_home, CACHE_NAME)
twenty_home = os.path.join(data_home, "20news_home")
cache = None
if os.path.exists(cache_path):
try:
with open(cache_path, 'rb') as f:
compressed_content = f.read()
uncompressed_content = codecs.decode(
compressed_content, 'zlib_codec')
cache = pickle.loads(uncompressed_content)
except Exception as e:
print(80 * '_')
print('Cache loading failed')
print(80 * '_')
print(e)
if cache is None:
if download_if_missing:
cache = download_20newsgroups(target_dir=twenty_home,
cache_path=cache_path)
else:
raise IOError('20Newsgroups dataset not found')
if subset in ('train', 'test'):
data = cache[subset]
elif subset == 'all':
data_lst = list()
target = list()
filenames = list()
for subset in ('train', 'test'):
data = cache[subset]
data_lst.extend(data.data)
target.extend(data.target)
filenames.extend(data.filenames)
data.data = data_lst
data.target = np.array(target)
data.filenames = np.array(filenames)
else:
raise ValueError(
"subset can only be 'train', 'test' or 'all', got '%s'" % subset)
data.description = 'the 20 newsgroups by date dataset'
if 'headers' in remove:
data.data = [strip_newsgroup_header(text) for text in data.data]
if 'footers' in remove:
data.data = [strip_newsgroup_footer(text) for text in data.data]
if 'quotes' in remove:
data.data = [strip_newsgroup_quoting(text) for text in data.data]
if categories is not None:
labels = [(data.target_names.index(cat), cat) for cat in categories]
# Sort the categories to have the ordering of the labels
labels.sort()
labels, categories = zip(*labels)
mask = np.in1d(data.target, labels)
data.filenames = data.filenames[mask]
data.target = data.target[mask]
# searchsorted to have continuous labels
data.target = np.searchsorted(labels, data.target)
data.target_names = list(categories)
# Use an object array to shuffle: avoids memory copy
data_lst = np.array(data.data, dtype=object)
data_lst = data_lst[mask]
data.data = data_lst.tolist()
if shuffle:
random_state = check_random_state(random_state)
indices = np.arange(data.target.shape[0])
random_state.shuffle(indices)
data.filenames = data.filenames[indices]
data.target = data.target[indices]
# Use an object array to shuffle: avoids memory copy
data_lst = np.array(data.data, dtype=object)
data_lst = data_lst[indices]
data.data = data_lst.tolist()
return data
def fetch_20newsgroups_vectorized(subset="train", remove=(), data_home=None):
"""Load the 20 newsgroups dataset and transform it into tf-idf vectors.
This is a convenience function; the tf-idf transformation is done using the
default settings for `sklearn.feature_extraction.text.Vectorizer`. For more
advanced usage (stopword filtering, n-gram extraction, etc.), combine
fetch_20newsgroups with a custom `Vectorizer` or `CountVectorizer`.
Read more in the :ref:`User Guide <20newsgroups>`.
Parameters
----------
subset: 'train' or 'test', 'all', optional
Select the dataset to load: 'train' for the training set, 'test'
for the test set, 'all' for both, with shuffled ordering.
data_home: optional, default: None
Specify an download and cache folder for the datasets. If None,
all scikit-learn data is stored in '~/scikit_learn_data' subfolders.
remove: tuple
May contain any subset of ('headers', 'footers', 'quotes'). Each of
these are kinds of text that will be detected and removed from the
newsgroup posts, preventing classifiers from overfitting on
metadata.
'headers' removes newsgroup headers, 'footers' removes blocks at the
ends of posts that look like signatures, and 'quotes' removes lines
that appear to be quoting another post.
Returns
-------
bunch : Bunch object
bunch.data: sparse matrix, shape [n_samples, n_features]
bunch.target: array, shape [n_samples]
bunch.target_names: list, length [n_classes]
"""
data_home = get_data_home(data_home=data_home)
filebase = '20newsgroup_vectorized'
if remove:
filebase += 'remove-' + ('-'.join(remove))
target_file = os.path.join(data_home, filebase + ".pk")
# we shuffle but use a fixed seed for the memoization
data_train = fetch_20newsgroups(data_home=data_home,
subset='train',
categories=None,
shuffle=True,
random_state=12,
remove=remove)
data_test = fetch_20newsgroups(data_home=data_home,
subset='test',
categories=None,
shuffle=True,
random_state=12,
remove=remove)
if os.path.exists(target_file):
X_train, X_test = joblib.load(target_file)
else:
vectorizer = CountVectorizer(dtype=np.int16)
X_train = vectorizer.fit_transform(data_train.data).tocsr()
X_test = vectorizer.transform(data_test.data).tocsr()
joblib.dump((X_train, X_test), target_file, compress=9)
# the data is stored as int16 for compactness
# but normalize needs floats
X_train = X_train.astype(np.float64)
X_test = X_test.astype(np.float64)
normalize(X_train, copy=False)
normalize(X_test, copy=False)
target_names = data_train.target_names
if subset == "train":
data = X_train
target = data_train.target
elif subset == "test":
data = X_test
target = data_test.target
elif subset == "all":
data = sp.vstack((X_train, X_test)).tocsr()
target = np.concatenate((data_train.target, data_test.target))
else:
raise ValueError("%r is not a valid subset: should be one of "
"['train', 'test', 'all']" % subset)
return Bunch(data=data, target=target, target_names=target_names)
| bsd-3-clause |
ryfeus/lambda-packs | Skimage_numpy/source/scipy/ndimage/filters.py | 24 | 42327 | # Copyright (C) 2003-2005 Peter J. Verveer
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions
# are met:
#
# 1. Redistributions of source code must retain the above copyright
# notice, this list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above
# copyright notice, this list of conditions and the following
# disclaimer in the documentation and/or other materials provided
# with the distribution.
#
# 3. The name of the author may not be used to endorse or promote
# products derived from this software without specific prior
# written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS
# OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
# WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
# ARE DISCLAIMED. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY
# DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE
# GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY,
# WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
# NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
# SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
from __future__ import division, print_function, absolute_import
import math
import numpy
from . import _ni_support
from . import _nd_image
from scipy.misc import doccer
from scipy._lib._version import NumpyVersion
__all__ = ['correlate1d', 'convolve1d', 'gaussian_filter1d', 'gaussian_filter',
'prewitt', 'sobel', 'generic_laplace', 'laplace',
'gaussian_laplace', 'generic_gradient_magnitude',
'gaussian_gradient_magnitude', 'correlate', 'convolve',
'uniform_filter1d', 'uniform_filter', 'minimum_filter1d',
'maximum_filter1d', 'minimum_filter', 'maximum_filter',
'rank_filter', 'median_filter', 'percentile_filter',
'generic_filter1d', 'generic_filter']
_input_doc = \
"""input : array_like
Input array to filter."""
_axis_doc = \
"""axis : int, optional
The axis of `input` along which to calculate. Default is -1."""
_output_doc = \
"""output : array, optional
The `output` parameter passes an array in which to store the
filter output."""
_size_foot_doc = \
"""size : scalar or tuple, optional
See footprint, below
footprint : array, optional
Either `size` or `footprint` must be defined. `size` gives
the shape that is taken from the input array, at every element
position, to define the input to the filter function.
`footprint` is a boolean array that specifies (implicitly) a
shape, but also which of the elements within this shape will get
passed to the filter function. Thus ``size=(n,m)`` is equivalent
to ``footprint=np.ones((n,m))``. We adjust `size` to the number
of dimensions of the input array, so that, if the input array is
shape (10,10,10), and `size` is 2, then the actual size used is
(2,2,2).
"""
_mode_doc = \
"""mode : {'reflect', 'constant', 'nearest', 'mirror', 'wrap'}, optional
The `mode` parameter determines how the array borders are
handled, where `cval` is the value when mode is equal to
'constant'. Default is 'reflect'"""
_cval_doc = \
"""cval : scalar, optional
Value to fill past edges of input if `mode` is 'constant'. Default
is 0.0"""
_origin_doc = \
"""origin : scalar, optional
The `origin` parameter controls the placement of the filter.
Default 0.0."""
_extra_arguments_doc = \
"""extra_arguments : sequence, optional
Sequence of extra positional arguments to pass to passed function"""
_extra_keywords_doc = \
"""extra_keywords : dict, optional
dict of extra keyword arguments to pass to passed function"""
docdict = {
'input': _input_doc,
'axis': _axis_doc,
'output': _output_doc,
'size_foot': _size_foot_doc,
'mode': _mode_doc,
'cval': _cval_doc,
'origin': _origin_doc,
'extra_arguments': _extra_arguments_doc,
'extra_keywords': _extra_keywords_doc,
}
docfiller = doccer.filldoc(docdict)
@docfiller
def correlate1d(input, weights, axis=-1, output=None, mode="reflect",
cval=0.0, origin=0):
"""Calculate a one-dimensional correlation along the given axis.
The lines of the array along the given axis are correlated with the
given weights.
Parameters
----------
%(input)s
weights : array
One-dimensional sequence of numbers.
%(axis)s
%(output)s
%(mode)s
%(cval)s
%(origin)s
"""
input = numpy.asarray(input)
if numpy.iscomplexobj(input):
raise TypeError('Complex type not supported')
output, return_value = _ni_support._get_output(output, input)
weights = numpy.asarray(weights, dtype=numpy.float64)
if weights.ndim != 1 or weights.shape[0] < 1:
raise RuntimeError('no filter weights given')
if not weights.flags.contiguous:
weights = weights.copy()
axis = _ni_support._check_axis(axis, input.ndim)
if (len(weights) // 2 + origin < 0) or (len(weights) // 2 +
origin > len(weights)):
raise ValueError('invalid origin')
mode = _ni_support._extend_mode_to_code(mode)
_nd_image.correlate1d(input, weights, axis, output, mode, cval,
origin)
return return_value
@docfiller
def convolve1d(input, weights, axis=-1, output=None, mode="reflect",
cval=0.0, origin=0):
"""Calculate a one-dimensional convolution along the given axis.
The lines of the array along the given axis are convolved with the
given weights.
Parameters
----------
%(input)s
weights : ndarray
One-dimensional sequence of numbers.
%(axis)s
%(output)s
%(mode)s
%(cval)s
%(origin)s
Returns
-------
convolve1d : ndarray
Convolved array with same shape as input
"""
weights = weights[::-1]
origin = -origin
if not len(weights) & 1:
origin -= 1
return correlate1d(input, weights, axis, output, mode, cval, origin)
@docfiller
def gaussian_filter1d(input, sigma, axis=-1, order=0, output=None,
mode="reflect", cval=0.0, truncate=4.0):
"""One-dimensional Gaussian filter.
Parameters
----------
%(input)s
sigma : scalar
standard deviation for Gaussian kernel
%(axis)s
order : {0, 1, 2, 3}, optional
An order of 0 corresponds to convolution with a Gaussian
kernel. An order of 1, 2, or 3 corresponds to convolution with
the first, second or third derivatives of a Gaussian. Higher
order derivatives are not implemented
%(output)s
%(mode)s
%(cval)s
truncate : float, optional
Truncate the filter at this many standard deviations.
Default is 4.0.
Returns
-------
gaussian_filter1d : ndarray
"""
if order not in range(4):
raise ValueError('Order outside 0..3 not implemented')
sd = float(sigma)
# make the radius of the filter equal to truncate standard deviations
lw = int(truncate * sd + 0.5)
weights = [0.0] * (2 * lw + 1)
weights[lw] = 1.0
sum = 1.0
sd = sd * sd
# calculate the kernel:
for ii in range(1, lw + 1):
tmp = math.exp(-0.5 * float(ii * ii) / sd)
weights[lw + ii] = tmp
weights[lw - ii] = tmp
sum += 2.0 * tmp
for ii in range(2 * lw + 1):
weights[ii] /= sum
# implement first, second and third order derivatives:
if order == 1: # first derivative
weights[lw] = 0.0
for ii in range(1, lw + 1):
x = float(ii)
tmp = -x / sd * weights[lw + ii]
weights[lw + ii] = -tmp
weights[lw - ii] = tmp
elif order == 2: # second derivative
weights[lw] *= -1.0 / sd
for ii in range(1, lw + 1):
x = float(ii)
tmp = (x * x / sd - 1.0) * weights[lw + ii] / sd
weights[lw + ii] = tmp
weights[lw - ii] = tmp
elif order == 3: # third derivative
weights[lw] = 0.0
sd2 = sd * sd
for ii in range(1, lw + 1):
x = float(ii)
tmp = (3.0 - x * x / sd) * x * weights[lw + ii] / sd2
weights[lw + ii] = -tmp
weights[lw - ii] = tmp
return correlate1d(input, weights, axis, output, mode, cval, 0)
@docfiller
def gaussian_filter(input, sigma, order=0, output=None,
mode="reflect", cval=0.0, truncate=4.0):
"""Multidimensional Gaussian filter.
Parameters
----------
%(input)s
sigma : scalar or sequence of scalars
Standard deviation for Gaussian kernel. The standard
deviations of the Gaussian filter are given for each axis as a
sequence, or as a single number, in which case it is equal for
all axes.
order : {0, 1, 2, 3} or sequence from same set, optional
The order of the filter along each axis is given as a sequence
of integers, or as a single number. An order of 0 corresponds
to convolution with a Gaussian kernel. An order of 1, 2, or 3
corresponds to convolution with the first, second or third
derivatives of a Gaussian. Higher order derivatives are not
implemented
%(output)s
%(mode)s
%(cval)s
truncate : float
Truncate the filter at this many standard deviations.
Default is 4.0.
Returns
-------
gaussian_filter : ndarray
Returned array of same shape as `input`.
Notes
-----
The multidimensional filter is implemented as a sequence of
one-dimensional convolution filters. The intermediate arrays are
stored in the same data type as the output. Therefore, for output
types with a limited precision, the results may be imprecise
because intermediate results may be stored with insufficient
precision.
Examples
--------
>>> from scipy.ndimage import gaussian_filter
>>> a = np.arange(50, step=2).reshape((5,5))
>>> a
array([[ 0, 2, 4, 6, 8],
[10, 12, 14, 16, 18],
[20, 22, 24, 26, 28],
[30, 32, 34, 36, 38],
[40, 42, 44, 46, 48]])
>>> gaussian_filter(a, sigma=1)
array([[ 4, 6, 8, 9, 11],
[10, 12, 14, 15, 17],
[20, 22, 24, 25, 27],
[29, 31, 33, 34, 36],
[35, 37, 39, 40, 42]])
"""
input = numpy.asarray(input)
output, return_value = _ni_support._get_output(output, input)
orders = _ni_support._normalize_sequence(order, input.ndim)
if not set(orders).issubset(set(range(4))):
raise ValueError('Order outside 0..4 not implemented')
sigmas = _ni_support._normalize_sequence(sigma, input.ndim)
axes = list(range(input.ndim))
axes = [(axes[ii], sigmas[ii], orders[ii])
for ii in range(len(axes)) if sigmas[ii] > 1e-15]
if len(axes) > 0:
for axis, sigma, order in axes:
gaussian_filter1d(input, sigma, axis, order, output,
mode, cval, truncate)
input = output
else:
output[...] = input[...]
return return_value
@docfiller
def prewitt(input, axis=-1, output=None, mode="reflect", cval=0.0):
"""Calculate a Prewitt filter.
Parameters
----------
%(input)s
%(axis)s
%(output)s
%(mode)s
%(cval)s
Examples
--------
>>> from scipy import ndimage, misc
>>> import matplotlib.pyplot as plt
>>> ascent = misc.ascent()
>>> result = ndimage.prewitt(ascent)
>>> plt.gray() # show the filtered result in grayscale
>>> plt.imshow(result)
"""
input = numpy.asarray(input)
axis = _ni_support._check_axis(axis, input.ndim)
output, return_value = _ni_support._get_output(output, input)
correlate1d(input, [-1, 0, 1], axis, output, mode, cval, 0)
axes = [ii for ii in range(input.ndim) if ii != axis]
for ii in axes:
correlate1d(output, [1, 1, 1], ii, output, mode, cval, 0,)
return return_value
@docfiller
def sobel(input, axis=-1, output=None, mode="reflect", cval=0.0):
"""Calculate a Sobel filter.
Parameters
----------
%(input)s
%(axis)s
%(output)s
%(mode)s
%(cval)s
Examples
--------
>>> from scipy import ndimage, misc
>>> import matplotlib.pyplot as plt
>>> ascent = misc.ascent()
>>> result = ndimage.sobel(ascent)
>>> plt.gray() # show the filtered result in grayscale
>>> plt.imshow(result)
"""
input = numpy.asarray(input)
axis = _ni_support._check_axis(axis, input.ndim)
output, return_value = _ni_support._get_output(output, input)
correlate1d(input, [-1, 0, 1], axis, output, mode, cval, 0)
axes = [ii for ii in range(input.ndim) if ii != axis]
for ii in axes:
correlate1d(output, [1, 2, 1], ii, output, mode, cval, 0)
return return_value
@docfiller
def generic_laplace(input, derivative2, output=None, mode="reflect",
cval=0.0,
extra_arguments=(),
extra_keywords = None):
"""N-dimensional Laplace filter using a provided second derivative function
Parameters
----------
%(input)s
derivative2 : callable
Callable with the following signature::
derivative2(input, axis, output, mode, cval,
*extra_arguments, **extra_keywords)
See `extra_arguments`, `extra_keywords` below.
%(output)s
%(mode)s
%(cval)s
%(extra_keywords)s
%(extra_arguments)s
"""
if extra_keywords is None:
extra_keywords = {}
input = numpy.asarray(input)
output, return_value = _ni_support._get_output(output, input)
axes = list(range(input.ndim))
if len(axes) > 0:
derivative2(input, axes[0], output, mode, cval,
*extra_arguments, **extra_keywords)
for ii in range(1, len(axes)):
tmp = derivative2(input, axes[ii], output.dtype, mode, cval,
*extra_arguments, **extra_keywords)
output += tmp
else:
output[...] = input[...]
return return_value
@docfiller
def laplace(input, output=None, mode="reflect", cval=0.0):
"""N-dimensional Laplace filter based on approximate second derivatives.
Parameters
----------
%(input)s
%(output)s
%(mode)s
%(cval)s
Examples
--------
>>> from scipy import ndimage, misc
>>> import matplotlib.pyplot as plt
>>> ascent = misc.ascent()
>>> result = ndimage.laplace(ascent)
>>> plt.gray() # show the filtered result in grayscale
>>> plt.imshow(result)
"""
def derivative2(input, axis, output, mode, cval):
return correlate1d(input, [1, -2, 1], axis, output, mode, cval, 0)
return generic_laplace(input, derivative2, output, mode, cval)
@docfiller
def gaussian_laplace(input, sigma, output=None, mode="reflect",
cval=0.0, **kwargs):
"""Multidimensional Laplace filter using gaussian second derivatives.
Parameters
----------
%(input)s
sigma : scalar or sequence of scalars
The standard deviations of the Gaussian filter are given for
each axis as a sequence, or as a single number, in which case
it is equal for all axes.
%(output)s
%(mode)s
%(cval)s
Extra keyword arguments will be passed to gaussian_filter().
Examples
--------
>>> from scipy import ndimage, misc
>>> import matplotlib.pyplot as plt
>>> ascent = misc.ascent()
>>> fig = plt.figure()
>>> plt.gray() # show the filtered result in grayscale
>>> ax1 = fig.add_subplot(121) # left side
>>> ax2 = fig.add_subplot(122) # right side
>>> result = ndimage.gaussian_laplace(ascent, sigma=1)
>>> ax1.imshow(result)
>>> result = ndimage.gaussian_laplace(ascent, sigma=3)
>>> ax2.imshow(result)
>>> plt.show()
"""
input = numpy.asarray(input)
def derivative2(input, axis, output, mode, cval, sigma, **kwargs):
order = [0] * input.ndim
order[axis] = 2
return gaussian_filter(input, sigma, order, output, mode, cval,
**kwargs)
return generic_laplace(input, derivative2, output, mode, cval,
extra_arguments=(sigma,),
extra_keywords=kwargs)
@docfiller
def generic_gradient_magnitude(input, derivative, output=None,
mode="reflect", cval=0.0,
extra_arguments=(), extra_keywords = None):
"""Gradient magnitude using a provided gradient function.
Parameters
----------
%(input)s
derivative : callable
Callable with the following signature::
derivative(input, axis, output, mode, cval,
*extra_arguments, **extra_keywords)
See `extra_arguments`, `extra_keywords` below.
`derivative` can assume that `input` and `output` are ndarrays.
Note that the output from `derivative` is modified inplace;
be careful to copy important inputs before returning them.
%(output)s
%(mode)s
%(cval)s
%(extra_keywords)s
%(extra_arguments)s
"""
if extra_keywords is None:
extra_keywords = {}
input = numpy.asarray(input)
output, return_value = _ni_support._get_output(output, input)
axes = list(range(input.ndim))
if len(axes) > 0:
derivative(input, axes[0], output, mode, cval,
*extra_arguments, **extra_keywords)
numpy.multiply(output, output, output)
for ii in range(1, len(axes)):
tmp = derivative(input, axes[ii], output.dtype, mode, cval,
*extra_arguments, **extra_keywords)
numpy.multiply(tmp, tmp, tmp)
output += tmp
# This allows the sqrt to work with a different default casting
numpy.sqrt(output, output, casting='unsafe')
else:
output[...] = input[...]
return return_value
@docfiller
def gaussian_gradient_magnitude(input, sigma, output=None,
mode="reflect", cval=0.0, **kwargs):
"""Multidimensional gradient magnitude using Gaussian derivatives.
Parameters
----------
%(input)s
sigma : scalar or sequence of scalars
The standard deviations of the Gaussian filter are given for
each axis as a sequence, or as a single number, in which case
it is equal for all axes..
%(output)s
%(mode)s
%(cval)s
Extra keyword arguments will be passed to gaussian_filter().
"""
input = numpy.asarray(input)
def derivative(input, axis, output, mode, cval, sigma, **kwargs):
order = [0] * input.ndim
order[axis] = 1
return gaussian_filter(input, sigma, order, output, mode,
cval, **kwargs)
return generic_gradient_magnitude(input, derivative, output, mode,
cval, extra_arguments=(sigma,),
extra_keywords=kwargs)
def _correlate_or_convolve(input, weights, output, mode, cval, origin,
convolution):
input = numpy.asarray(input)
if numpy.iscomplexobj(input):
raise TypeError('Complex type not supported')
origins = _ni_support._normalize_sequence(origin, input.ndim)
weights = numpy.asarray(weights, dtype=numpy.float64)
wshape = [ii for ii in weights.shape if ii > 0]
if len(wshape) != input.ndim:
raise RuntimeError('filter weights array has incorrect shape.')
if convolution:
weights = weights[tuple([slice(None, None, -1)] * weights.ndim)]
for ii in range(len(origins)):
origins[ii] = -origins[ii]
if not weights.shape[ii] & 1:
origins[ii] -= 1
for origin, lenw in zip(origins, wshape):
if (lenw // 2 + origin < 0) or (lenw // 2 + origin > lenw):
raise ValueError('invalid origin')
if not weights.flags.contiguous:
weights = weights.copy()
output, return_value = _ni_support._get_output(output, input)
mode = _ni_support._extend_mode_to_code(mode)
_nd_image.correlate(input, weights, output, mode, cval, origins)
return return_value
@docfiller
def correlate(input, weights, output=None, mode='reflect', cval=0.0,
origin=0):
"""
Multi-dimensional correlation.
The array is correlated with the given kernel.
Parameters
----------
input : array-like
input array to filter
weights : ndarray
array of weights, same number of dimensions as input
output : array, optional
The ``output`` parameter passes an array in which to store the
filter output.
mode : {'reflect','constant','nearest','mirror', 'wrap'}, optional
The ``mode`` parameter determines how the array borders are
handled, where ``cval`` is the value when mode is equal to
'constant'. Default is 'reflect'
cval : scalar, optional
Value to fill past edges of input if ``mode`` is 'constant'. Default
is 0.0
origin : scalar, optional
The ``origin`` parameter controls the placement of the filter.
Default 0
See Also
--------
convolve : Convolve an image with a kernel.
"""
return _correlate_or_convolve(input, weights, output, mode, cval,
origin, False)
@docfiller
def convolve(input, weights, output=None, mode='reflect', cval=0.0,
origin=0):
"""
Multidimensional convolution.
The array is convolved with the given kernel.
Parameters
----------
input : array_like
Input array to filter.
weights : array_like
Array of weights, same number of dimensions as input
output : ndarray, optional
The `output` parameter passes an array in which to store the
filter output.
mode : {'reflect','constant','nearest','mirror', 'wrap'}, optional
the `mode` parameter determines how the array borders are
handled. For 'constant' mode, values beyond borders are set to be
`cval`. Default is 'reflect'.
cval : scalar, optional
Value to fill past edges of input if `mode` is 'constant'. Default
is 0.0
origin : array_like, optional
The `origin` parameter controls the placement of the filter,
relative to the centre of the current element of the input.
Default of 0 is equivalent to ``(0,)*input.ndim``.
Returns
-------
result : ndarray
The result of convolution of `input` with `weights`.
See Also
--------
correlate : Correlate an image with a kernel.
Notes
-----
Each value in result is :math:`C_i = \\sum_j{I_{i+k-j} W_j}`, where
W is the `weights` kernel,
j is the n-D spatial index over :math:`W`,
I is the `input` and k is the coordinate of the center of
W, specified by `origin` in the input parameters.
Examples
--------
Perhaps the simplest case to understand is ``mode='constant', cval=0.0``,
because in this case borders (i.e. where the `weights` kernel, centered
on any one value, extends beyond an edge of `input`.
>>> a = np.array([[1, 2, 0, 0],
... [5, 3, 0, 4],
... [0, 0, 0, 7],
... [9, 3, 0, 0]])
>>> k = np.array([[1,1,1],[1,1,0],[1,0,0]])
>>> from scipy import ndimage
>>> ndimage.convolve(a, k, mode='constant', cval=0.0)
array([[11, 10, 7, 4],
[10, 3, 11, 11],
[15, 12, 14, 7],
[12, 3, 7, 0]])
Setting ``cval=1.0`` is equivalent to padding the outer edge of `input`
with 1.0's (and then extracting only the original region of the result).
>>> ndimage.convolve(a, k, mode='constant', cval=1.0)
array([[13, 11, 8, 7],
[11, 3, 11, 14],
[16, 12, 14, 10],
[15, 6, 10, 5]])
With ``mode='reflect'`` (the default), outer values are reflected at the
edge of `input` to fill in missing values.
>>> b = np.array([[2, 0, 0],
... [1, 0, 0],
... [0, 0, 0]])
>>> k = np.array([[0,1,0], [0,1,0], [0,1,0]])
>>> ndimage.convolve(b, k, mode='reflect')
array([[5, 0, 0],
[3, 0, 0],
[1, 0, 0]])
This includes diagonally at the corners.
>>> k = np.array([[1,0,0],[0,1,0],[0,0,1]])
>>> ndimage.convolve(b, k)
array([[4, 2, 0],
[3, 2, 0],
[1, 1, 0]])
With ``mode='nearest'``, the single nearest value in to an edge in
`input` is repeated as many times as needed to match the overlapping
`weights`.
>>> c = np.array([[2, 0, 1],
... [1, 0, 0],
... [0, 0, 0]])
>>> k = np.array([[0, 1, 0],
... [0, 1, 0],
... [0, 1, 0],
... [0, 1, 0],
... [0, 1, 0]])
>>> ndimage.convolve(c, k, mode='nearest')
array([[7, 0, 3],
[5, 0, 2],
[3, 0, 1]])
"""
return _correlate_or_convolve(input, weights, output, mode, cval,
origin, True)
@docfiller
def uniform_filter1d(input, size, axis=-1, output=None,
mode="reflect", cval=0.0, origin=0):
"""Calculate a one-dimensional uniform filter along the given axis.
The lines of the array along the given axis are filtered with a
uniform filter of given size.
Parameters
----------
%(input)s
size : int
length of uniform filter
%(axis)s
%(output)s
%(mode)s
%(cval)s
%(origin)s
"""
input = numpy.asarray(input)
if numpy.iscomplexobj(input):
raise TypeError('Complex type not supported')
axis = _ni_support._check_axis(axis, input.ndim)
if size < 1:
raise RuntimeError('incorrect filter size')
output, return_value = _ni_support._get_output(output, input)
if (size // 2 + origin < 0) or (size // 2 + origin >= size):
raise ValueError('invalid origin')
mode = _ni_support._extend_mode_to_code(mode)
_nd_image.uniform_filter1d(input, size, axis, output, mode, cval,
origin)
return return_value
@docfiller
def uniform_filter(input, size=3, output=None, mode="reflect",
cval=0.0, origin=0):
"""Multi-dimensional uniform filter.
Parameters
----------
%(input)s
size : int or sequence of ints, optional
The sizes of the uniform filter are given for each axis as a
sequence, or as a single number, in which case the size is
equal for all axes.
%(output)s
%(mode)s
%(cval)s
%(origin)s
Notes
-----
The multi-dimensional filter is implemented as a sequence of
one-dimensional uniform filters. The intermediate arrays are stored
in the same data type as the output. Therefore, for output types
with a limited precision, the results may be imprecise because
intermediate results may be stored with insufficient precision.
"""
input = numpy.asarray(input)
output, return_value = _ni_support._get_output(output, input)
sizes = _ni_support._normalize_sequence(size, input.ndim)
origins = _ni_support._normalize_sequence(origin, input.ndim)
axes = list(range(input.ndim))
axes = [(axes[ii], sizes[ii], origins[ii])
for ii in range(len(axes)) if sizes[ii] > 1]
if len(axes) > 0:
for axis, size, origin in axes:
uniform_filter1d(input, int(size), axis, output, mode,
cval, origin)
input = output
else:
output[...] = input[...]
return return_value
@docfiller
def minimum_filter1d(input, size, axis=-1, output=None,
mode="reflect", cval=0.0, origin=0):
"""Calculate a one-dimensional minimum filter along the given axis.
The lines of the array along the given axis are filtered with a
minimum filter of given size.
Parameters
----------
%(input)s
size : int
length along which to calculate 1D minimum
%(axis)s
%(output)s
%(mode)s
%(cval)s
%(origin)s
Notes
-----
This function implements the MINLIST algorithm [1]_, as described by
Richard Harter [2]_, and has a guaranteed O(n) performance, `n` being
the `input` length, regardless of filter size.
References
----------
.. [1] http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.42.2777
.. [2] http://www.richardhartersworld.com/cri/2001/slidingmin.html
"""
input = numpy.asarray(input)
if numpy.iscomplexobj(input):
raise TypeError('Complex type not supported')
axis = _ni_support._check_axis(axis, input.ndim)
if size < 1:
raise RuntimeError('incorrect filter size')
output, return_value = _ni_support._get_output(output, input)
if (size // 2 + origin < 0) or (size // 2 + origin >= size):
raise ValueError('invalid origin')
mode = _ni_support._extend_mode_to_code(mode)
_nd_image.min_or_max_filter1d(input, size, axis, output, mode, cval,
origin, 1)
return return_value
@docfiller
def maximum_filter1d(input, size, axis=-1, output=None,
mode="reflect", cval=0.0, origin=0):
"""Calculate a one-dimensional maximum filter along the given axis.
The lines of the array along the given axis are filtered with a
maximum filter of given size.
Parameters
----------
%(input)s
size : int
Length along which to calculate the 1-D maximum.
%(axis)s
%(output)s
%(mode)s
%(cval)s
%(origin)s
Returns
-------
maximum1d : ndarray, None
Maximum-filtered array with same shape as input.
None if `output` is not None
Notes
-----
This function implements the MAXLIST algorithm [1]_, as described by
Richard Harter [2]_, and has a guaranteed O(n) performance, `n` being
the `input` length, regardless of filter size.
References
----------
.. [1] http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.42.2777
.. [2] http://www.richardhartersworld.com/cri/2001/slidingmin.html
"""
input = numpy.asarray(input)
if numpy.iscomplexobj(input):
raise TypeError('Complex type not supported')
axis = _ni_support._check_axis(axis, input.ndim)
if size < 1:
raise RuntimeError('incorrect filter size')
output, return_value = _ni_support._get_output(output, input)
if (size // 2 + origin < 0) or (size // 2 + origin >= size):
raise ValueError('invalid origin')
mode = _ni_support._extend_mode_to_code(mode)
_nd_image.min_or_max_filter1d(input, size, axis, output, mode, cval,
origin, 0)
return return_value
def _min_or_max_filter(input, size, footprint, structure, output, mode,
cval, origin, minimum):
if structure is None:
if footprint is None:
if size is None:
raise RuntimeError("no footprint provided")
separable = True
else:
footprint = numpy.asarray(footprint)
footprint = footprint.astype(bool)
if numpy.alltrue(numpy.ravel(footprint), axis=0):
size = footprint.shape
footprint = None
separable = True
else:
separable = False
else:
structure = numpy.asarray(structure, dtype=numpy.float64)
separable = False
if footprint is None:
footprint = numpy.ones(structure.shape, bool)
else:
footprint = numpy.asarray(footprint)
footprint = footprint.astype(bool)
input = numpy.asarray(input)
if numpy.iscomplexobj(input):
raise TypeError('Complex type not supported')
output, return_value = _ni_support._get_output(output, input)
origins = _ni_support._normalize_sequence(origin, input.ndim)
if separable:
sizes = _ni_support._normalize_sequence(size, input.ndim)
axes = list(range(input.ndim))
axes = [(axes[ii], sizes[ii], origins[ii])
for ii in range(len(axes)) if sizes[ii] > 1]
if minimum:
filter_ = minimum_filter1d
else:
filter_ = maximum_filter1d
if len(axes) > 0:
for axis, size, origin in axes:
filter_(input, int(size), axis, output, mode, cval, origin)
input = output
else:
output[...] = input[...]
else:
fshape = [ii for ii in footprint.shape if ii > 0]
if len(fshape) != input.ndim:
raise RuntimeError('footprint array has incorrect shape.')
for origin, lenf in zip(origins, fshape):
if (lenf // 2 + origin < 0) or (lenf // 2 + origin >= lenf):
raise ValueError('invalid origin')
if not footprint.flags.contiguous:
footprint = footprint.copy()
if structure is not None:
if len(structure.shape) != input.ndim:
raise RuntimeError('structure array has incorrect shape')
if not structure.flags.contiguous:
structure = structure.copy()
mode = _ni_support._extend_mode_to_code(mode)
_nd_image.min_or_max_filter(input, footprint, structure, output,
mode, cval, origins, minimum)
return return_value
@docfiller
def minimum_filter(input, size=None, footprint=None, output=None,
mode="reflect", cval=0.0, origin=0):
"""Calculates a multi-dimensional minimum filter.
Parameters
----------
%(input)s
%(size_foot)s
%(output)s
%(mode)s
%(cval)s
%(origin)s
"""
return _min_or_max_filter(input, size, footprint, None, output, mode,
cval, origin, 1)
@docfiller
def maximum_filter(input, size=None, footprint=None, output=None,
mode="reflect", cval=0.0, origin=0):
"""Calculates a multi-dimensional maximum filter.
Parameters
----------
%(input)s
%(size_foot)s
%(output)s
%(mode)s
%(cval)s
%(origin)s
"""
return _min_or_max_filter(input, size, footprint, None, output, mode,
cval, origin, 0)
@docfiller
def _rank_filter(input, rank, size=None, footprint=None, output=None,
mode="reflect", cval=0.0, origin=0, operation='rank'):
input = numpy.asarray(input)
if numpy.iscomplexobj(input):
raise TypeError('Complex type not supported')
origins = _ni_support._normalize_sequence(origin, input.ndim)
if footprint is None:
if size is None:
raise RuntimeError("no footprint or filter size provided")
sizes = _ni_support._normalize_sequence(size, input.ndim)
footprint = numpy.ones(sizes, dtype=bool)
else:
footprint = numpy.asarray(footprint, dtype=bool)
fshape = [ii for ii in footprint.shape if ii > 0]
if len(fshape) != input.ndim:
raise RuntimeError('filter footprint array has incorrect shape.')
for origin, lenf in zip(origins, fshape):
if (lenf // 2 + origin < 0) or (lenf // 2 + origin >= lenf):
raise ValueError('invalid origin')
if not footprint.flags.contiguous:
footprint = footprint.copy()
filter_size = numpy.where(footprint, 1, 0).sum()
if operation == 'median':
rank = filter_size // 2
elif operation == 'percentile':
percentile = rank
if percentile < 0.0:
percentile += 100.0
if percentile < 0 or percentile > 100:
raise RuntimeError('invalid percentile')
if percentile == 100.0:
rank = filter_size - 1
else:
rank = int(float(filter_size) * percentile / 100.0)
if rank < 0:
rank += filter_size
if rank < 0 or rank >= filter_size:
raise RuntimeError('rank not within filter footprint size')
if rank == 0:
return minimum_filter(input, None, footprint, output, mode, cval,
origins)
elif rank == filter_size - 1:
return maximum_filter(input, None, footprint, output, mode, cval,
origins)
else:
output, return_value = _ni_support._get_output(output, input)
mode = _ni_support._extend_mode_to_code(mode)
_nd_image.rank_filter(input, rank, footprint, output, mode, cval,
origins)
return return_value
@docfiller
def rank_filter(input, rank, size=None, footprint=None, output=None,
mode="reflect", cval=0.0, origin=0):
"""Calculates a multi-dimensional rank filter.
Parameters
----------
%(input)s
rank : int
The rank parameter may be less then zero, i.e., rank = -1
indicates the largest element.
%(size_foot)s
%(output)s
%(mode)s
%(cval)s
%(origin)s
"""
return _rank_filter(input, rank, size, footprint, output, mode, cval,
origin, 'rank')
@docfiller
def median_filter(input, size=None, footprint=None, output=None,
mode="reflect", cval=0.0, origin=0):
"""
Calculates a multidimensional median filter.
Parameters
----------
%(input)s
%(size_foot)s
%(output)s
%(mode)s
%(cval)s
%(origin)s
Returns
-------
median_filter : ndarray
Return of same shape as `input`.
"""
return _rank_filter(input, 0, size, footprint, output, mode, cval,
origin, 'median')
@docfiller
def percentile_filter(input, percentile, size=None, footprint=None,
output=None, mode="reflect", cval=0.0, origin=0):
"""Calculates a multi-dimensional percentile filter.
Parameters
----------
%(input)s
percentile : scalar
The percentile parameter may be less then zero, i.e.,
percentile = -20 equals percentile = 80
%(size_foot)s
%(output)s
%(mode)s
%(cval)s
%(origin)s
"""
return _rank_filter(input, percentile, size, footprint, output, mode,
cval, origin, 'percentile')
@docfiller
def generic_filter1d(input, function, filter_size, axis=-1,
output=None, mode="reflect", cval=0.0, origin=0,
extra_arguments=(), extra_keywords = None):
"""Calculate a one-dimensional filter along the given axis.
`generic_filter1d` iterates over the lines of the array, calling the
given function at each line. The arguments of the line are the
input line, and the output line. The input and output lines are 1D
double arrays. The input line is extended appropriately according
to the filter size and origin. The output line must be modified
in-place with the result.
Parameters
----------
%(input)s
function : callable
Function to apply along given axis.
filter_size : scalar
Length of the filter.
%(axis)s
%(output)s
%(mode)s
%(cval)s
%(origin)s
%(extra_arguments)s
%(extra_keywords)s
"""
if extra_keywords is None:
extra_keywords = {}
input = numpy.asarray(input)
if numpy.iscomplexobj(input):
raise TypeError('Complex type not supported')
output, return_value = _ni_support._get_output(output, input)
if filter_size < 1:
raise RuntimeError('invalid filter size')
axis = _ni_support._check_axis(axis, input.ndim)
if (filter_size // 2 + origin < 0) or (filter_size // 2 + origin >=
filter_size):
raise ValueError('invalid origin')
mode = _ni_support._extend_mode_to_code(mode)
_nd_image.generic_filter1d(input, function, filter_size, axis, output,
mode, cval, origin, extra_arguments, extra_keywords)
return return_value
@docfiller
def generic_filter(input, function, size=None, footprint=None,
output=None, mode="reflect", cval=0.0, origin=0,
extra_arguments=(), extra_keywords = None):
"""Calculates a multi-dimensional filter using the given function.
At each element the provided function is called. The input values
within the filter footprint at that element are passed to the function
as a 1D array of double values.
Parameters
----------
%(input)s
function : callable
Function to apply at each element.
%(size_foot)s
%(output)s
%(mode)s
%(cval)s
%(origin)s
%(extra_arguments)s
%(extra_keywords)s
"""
if extra_keywords is None:
extra_keywords = {}
input = numpy.asarray(input)
if numpy.iscomplexobj(input):
raise TypeError('Complex type not supported')
origins = _ni_support._normalize_sequence(origin, input.ndim)
if footprint is None:
if size is None:
raise RuntimeError("no footprint or filter size provided")
sizes = _ni_support._normalize_sequence(size, input.ndim)
footprint = numpy.ones(sizes, dtype=bool)
else:
footprint = numpy.asarray(footprint)
footprint = footprint.astype(bool)
fshape = [ii for ii in footprint.shape if ii > 0]
if len(fshape) != input.ndim:
raise RuntimeError('filter footprint array has incorrect shape.')
for origin, lenf in zip(origins, fshape):
if (lenf // 2 + origin < 0) or (lenf // 2 + origin >= lenf):
raise ValueError('invalid origin')
if not footprint.flags.contiguous:
footprint = footprint.copy()
output, return_value = _ni_support._get_output(output, input)
mode = _ni_support._extend_mode_to_code(mode)
_nd_image.generic_filter(input, function, footprint, output, mode,
cval, origins, extra_arguments, extra_keywords)
return return_value
| mit |
Lyleo/nupic | external/linux32/lib/python2.6/site-packages/matplotlib/projections/polar.py | 69 | 20981 | import math
import numpy as npy
import matplotlib
rcParams = matplotlib.rcParams
from matplotlib.artist import kwdocd
from matplotlib.axes import Axes
from matplotlib import cbook
from matplotlib.patches import Circle
from matplotlib.path import Path
from matplotlib.ticker import Formatter, Locator
from matplotlib.transforms import Affine2D, Affine2DBase, Bbox, \
BboxTransformTo, IdentityTransform, Transform, TransformWrapper
class PolarAxes(Axes):
"""
A polar graph projection, where the input dimensions are *theta*, *r*.
Theta starts pointing east and goes anti-clockwise.
"""
name = 'polar'
class PolarTransform(Transform):
"""
The base polar transform. This handles projection *theta* and
*r* into Cartesian coordinate space *x* and *y*, but does not
perform the ultimate affine transformation into the correct
position.
"""
input_dims = 2
output_dims = 2
is_separable = False
def __init__(self, resolution):
"""
Create a new polar transform. Resolution is the number of steps
to interpolate between each input line segment to approximate its
path in curved polar space.
"""
Transform.__init__(self)
self._resolution = resolution
def transform(self, tr):
xy = npy.zeros(tr.shape, npy.float_)
t = tr[:, 0:1]
r = tr[:, 1:2]
x = xy[:, 0:1]
y = xy[:, 1:2]
x[:] = r * npy.cos(t)
y[:] = r * npy.sin(t)
return xy
transform.__doc__ = Transform.transform.__doc__
transform_non_affine = transform
transform_non_affine.__doc__ = Transform.transform_non_affine.__doc__
def transform_path(self, path):
vertices = path.vertices
t = vertices[:, 0:1]
t[t != (npy.pi * 2.0)] %= (npy.pi * 2.0)
if len(vertices) == 2 and vertices[0, 0] == vertices[1, 0]:
return Path(self.transform(vertices), path.codes)
ipath = path.interpolated(self._resolution)
return Path(self.transform(ipath.vertices), ipath.codes)
transform_path.__doc__ = Transform.transform_path.__doc__
transform_path_non_affine = transform_path
transform_path_non_affine.__doc__ = Transform.transform_path_non_affine.__doc__
def inverted(self):
return PolarAxes.InvertedPolarTransform(self._resolution)
inverted.__doc__ = Transform.inverted.__doc__
class PolarAffine(Affine2DBase):
"""
The affine part of the polar projection. Scales the output so
that maximum radius rests on the edge of the axes circle.
"""
def __init__(self, scale_transform, limits):
u"""
*limits* is the view limit of the data. The only part of
its bounds that is used is ymax (for the radius maximum).
The theta range is always fixed to (0, 2\u03c0).
"""
Affine2DBase.__init__(self)
self._scale_transform = scale_transform
self._limits = limits
self.set_children(scale_transform, limits)
self._mtx = None
def get_matrix(self):
if self._invalid:
limits_scaled = self._limits.transformed(self._scale_transform)
ymax = limits_scaled.ymax
affine = Affine2D() \
.scale(0.5 / ymax) \
.translate(0.5, 0.5)
self._mtx = affine.get_matrix()
self._inverted = None
self._invalid = 0
return self._mtx
get_matrix.__doc__ = Affine2DBase.get_matrix.__doc__
class InvertedPolarTransform(Transform):
"""
The inverse of the polar transform, mapping Cartesian
coordinate space *x* and *y* back to *theta* and *r*.
"""
input_dims = 2
output_dims = 2
is_separable = False
def __init__(self, resolution):
Transform.__init__(self)
self._resolution = resolution
def transform(self, xy):
x = xy[:, 0:1]
y = xy[:, 1:]
r = npy.sqrt(x*x + y*y)
theta = npy.arccos(x / r)
theta = npy.where(y < 0, 2 * npy.pi - theta, theta)
return npy.concatenate((theta, r), 1)
transform.__doc__ = Transform.transform.__doc__
def inverted(self):
return PolarAxes.PolarTransform(self._resolution)
inverted.__doc__ = Transform.inverted.__doc__
class ThetaFormatter(Formatter):
u"""
Used to format the *theta* tick labels. Converts the
native unit of radians into degrees and adds a degree symbol
(\u00b0).
"""
def __call__(self, x, pos=None):
# \u00b0 : degree symbol
if rcParams['text.usetex'] and not rcParams['text.latex.unicode']:
return r"$%0.0f^\circ$" % ((x / npy.pi) * 180.0)
else:
# we use unicode, rather than mathtext with \circ, so
# that it will work correctly with any arbitrary font
# (assuming it has a degree sign), whereas $5\circ$
# will only work correctly with one of the supported
# math fonts (Computer Modern and STIX)
return u"%0.0f\u00b0" % ((x / npy.pi) * 180.0)
class RadialLocator(Locator):
"""
Used to locate radius ticks.
Ensures that all ticks are strictly positive. For all other
tasks, it delegates to the base
:class:`~matplotlib.ticker.Locator` (which may be different
depending on the scale of the *r*-axis.
"""
def __init__(self, base):
self.base = base
def __call__(self):
ticks = self.base()
return [x for x in ticks if x > 0]
def autoscale(self):
return self.base.autoscale()
def pan(self, numsteps):
return self.base.pan(numsteps)
def zoom(self, direction):
return self.base.zoom(direction)
def refresh(self):
return self.base.refresh()
RESOLUTION = 75
def __init__(self, *args, **kwargs):
"""
Create a new Polar Axes for a polar plot.
"""
self._rpad = 0.05
self.resolution = kwargs.pop('resolution', self.RESOLUTION)
Axes.__init__(self, *args, **kwargs)
self.set_aspect('equal', adjustable='box', anchor='C')
self.cla()
__init__.__doc__ = Axes.__init__.__doc__
def cla(self):
Axes.cla(self)
self.title.set_y(1.05)
self.xaxis.set_major_formatter(self.ThetaFormatter())
angles = npy.arange(0.0, 360.0, 45.0)
self.set_thetagrids(angles)
self.yaxis.set_major_locator(self.RadialLocator(self.yaxis.get_major_locator()))
self.grid(rcParams['polaraxes.grid'])
self.xaxis.set_ticks_position('none')
self.yaxis.set_ticks_position('none')
def _set_lim_and_transforms(self):
self.transAxes = BboxTransformTo(self.bbox)
# Transforms the x and y axis separately by a scale factor
# It is assumed that this part will have non-linear components
self.transScale = TransformWrapper(IdentityTransform())
# A (possibly non-linear) projection on the (already scaled) data
self.transProjection = self.PolarTransform(self.resolution)
# An affine transformation on the data, generally to limit the
# range of the axes
self.transProjectionAffine = self.PolarAffine(self.transScale, self.viewLim)
# The complete data transformation stack -- from data all the
# way to display coordinates
self.transData = self.transScale + self.transProjection + \
(self.transProjectionAffine + self.transAxes)
# This is the transform for theta-axis ticks. It is
# equivalent to transData, except it always puts r == 1.0 at
# the edge of the axis circle.
self._xaxis_transform = (
self.transProjection +
self.PolarAffine(IdentityTransform(), Bbox.unit()) +
self.transAxes)
# The theta labels are moved from radius == 0.0 to radius == 1.1
self._theta_label1_position = Affine2D().translate(0.0, 1.1)
self._xaxis_text1_transform = (
self._theta_label1_position +
self._xaxis_transform)
self._theta_label2_position = Affine2D().translate(0.0, 1.0 / 1.1)
self._xaxis_text2_transform = (
self._theta_label2_position +
self._xaxis_transform)
# This is the transform for r-axis ticks. It scales the theta
# axis so the gridlines from 0.0 to 1.0, now go from 0.0 to
# 2pi.
self._yaxis_transform = (
Affine2D().scale(npy.pi * 2.0, 1.0) +
self.transData)
# The r-axis labels are put at an angle and padded in the r-direction
self._r_label1_position = Affine2D().translate(22.5, self._rpad)
self._yaxis_text1_transform = (
self._r_label1_position +
Affine2D().scale(1.0 / 360.0, 1.0) +
self._yaxis_transform
)
self._r_label2_position = Affine2D().translate(22.5, self._rpad)
self._yaxis_text2_transform = (
self._r_label2_position +
Affine2D().scale(1.0 / 360.0, 1.0) +
self._yaxis_transform
)
def get_xaxis_transform(self):
return self._xaxis_transform
def get_xaxis_text1_transform(self, pad):
return self._xaxis_text1_transform, 'center', 'center'
def get_xaxis_text2_transform(self, pad):
return self._xaxis_text2_transform, 'center', 'center'
def get_yaxis_transform(self):
return self._yaxis_transform
def get_yaxis_text1_transform(self, pad):
return self._yaxis_text1_transform, 'center', 'center'
def get_yaxis_text2_transform(self, pad):
return self._yaxis_text2_transform, 'center', 'center'
def _gen_axes_patch(self):
return Circle((0.5, 0.5), 0.5)
def set_rmax(self, rmax):
self.viewLim.y1 = rmax
angle = self._r_label1_position.to_values()[4]
self._r_label1_position.clear().translate(
angle, rmax * self._rpad)
self._r_label2_position.clear().translate(
angle, -rmax * self._rpad)
def get_rmax(self):
return self.viewLim.ymax
def set_yscale(self, *args, **kwargs):
Axes.set_yscale(self, *args, **kwargs)
self.yaxis.set_major_locator(
self.RadialLocator(self.yaxis.get_major_locator()))
set_rscale = Axes.set_yscale
set_rticks = Axes.set_yticks
def set_thetagrids(self, angles, labels=None, frac=None,
**kwargs):
"""
Set the angles at which to place the theta grids (these
gridlines are equal along the theta dimension). *angles* is in
degrees.
*labels*, if not None, is a ``len(angles)`` list of strings of
the labels to use at each angle.
If *labels* is None, the labels will be ``fmt %% angle``
*frac* is the fraction of the polar axes radius at which to
place the label (1 is the edge). Eg. 1.05 is outside the axes
and 0.95 is inside the axes.
Return value is a list of tuples (*line*, *label*), where
*line* is :class:`~matplotlib.lines.Line2D` instances and the
*label* is :class:`~matplotlib.text.Text` instances.
kwargs are optional text properties for the labels:
%(Text)s
ACCEPTS: sequence of floats
"""
angles = npy.asarray(angles, npy.float_)
self.set_xticks(angles * (npy.pi / 180.0))
if labels is not None:
self.set_xticklabels(labels)
if frac is not None:
self._theta_label1_position.clear().translate(0.0, frac)
self._theta_label2_position.clear().translate(0.0, 1.0 / frac)
for t in self.xaxis.get_ticklabels():
t.update(kwargs)
return self.xaxis.get_ticklines(), self.xaxis.get_ticklabels()
set_thetagrids.__doc__ = cbook.dedent(set_thetagrids.__doc__) % kwdocd
def set_rgrids(self, radii, labels=None, angle=None, rpad=None, **kwargs):
"""
Set the radial locations and labels of the *r* grids.
The labels will appear at radial distances *radii* at the
given *angle* in degrees.
*labels*, if not None, is a ``len(radii)`` list of strings of the
labels to use at each radius.
If *labels* is None, the built-in formatter will be used.
*rpad* is a fraction of the max of *radii* which will pad each of
the radial labels in the radial direction.
Return value is a list of tuples (*line*, *label*), where
*line* is :class:`~matplotlib.lines.Line2D` instances and the
*label* is :class:`~matplotlib.text.Text` instances.
kwargs are optional text properties for the labels:
%(Text)s
ACCEPTS: sequence of floats
"""
radii = npy.asarray(radii)
rmin = radii.min()
if rmin <= 0:
raise ValueError('radial grids must be strictly positive')
self.set_yticks(radii)
if labels is not None:
self.set_yticklabels(labels)
if angle is None:
angle = self._r_label1_position.to_values()[4]
if rpad is not None:
self._rpad = rpad
rmax = self.get_rmax()
self._r_label1_position.clear().translate(angle, self._rpad * rmax)
self._r_label2_position.clear().translate(angle, -self._rpad * rmax)
for t in self.yaxis.get_ticklabels():
t.update(kwargs)
return self.yaxis.get_ticklines(), self.yaxis.get_ticklabels()
set_rgrids.__doc__ = cbook.dedent(set_rgrids.__doc__) % kwdocd
def set_xscale(self, scale, *args, **kwargs):
if scale != 'linear':
raise NotImplementedError("You can not set the xscale on a polar plot.")
def set_xlim(self, *args, **kargs):
# The xlim is fixed, no matter what you do
self.viewLim.intervalx = (0.0, npy.pi * 2.0)
def format_coord(self, theta, r):
"""
Return a format string formatting the coordinate using Unicode
characters.
"""
theta /= math.pi
# \u03b8: lower-case theta
# \u03c0: lower-case pi
# \u00b0: degree symbol
return u'\u03b8=%0.3f\u03c0 (%0.3f\u00b0), r=%0.3f' % (theta, theta * 180.0, r)
def get_data_ratio(self):
'''
Return the aspect ratio of the data itself. For a polar plot,
this should always be 1.0
'''
return 1.0
### Interactive panning
def can_zoom(self):
"""
Return True if this axes support the zoom box
"""
return False
def start_pan(self, x, y, button):
angle = self._r_label1_position.to_values()[4] / 180.0 * npy.pi
mode = ''
if button == 1:
epsilon = npy.pi / 45.0
t, r = self.transData.inverted().transform_point((x, y))
if t >= angle - epsilon and t <= angle + epsilon:
mode = 'drag_r_labels'
elif button == 3:
mode = 'zoom'
self._pan_start = cbook.Bunch(
rmax = self.get_rmax(),
trans = self.transData.frozen(),
trans_inverse = self.transData.inverted().frozen(),
r_label_angle = self._r_label1_position.to_values()[4],
x = x,
y = y,
mode = mode
)
def end_pan(self):
del self._pan_start
def drag_pan(self, button, key, x, y):
p = self._pan_start
if p.mode == 'drag_r_labels':
startt, startr = p.trans_inverse.transform_point((p.x, p.y))
t, r = p.trans_inverse.transform_point((x, y))
# Deal with theta
dt0 = t - startt
dt1 = startt - t
if abs(dt1) < abs(dt0):
dt = abs(dt1) * sign(dt0) * -1.0
else:
dt = dt0 * -1.0
dt = (dt / npy.pi) * 180.0
rpad = self._r_label1_position.to_values()[5]
self._r_label1_position.clear().translate(
p.r_label_angle - dt, rpad)
self._r_label2_position.clear().translate(
p.r_label_angle - dt, -rpad)
elif p.mode == 'zoom':
startt, startr = p.trans_inverse.transform_point((p.x, p.y))
t, r = p.trans_inverse.transform_point((x, y))
dr = r - startr
# Deal with r
scale = r / startr
self.set_rmax(p.rmax / scale)
# These are a couple of aborted attempts to project a polar plot using
# cubic bezier curves.
# def transform_path(self, path):
# twopi = 2.0 * npy.pi
# halfpi = 0.5 * npy.pi
# vertices = path.vertices
# t0 = vertices[0:-1, 0]
# t1 = vertices[1: , 0]
# td = npy.where(t1 > t0, t1 - t0, twopi - (t0 - t1))
# maxtd = td.max()
# interpolate = npy.ceil(maxtd / halfpi)
# if interpolate > 1.0:
# vertices = self.interpolate(vertices, interpolate)
# vertices = self.transform(vertices)
# result = npy.zeros((len(vertices) * 3 - 2, 2), npy.float_)
# codes = mpath.Path.CURVE4 * npy.ones((len(vertices) * 3 - 2, ), mpath.Path.code_type)
# result[0] = vertices[0]
# codes[0] = mpath.Path.MOVETO
# kappa = 4.0 * ((npy.sqrt(2.0) - 1.0) / 3.0)
# kappa = 0.5
# p0 = vertices[0:-1]
# p1 = vertices[1: ]
# x0 = p0[:, 0:1]
# y0 = p0[:, 1: ]
# b0 = ((y0 - x0) - y0) / ((x0 + y0) - x0)
# a0 = y0 - b0*x0
# x1 = p1[:, 0:1]
# y1 = p1[:, 1: ]
# b1 = ((y1 - x1) - y1) / ((x1 + y1) - x1)
# a1 = y1 - b1*x1
# x = -(a0-a1) / (b0-b1)
# y = a0 + b0*x
# xk = (x - x0) * kappa + x0
# yk = (y - y0) * kappa + y0
# result[1::3, 0:1] = xk
# result[1::3, 1: ] = yk
# xk = (x - x1) * kappa + x1
# yk = (y - y1) * kappa + y1
# result[2::3, 0:1] = xk
# result[2::3, 1: ] = yk
# result[3::3] = p1
# print vertices[-2:]
# print result[-2:]
# return mpath.Path(result, codes)
# twopi = 2.0 * npy.pi
# halfpi = 0.5 * npy.pi
# vertices = path.vertices
# t0 = vertices[0:-1, 0]
# t1 = vertices[1: , 0]
# td = npy.where(t1 > t0, t1 - t0, twopi - (t0 - t1))
# maxtd = td.max()
# interpolate = npy.ceil(maxtd / halfpi)
# print "interpolate", interpolate
# if interpolate > 1.0:
# vertices = self.interpolate(vertices, interpolate)
# result = npy.zeros((len(vertices) * 3 - 2, 2), npy.float_)
# codes = mpath.Path.CURVE4 * npy.ones((len(vertices) * 3 - 2, ), mpath.Path.code_type)
# result[0] = vertices[0]
# codes[0] = mpath.Path.MOVETO
# kappa = 4.0 * ((npy.sqrt(2.0) - 1.0) / 3.0)
# tkappa = npy.arctan(kappa)
# hyp_kappa = npy.sqrt(kappa*kappa + 1.0)
# t0 = vertices[0:-1, 0]
# t1 = vertices[1: , 0]
# r0 = vertices[0:-1, 1]
# r1 = vertices[1: , 1]
# td = npy.where(t1 > t0, t1 - t0, twopi - (t0 - t1))
# td_scaled = td / (npy.pi * 0.5)
# rd = r1 - r0
# r0kappa = r0 * kappa * td_scaled
# r1kappa = r1 * kappa * td_scaled
# ravg_kappa = ((r1 + r0) / 2.0) * kappa * td_scaled
# result[1::3, 0] = t0 + (tkappa * td_scaled)
# result[1::3, 1] = r0*hyp_kappa
# # result[1::3, 1] = r0 / npy.cos(tkappa * td_scaled) # npy.sqrt(r0*r0 + ravg_kappa*ravg_kappa)
# result[2::3, 0] = t1 - (tkappa * td_scaled)
# result[2::3, 1] = r1*hyp_kappa
# # result[2::3, 1] = r1 / npy.cos(tkappa * td_scaled) # npy.sqrt(r1*r1 + ravg_kappa*ravg_kappa)
# result[3::3, 0] = t1
# result[3::3, 1] = r1
# print vertices[:6], result[:6], t0[:6], t1[:6], td[:6], td_scaled[:6], tkappa
# result = self.transform(result)
# return mpath.Path(result, codes)
# transform_path_non_affine = transform_path
| gpl-3.0 |
edwardsmith999/pyDataView | postproclib/field.py | 1 | 25726 | #! /usr/bin/env python
import numpy as np
import sys
from .pplexceptions import OutsideRecRange
class Field():
"""
Abstract base class to be inherited by MDField, CFDField and CPLField.
Authors: Ed Smith 2019 & David Trevelyan (2010-2014)
Abstract base class template that generally specifies how data
should be processed and returned in postprocessing routines.
Field should be inherited by MDField (which will then, in turn,
be inherited by MD_mField for mass, etc.).
Fields are, essentially, data reformatters. They must be instantiated
with a RawData object, which is used to do the actual reading, and
the methods in Field re-package that data in an easily plottable
format.
TOP-LEVEL EXAMPLE
v = MD_vField(fdir)
y, v3 = v.profile(axis=1)
plt.plot(v3[:,0], y)
This will instantiate a velocity field (see MDFields for details of
the complex inheritance and containment), read and store the data
from multiple files to construct a velocity profile, and finally
will plot the x-component of velocity against the y-axis.
In line with RawData readers, axes correspond to:
[ spatial ax1, spatial ax2, spatial ax3, record, component ]
[ 0 , 1 , 2 , 3 , 4 (&-1) ]
"""
def __init__(self, RawDataObj):
self.Raw = RawDataObj
self.fdir = self.Raw.fdir
self.grid = self.Raw.grid
self.maxrec = self.Raw.maxrec
def read(self,startrec,endrec,**kwargs):
"""
TO BE OVERRIDDEN IN COMPLICATED FIELDS.
Method that returns grid data that is read by Raw.
"""
if (endrec > self.maxrec):
print(('Record ' + str(endrec) + ' is greater than the maximum '
'available (' + str(self.maxrec) + ').'))
raise OutsideRecRange
grid_data = self.Raw.read(startrec,endrec,**kwargs)
return grid_data
def read_halo(self,startrec,endrec,**kwargs):
"""
Method to read halo if Raw data reader supports it.
"""
try:
return self.Raw.read_halo(startrec,endrec,**kwargs)
except AttributeError:
raise
def averaged_data(self,startrec,endrec,avgaxes=(),**kwargs):
"""
TO BE OVERRIDDEN IN ALL COMPLICATED FIELDS.
Average the data in the user-specified way.
"""
# Read 4D time series from startrec to endrec
grid_data = self.read(startrec,endrec,**kwargs)
# Average over axes
if (avgaxes != ()):
grid_data = np.mean(grid_data,axis=avgaxes)
#return avg_data
return grid_data
def contour(self,axes,startrec=0,endrec=None,**kwargs):
"""
NOT TO BE OVERRIDDEN UNLESS ABSOLUTELY NECESSARY
Wrapper for averaged_data, returns easily plottable data
"""
avgaxes = [0,1,2,3]
avgaxes.remove(axes[0])
avgaxes.remove(axes[1])
avgaxes = tuple(avgaxes)
if (endrec==None):
endrec = self.maxrec
data = self.averaged_data(startrec,endrec,avgaxes=avgaxes,**kwargs)
# Need version 1.7.1 of numpy or higher
X, Y = np.meshgrid(self.grid[axes[0]],self.grid[axes[1]],indexing='ij')
return X, Y, data
def profile(self,axis,startrec=0,endrec=None,**kwargs):
"""
NOT TO BE OVERRIDDEN UNLESS ABSOLUTELY NECESSARY
Wrapper for averaged_data, returns easily plottable data
"""
avgaxes = [0,1,2,3]
avgaxes.remove(axis)
avgaxes = tuple(avgaxes)
if (endrec==None):
endrec = self.maxrec
data = self.averaged_data(startrec,endrec,avgaxes=avgaxes,**kwargs)
return self.grid[axis], data
def quiver(self,axes,components=None,startrec=0,endrec=None,**kwargs):
"""
NOT TO BE OVERRIDDEN UNLESS ABSOLUTELY NECESSARY
Wrapper for averaged_data, returns easily plottable data
"""
if (components==None):
components = axes
X, Y, data = self.contour(axes,startrec=startrec,endrec=endrec,**kwargs)
#avgaxes = [0,1,2,3]
#avgaxes.remove(axes[0])
#avgaxes.remove(axes[1])
#avgaxes = tuple(avgaxes)
#if (endrec==None):
# endrec = self.maxrec
#data = self.averaged_data(startrec,endrec,avgaxes=avgaxes,**kwargs)
# Need version 1.7.1 of numpy or higher
#X, Y = np.meshgrid(self.grid[axes[0]],self.grid[axes[1]],indexing='ij')
return X, Y, data[:,:,components]
class AxisManager():
def __init__(self):
self.nreduced = 0
self.axisactive = [True]*5
def reduce_axes(self,axes):
for axis in axes:
self.nreduced += 1
self.axisactive[axis] = False
def current_axis_number(self,axis_request):
if (not self.axisactive[axis_request]):
return None
n = 0
for otheraxis in range(axis_request):
if (not self.axisactive[otheraxis]):
n += 1
return int(axis_request - n)
def current_axes_numbers(self,axes_request):
newaxes = []
for axis in list(axes_request):
newaxes.append(self.current_axis_number(axis))
return tuple(newaxes)
def managed_mean(self, axismanager, data, avgaxes):
newaxes = axismanager.current_axes_numbers(avgaxes)
if (None in newaxes):
sys.exit("Can't average over an axis that has been reduced")
avgdata = np.mean(data, axis=newaxes)
axismanager.reduce_axes(avgaxes)
return avgdata
def managed_fft(self,axismanager, data, fftaxes):
newaxes = axismanager.current_axes_numbers(fftaxes)
if (None in newaxes):
sys.exit("Can't fft over an axis that has been reduced")
fftdata = np.fft.fftn(data,axes=newaxes)
return fftdata
def managed_energyfield(self,axismanager, data, fftaxes):
def add_negatives(a):
b = a
for i in range(1,len(b)/2):
b[i] += a[-i]
return b
newfftaxes = axismanager.current_axes_numbers(fftaxes)
# Count N
N = 1
for axis in newfftaxes:
N = N * data.shape[axis]
# Initial energy in every wavenumber (pos and neg)
E = np.abs(data)**2.0
#Add negative contributions to positive wavenumbers
nd = len(E.shape)
slicer = [slice(None)]*nd
for axis in newfftaxes:
E = np.apply_along_axis(add_negatives,axis,E)
# Discard negative parts after adding to positive
k = E.shape[axis]
mid = int(np.ceil(float(k)/2.0))
cutout = np.s_[0:mid+1:1]
slicer[axis] = cutout
E = E[slicer]
# Refresh slice for new axis calculation
slicer[axis] = slice(None)
return E/N
def managed_window(self,axismanager, data, windowaxis):
def window_axis_function(a, window):
a = a * window
return a
newaxis = axismanager.current_axis_number(windowaxis)
N = data.shape[newaxis]
window = np.hanning(N)
# Save "window summed and squared" (see Numerical Recipes)
wss = np.sum(window**2.0)/float(N)
# Apply window
windoweddata = np.apply_along_axis(window_axis_function,
newaxis, data, window)
return windoweddata, wss
def trim_binlimits(self, binlimits, bins):
"""
Trims a given field of bins based on binlimits
"""
# Defaults
lower = [0]*3
upper = [i for i in bins.shape]
for axis in range(3):
if (binlimits[axis] == None):
continue
else:
lower[axis] = binlimits[axis][0]
upper[axis] = binlimits[axis][1]
return bins[lower[0]:upper[0],
lower[1]:upper[1],
lower[2]:upper[2], :, :]
def power_spectrum(self,data=None,startrec=None,endrec=None,
preavgaxes=(), fftaxes=(),postavgaxes=(),
windowaxis=None, verify_Parseval=True,
savefile=None,**kwargs):
"""
Calculates power spectrum
"""
# ----------------------------------------------------------------
# Checks
if (not isinstance(preavgaxes,tuple)):
try:
preavgaxes = tuple([preavgaxes])
except:
print('Failed to make preavgaxes and fftaxes a tuple')
if (not isinstance(fftaxes,tuple)):
try:
fftaxes = tuple([fftaxes])
except:
print('Failed to make preavgaxes and fftaxes a tuple')
if (not isinstance(postavgaxes,tuple)):
try:
postavgaxes = tuple([postavgaxes])
except:
print('Failed to make preavgaxes and fftaxes a tuple')
if (4 in postavgaxes or 4 in fftaxes or 4 in preavgaxes):
message = "WARNING: you're asking me to average or fft over "
message += "each component of the field. I don't know how to "
message += "deal with this right now. Aborting."
sys.exit(message)
if (windowaxis):
if (windowaxis in preavgaxes or windowaxis in postavgaxes):
message = "Warning: you're asking me to window over an axis "
message += "that you're eventually going to average over. "
message += "Aborting."
sys.exit(message)
if (windowaxis not in fftaxes):
message = "Warning: you're asking me to window over an axis "
message += "that won't be Fourier transformed. This makes no "
message += "sense. Aborting."
# ----------------------------------------------------------------
# Do the process
if (startrec==None):
startrec = 0
if (endrec==None):
endrec = self.maxrec
axisman = self.AxisManager()
if data == None:
data = self.read(startrec, endrec,**kwargs)
data = self.managed_mean(axisman, data, preavgaxes)
if (windowaxis):
data, wss = self.managed_window(axisman, data, windowaxis)
if (verify_Parseval):
Esumreal = np.sum(np.abs(data)**2.0)
fftdata = self.managed_fft(axisman, data, fftaxes)
energy = self.managed_energyfield(axisman, fftdata, fftaxes)
if (verify_Parseval):
Esumfft = np.sum(energy)
ratio = abs(Esumreal - Esumfft)/Esumreal
perc = (1. - ratio)*100.
print(('Parseval thm (discounting window): ' + "%9.6f"%perc + '%'))
if (windowaxis):
energy = energy / wss
energy = self.managed_mean(axisman, energy, postavgaxes)
if (savefile):
with open(savefile,'w') as f:
f.write(energy)
return energy
def grad(self, data, dx=None,
dy=None,
dz=None, preavgaxes=()):
"""
Return the gradient of a vector field
"""
# ----------------------------------------------------------------
# Checks
if (not isinstance(preavgaxes,tuple)):
try:
preavgaxes = tuple([preavgaxes])
except:
print('Failed to make preavgaxes in grad')
#if (dxyz is None):
# dxyz=[self.Raw.dx,self.Raw.dy,self.Raw.dz]
data = np.mean(data,axis=preavgaxes,keepdims=True)
if (dx is None):
dx=self.Raw.dx
if (dy is None):
dy=self.Raw.dy
if (dz is None):
dz=self.Raw.dz
dxyz = (dx, dy, dz)
ndims = data.shape[4]
nonsingleton = [i!=1 for i in data.shape[0:3]]
dxyz = [elem for i,elem in enumerate(dxyz) if nonsingleton[i]]
gradv = np.zeros(list(data.shape[:-1]) + [3*ndims])
for rec in range(gradv.shape[-2]):
for ixyz in range(ndims):
grad_temp = np.gradient(np.squeeze(data[:,:,:,rec,ixyz]),
dx, dy, dz)
for jxyz in range(np.sum(nonsingleton)):
c = 3*ixyz + jxyz
gradv[:,:,:,rec,c] = grad_temp[jxyz]
return gradv
def write_dx_file(self, startrec, endrec, writedir=None,
component=0, norm=False, origin=None, **kwargs):
"""
Write MD field to dx file format which is primarily
useful for importing into VMD, see
http://www.ks.uiuc.edu/Research/vmd/plugins/molfile/dxplugin.html
and format website http://www.opendx.org/index2.php
NOTE -- VMD dx format assumes data points are located at the
cell vertices while Field class and it's children contain
cell centred data
"""
#Get field data
datamin = []; datamax = []
for rec in range(startrec,endrec):
data = self.read(startrec=rec,endrec=rec,**kwargs)
if norm:
if ((np.max(data[:,:,:,:,component])) > 1e-14):
data = (data-np.min(data[:,:,:,:,component])
/( np.max(data[:,:,:,:,component])
-np.min(data[:,:,:,:,component])))
#Return minimum and maximum values
datamin.append(np.min(data[:,:,:,:,component]))
datamax.append(np.max(data[:,:,:,:,component]))
Nx, Ny, Nz = data.shape[0], data.shape[1], data.shape[2]
dx,dy,dz = [(self.grid[i][1] - self.grid[i][0]) for i in range(3)]
Lx = float(Nx) * dx; Ly = float(Ny) * dy; Lz = float(Nz) * dz
originx = np.min(self.grid[0])
originy = np.min(self.grid[1])
originz = np.min(self.grid[2])
if origin != None:
assert len(origin) == 3
originx = origin[0] #-Ly/2
originy = origin[1] #-Ly/2.0
originz = origin[2] #-Lz/2.0
data = self.cellcentre2vertex(data[:,:,:,0,component])
Nx_v, Ny_v, Nz_v = data.shape[0], data.shape[1], data.shape[2]
#Get file name
if (writedir == None):
writedir = self.fdir + '/vmd/vol_data/'
dxFileName = writedir + 'DATA' + str(rec) + '.dx'
#Write data
with open(dxFileName,'w+') as f:
# - - Write Header - -
#dx_ = dx*1.25; dy_ = dy*1.25; dz_ = dz*1.25
f.write("object 1 class gridpositions counts%8.0f%8.0f%8.0f\n" % (Nx_v,Ny_v,Nz_v))
f.write("origin%16g%16g%16g\n" % (originx,originy,originz))
f.write("delta %16g 0 0\n" % dx)
f.write("delta 0 %16g 0\n" % dy)
f.write("delta 0 0 %16g\n" % dz)
f.write("object 2 class gridconnections counts%8.0f%8.0f%8.0f\n" % (Nx_v,Ny_v,Nz_v))
f.write("object 3 class array type double rank 0 items%8.0f follows\n" % (Nx_v*Ny_v*Nz_v))
# - - Write Data - -
col=1
for i in range(Nx_v):
for j in range(Ny_v):
for k in range(Nz_v):
f.write("%16E" % data[i,j,k])
col=col+1
if (col>3):
f.write(' \n')
col=1
# - - Write Footer - -
if (col != 1):
f.write(' \n')
f.write('object "'+str(self).split('.')[1].split(' ')[0]+'" class field \n')
return np.mean(datamin), np.mean(datamax)
# Write ascii type field
def write_asciifield(self,startrec,endrec,
writedir=None,maptocosine=True,
flipdir=[False,True,False],**kwargs):
#Get file name
if (writedir == None):
writedir = self.fdir
Nx, Ny, Nz = data.shape[0], data.shape[1], data.shape[2]
Nxyz = [Nx,Ny,Nz]
for n,flip in enumerate(flipdir):
if flip:
mindir[n] = Nxyz[n]
maxdir[n] = 0
else:
mindir[n] = 0
maxdir[n] = Nxyz[n]
data = self.read(startrec=startrec,endrec=endrec,**kwargs)
outfiles =[]
for rec in range(startrec,endrec):
FileName = writedir + 'u' + str(rec) + '.asc'
outfiles.append(FileName)
with open(FileName,'w+') as f:
for nx in range(mindir[0],maxdir[0]):
for ny in range(mindir[1],maxdir[1]):
for nz in range(mindir[2],maxdir[2]):
for ixyz in data.shape[4]:
if maptocosine:
f.write(str(
self.map_data_lineartocosine(
data[nx,ny,nz,rec,ixyz],
self.Raw.Ny,
self.Raw.a,
self.Raw.b))
+"\n")
else:
f.write(str(
data[nx,ny,nz,rec,ixyz])
+"\n")
return outfiles
# Write ascii type field
def map_3Ddata_cosinetolinear(self,data,flipdir=[False,True,False],**kwargs):
Nx, Ny, Nz = data.shape[0], data.shape[1], data.shape[2]
Nxyz = [Nx,Ny,Nz]
mindir = [0,0,0]; maxdir = [Nx,Ny,Nz]
for n,flip in enumerate(flipdir):
if flip:
mindir[n] = Nxyz[n]
maxdir[n] = 0
else:
mindir[n] = 0
maxdir[n] = Nxyz[n]
lindata = np.empty(data.shape)
for nx in range(mindir[0],maxdir[0]):
for nz in range(mindir[2],maxdir[2]):
for rec in range(data.shape[3]):
for ixyz in range(data.shape[4]):
print((nx,nz,rec,ixyz))
lindata[nx,:,nz,rec,ixyz] = self.map_data_cosinetolinear(
data[nx,:,nz,rec,ixyz],
self.Raw.Ny,
self.Raw.a,
self.Raw.b)
return lindata
def map_data_lineartocosine(self, values_on_linear_grid, Ny, a, b, plot=False):
"""
Map data on a linear grid to a cosine grid
"""
import scipy.interpolate as interp
ycells = np.linspace(0, Ny, Ny)
ylin = np.linspace(a, b, Ny)
ycos = 0.5*(b+a) - 0.5*(b-a)*np.cos((ycells*np.pi)/(Ny-1))
values_on_cosine_grid = interp.griddata(ylin, values_on_linear_grid,
ycos, method='cubic',
fill_value=values_on_linear_grid[-1])
if plot:
import matplotlib.pyplot as plt
plt.plot(ylin,values_on_linear_grid,'o-',alpha=0.4,label='lineartocosine Before')
plt.plot(ycos,values_on_cosine_grid,'x-',label='lineartocosine After')
plt.legend()
plt.show()
return values_on_cosine_grid
def map_data_cosinetolinear(self,values_on_cosine_grid,Ny,a,b):
"""
Map data on a cosine grid to a linear grid
"""
import scipy.interpolate as interp
ycells = np.linspace(0, Ny, Ny)
ylin = np.linspace(a, b, Ny)
ycos = 0.5*(b+a) - 0.5*(b-a)*np.cos((ycells*np.pi)/(Ny-1))
#print(ycos.shape,values_on_cosine_grid.shape)
#plt.plot(ycos,values_on_cosine_grid,'x-',label='cosinetolinear Before')
values_on_linear_grid = interp.griddata(ycos, values_on_cosine_grid,
ylin, method='cubic',
fill_value=values_on_cosine_grid[-1])
#from scipy.ndimage import map_coordinates
#values_on_linear_grid = interp2.map_coordinates(values_on_cosine_grid,ycos,output=ylin)
#plt.plot(ylin,values_on_linear_grid,'o-',alpha=0.4,label='cosinetolinear After')
#plt.legend()
#plt.show()
return values_on_linear_grid
def field_interpolator(self, celldata):
from scipy.interpolate import RegularGridInterpolator
#Need to turn off bounds errors and fill values to allow extrapolation
fn = RegularGridInterpolator((self.grid[0], self.grid[1], self.grid[2]),
celldata, bounds_error=False, fill_value=None)
return fn
def interp_3Darrays(self, fn, x, y, z):
"""
We need array data as a list of lists
"""
#We need array data as a list of lists
pts = []
for i, xp in enumerate(x):
for j, yp in enumerate(y):
for k, zp in enumerate(z):
pts.append([xp, yp, zp])
#Interpolate and reshape
data = fn(pts)
interpdata = data.reshape(x.size, y.size, z.size)
return interpdata
def cellcentre2vertex(self, celldata, method="zoom"):
"""
Routine to return grid data on an array one larger than the existing
cell centred data - currently uses zoom for simplicity
"""
if method is "zoom":
import scipy.ndimage
Nx, Ny, Nz = celldata.shape[0], celldata.shape[1], celldata.shape[2]
vertexdata = scipy.ndimage.zoom(celldata,((Nx+1)/float(Nx),
(Ny+1)/float(Ny),
(Nz+1)/float(Nz)))
elif method is "interp":
#Define values
xg = self.grid[0]
yg = self.grid[1]
zg = self.grid[2]
dx = xg[1]-xg[0]
dy = yg[1]-yg[0]
dz = zg[1]-zg[0]
#Get interpolator
fn = self.field_interpolator(celldata)
#Get grid of courner nodes
Nx, Ny, Nz = celldata.shape[0], celldata.shape[1], celldata.shape[2]
assert Nx == self.grid[0].size
assert Ny == self.grid[1].size
assert Nz == self.grid[2].size
x = np.linspace(self.grid[0][0]-0.5*dx, self.grid[0][-1]+0.5*dx, Nx+1)
y = np.linspace(self.grid[1][0]-0.5*dy, self.grid[1][-1]+0.5*dy, Ny+1)
z = np.linspace(self.grid[2][0]-0.5*dz, self.grid[2][-1]+0.5*dz, Nz+1)
vertexdata = self.interp_3Darrays(fn, x, y, z)
else:
raise exception("Unknown method")
return vertexdata
def cellcentre2surface(self, celldata, method="interp"):
if method is "interp":
surfacedata = np.zeros((celldata.shape[0],
celldata.shape[1],
celldata.shape[2],6))
#Get grid of centre abd surface values
Nx, Ny, Nz = celldata.shape[0], celldata.shape[1], celldata.shape[2]
assert Nx == self.grid[0].size
assert Ny == self.grid[1].size
assert Nz == self.grid[2].size
xg = self.grid[0]
yg = self.grid[1]
zg = self.grid[2]
dx = xg[1]-xg[0]
dy = yg[1]-yg[0]
dz = zg[1]-zg[0]
xs = np.linspace(self.grid[0][0]-0.5*dx, self.grid[0][-1]+0.5*dx, Nx+1)
ys = np.linspace(self.grid[1][0]-0.5*dy, self.grid[1][-1]+0.5*dy, Ny+1)
zs = np.linspace(self.grid[2][0]-0.5*dz, self.grid[2][-1]+0.5*dz, Nz+1)
#Get interpolator
fn = self.field_interpolator(celldata)
# x surfaces xs, y, z
data = self.interp_3Darrays(fn, xs, yg, zg)
surfacedata[:,:,:,0] = data[:-1,:,:] #Bottom
surfacedata[:,:,:,3] = data[1:,:,:] #Top
# y surfaces x, ys, z
data = self.interp_3Darrays(fn, xg, ys, zg)
surfacedata[:,:,:,1] = data[:,:-1,:] #Bottom
surfacedata[:,:,:,4] = data[:,1:,:] #Top
# z surfaces x, y, zs
data = self.interp_3Darrays(fn, xg, yg, zs)
surfacedata[:,:,:,2] = data[:,:,:-1] #Bottom
surfacedata[:,:,:,5] = data[:,:,1:] #Top
else:
raise exception("Unknown method")
return surfacedata
# Innards
# for i in range(1,Nx):
# for j in range(1,Ny):
# for k in range(1,Nz):
# vertexdata[i,j,k] = np.mean(celldata[i-1:i+2,j-1:j+2,k-1:k+2])
| gpl-3.0 |
mbonvini/GreenCardLottery | gree_card_lottery.py | 1 | 9231 | """
Author: Marco Bonvini
Date: September 27 2015
This script scrapes the pages of the Department of State website to gather statistics and information
about the Diversity Visa (DV) lottery, aka green card lottery.
The Department of State publishes every month a visa bulletin at
http://travel.state.gov/content/visas/en/law-and-policy/bulletin.html
this script generates URLs of the bulletins that have been released over the last
years and look for information. In particular, the script looks for information
regarding the DV lottery that are typically located in section B of the page,
B. DIVERSITY IMMIGRANT (DV) CATEGORY FOR THE MONTH OF ...
This section contains a table that states which DV lottery numbers will be able to
move forward with their application for the green card.
The scripts collectes all the data from the tables and generate Pandas DataFrames
that can be used to anamyze the data and compute some statistics.
LICENSE
++++++++
This code is released under MIT licenseThe MIT License (MIT)
Copyright (c) 2015 Marco Bonvini
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
"""
import urllib2
import re
import unicodedata
from datetime import datetime
from bs4 import BeautifulSoup, Tag
import pandas as pd
import numpy as np
import json
# Months and year ranges. Because the bulletins refer to fiscal year (FY), the first month is October.
years = range(2003,2016)
months = ['october','november','december','january','february','march','april','may','june','july','august','september']
# Initialize empty pandas DataFrames that will contain the final data for every continent
df_europe = pd.DataFrame(np.NAN*np.zeros((len(months),len(years))), index = months, columns = years)
df_africa = pd.DataFrame(np.NAN*np.zeros((len(months),len(years))), index = months, columns = years)
df_asia = pd.DataFrame(np.NAN*np.zeros((len(months),len(years))), index = months, columns = years)
df_oceania = pd.DataFrame(np.NAN*np.zeros((len(months),len(years))), index = months, columns = years)
df_north_america = pd.DataFrame(np.NAN*np.zeros((len(months),len(years))), index = months, columns = years)
df_south_america = pd.DataFrame(np.NAN*np.zeros((len(months),len(years))), index = months, columns = years)
# When the table contains the string "Current", "current", "c" or "C" instead of the number,
# replace it with a np.NaN in the DataFrames
current_number = np.NaN
# The pattern of the URL where one can fond the information about the DV lottery
pattern = "http://travel.state.gov/content/visas/english/law-and-policy/bulletin/{0}/visa-bulletin{3}-{1}-{2}.html"
# Iterate over all the years and months, generate the URLs and look for the
# data regarding the DV lottery. For each year of each month multiple URLS are built.
# This is necessary because the URLs haven't been managed in a consistent way over the years,
# for example sometimes the month starts with a lowercase letter but in other cases it does not.
for fy in years:
for m in months:
# Initialize list of URLs to check
page_urls = []
# The months of Oct, Nov and Dec needs to be treated differently
# because, for example, Oct 2014 belongs to FY 2015.
if m in ['october','november','december']:
page_urls.append(pattern.format(fy,m,fy-1,"-for"))
page_urls.append(pattern.format(fy,m,fy-1,""))
page_urls.append(pattern.format(fy,m.title(),fy-1,""))
page_urls.append(pattern.format(fy,m.title(),fy-1,"-for"))
else:
page_urls.append(pattern.format(fy,m,fy,"-for"))
page_urls.append(pattern.format(fy,m,fy,""))
page_urls.append(pattern.format(fy,m.title(),fy,""))
page_urls.append(pattern.format(fy,m.title(),fy,"-for"))
# Test the URLs, verify if they're legitimate, and get the
# content of the HTML page
page_ok = False
i = 0
while not page_ok and i<len(page_urls):
print "Reading page: {0}".format(page_urls[i])
try:
response = urllib2.urlopen(page_urls[i])
html_text = response.read()
if "404 - Page Not Found" not in html_text:
page_ok = True
except urllib2.HTTPError, e:
print "Failed reading the URL: {0}".format(page_urls[i])
print str(e)
i += 1
# If there was a successful read, parse the HTML page and populate the
# columns/rows of the pandas DataFrames
if page_ok:
soup = BeautifulSoup(html_text)
soup.encode('utf-8')
for tag in soup.find_all('script'):
tag.replaceWith('')
text = unicode(soup.get_text('|', strip=True))
text = text.encode('ascii',errors='ignore')
text = re.sub(',','',text)
text = re.sub('\\r','',text)
text = re.sub('\\n','',text)
text = re.sub(':','',text)
text = re.sub('-','',text)
text = re.sub('\s+','',text)
text = text.lower()
g_europe = re.search('(europe[|]*[\s*eu[r]*\s*]*[|]*(\d+|current|c))', text)
if g_europe:
df_europe[fy][m] = g_europe.groups()[1] if g_europe.groups()[1] not in ['c','current'] else current_number
else:
print "Missing Europe..."
g_africa = re.search('(africa[|]*[\s]*[af]*[\s]*[|]*(\d+|current|c))', text)
if g_africa:
df_africa[fy][m] = g_africa.groups()[1] if g_africa.groups()[1] not in ['c','current'] else current_number
else:
print "Missing Africa..."
g_oceania = re.search('(oceania[|]*[\s]*[oc]*[\s]*[|]*(\d+|current|c))', text)
if g_oceania:
df_oceania[fy][m] = g_oceania.groups()[1] if g_oceania.groups()[1] not in ['c','current'] else current_number
else:
print "Missing Oceania..."
g_asia = re.search('(asia[|]*[\s]*[as]*[\s]*[|]*(\d+|current|c))', text)
if g_asia:
df_asia[fy][m] = g_asia.groups()[1] if g_asia.groups()[1] not in ['c','current'] else current_number
else:
print "Missing Asia..."
g_north_america = re.search('(northamerica[|]*\(bahamas\)[|]*[\s]*[na]*[\s]*[|]*(\d+|current|c))', text)
if g_north_america:
df_north_america[fy][m] = g_north_america.groups()[1] if g_north_america.groups()[1] not in ['c','current'] else current_number
else:
print "Missing North America..."
g_south_america = re.search('(southamerica[|]*and[|]*the[|]*caribbean[|]*[\s]*[sa]*[\s]*[|]*(\d+|current|c))', text)
if g_south_america:
df_south_america[fy][m] = g_south_america.groups()[1] if g_south_america.groups()[1] not in ['c','current'] else current_number
else:
print "Missing South America..."
def convert_month_to_date(d):
"""
This function converts a string representing a month like ``october``
and converts it into a format like ``2015-10-01``.
"""
if d in ["october", "november", "december"]:
return datetime.strptime(d+"-2014", "%B-%Y").strftime("%Y-%m-%d")
else:
return datetime.strptime(d+"-2015", "%B-%Y").strftime("%Y-%m-%d")
# Export all the data in the data frames to JSON
data_frames = [df_europe, df_africa, df_asia, df_oceania, df_north_america, df_south_america]
data_names = ["europe", "africa", "asia", "oceania", "north_america", "south_america"]
mean_df = pd.DataFrame()
std_df = pd.DataFrame()
min_df = pd.DataFrame()
max_df = pd.DataFrame()
for name, df in zip(data_names, data_frames):
# Generate statistics for every DataFrame
mean_df[name] = df.mean(axis = 1)
std_df[name] = df.std(axis = 1)
min_df[name] = df.min(axis = 1)
max_df[name] = df.max(axis = 1)
data = []
for mo in months:
data_point = {"date": convert_month_to_date(mo)}
for c in data_names:
data_point["avg_"+c] = mean_df[c][mo]
data_point["u_"+c] = mean_df[c][mo] + std_df[c][mo]
data_point["l_"+c] = mean_df[c][mo] - std_df[c][mo]
data_point["min_"+c] = min_df[c][mo]
data_point["max_"+c] = max_df[c][mo]
data.append(data_point)
# Save the data in a JSON file
with open("dv_lottery.json", "w") as fp:
json.dump(data, fp, indent = 2)
| mit |
rohanp/scikit-learn | sklearn/tests/test_learning_curve.py | 59 | 10869 | # Author: Alexander Fabisch <[email protected]>
#
# License: BSD 3 clause
import sys
from sklearn.externals.six.moves import cStringIO as StringIO
import numpy as np
import warnings
from sklearn.base import BaseEstimator
from sklearn.utils.testing import assert_raises
from sklearn.utils.testing import assert_warns
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.datasets import make_classification
with warnings.catch_warnings():
warnings.simplefilter('ignore')
from sklearn.learning_curve import learning_curve, validation_curve
from sklearn.cross_validation import KFold
from sklearn.linear_model import PassiveAggressiveClassifier
class MockImprovingEstimator(BaseEstimator):
"""Dummy classifier to test the learning curve"""
def __init__(self, n_max_train_sizes):
self.n_max_train_sizes = n_max_train_sizes
self.train_sizes = 0
self.X_subset = None
def fit(self, X_subset, y_subset=None):
self.X_subset = X_subset
self.train_sizes = X_subset.shape[0]
return self
def predict(self, X):
raise NotImplementedError
def score(self, X=None, Y=None):
# training score becomes worse (2 -> 1), test error better (0 -> 1)
if self._is_training_data(X):
return 2. - float(self.train_sizes) / self.n_max_train_sizes
else:
return float(self.train_sizes) / self.n_max_train_sizes
def _is_training_data(self, X):
return X is self.X_subset
class MockIncrementalImprovingEstimator(MockImprovingEstimator):
"""Dummy classifier that provides partial_fit"""
def __init__(self, n_max_train_sizes):
super(MockIncrementalImprovingEstimator,
self).__init__(n_max_train_sizes)
self.x = None
def _is_training_data(self, X):
return self.x in X
def partial_fit(self, X, y=None, **params):
self.train_sizes += X.shape[0]
self.x = X[0]
class MockEstimatorWithParameter(BaseEstimator):
"""Dummy classifier to test the validation curve"""
def __init__(self, param=0.5):
self.X_subset = None
self.param = param
def fit(self, X_subset, y_subset):
self.X_subset = X_subset
self.train_sizes = X_subset.shape[0]
return self
def predict(self, X):
raise NotImplementedError
def score(self, X=None, y=None):
return self.param if self._is_training_data(X) else 1 - self.param
def _is_training_data(self, X):
return X is self.X_subset
def test_learning_curve():
X, y = make_classification(n_samples=30, n_features=1, n_informative=1,
n_redundant=0, n_classes=2,
n_clusters_per_class=1, random_state=0)
estimator = MockImprovingEstimator(20)
with warnings.catch_warnings(record=True) as w:
train_sizes, train_scores, test_scores = learning_curve(
estimator, X, y, cv=3, train_sizes=np.linspace(0.1, 1.0, 10))
if len(w) > 0:
raise RuntimeError("Unexpected warning: %r" % w[0].message)
assert_equal(train_scores.shape, (10, 3))
assert_equal(test_scores.shape, (10, 3))
assert_array_equal(train_sizes, np.linspace(2, 20, 10))
assert_array_almost_equal(train_scores.mean(axis=1),
np.linspace(1.9, 1.0, 10))
assert_array_almost_equal(test_scores.mean(axis=1),
np.linspace(0.1, 1.0, 10))
def test_learning_curve_unsupervised():
X, _ = make_classification(n_samples=30, n_features=1, n_informative=1,
n_redundant=0, n_classes=2,
n_clusters_per_class=1, random_state=0)
estimator = MockImprovingEstimator(20)
train_sizes, train_scores, test_scores = learning_curve(
estimator, X, y=None, cv=3, train_sizes=np.linspace(0.1, 1.0, 10))
assert_array_equal(train_sizes, np.linspace(2, 20, 10))
assert_array_almost_equal(train_scores.mean(axis=1),
np.linspace(1.9, 1.0, 10))
assert_array_almost_equal(test_scores.mean(axis=1),
np.linspace(0.1, 1.0, 10))
def test_learning_curve_verbose():
X, y = make_classification(n_samples=30, n_features=1, n_informative=1,
n_redundant=0, n_classes=2,
n_clusters_per_class=1, random_state=0)
estimator = MockImprovingEstimator(20)
old_stdout = sys.stdout
sys.stdout = StringIO()
try:
train_sizes, train_scores, test_scores = \
learning_curve(estimator, X, y, cv=3, verbose=1)
finally:
out = sys.stdout.getvalue()
sys.stdout.close()
sys.stdout = old_stdout
assert("[learning_curve]" in out)
def test_learning_curve_incremental_learning_not_possible():
X, y = make_classification(n_samples=2, n_features=1, n_informative=1,
n_redundant=0, n_classes=2,
n_clusters_per_class=1, random_state=0)
# The mockup does not have partial_fit()
estimator = MockImprovingEstimator(1)
assert_raises(ValueError, learning_curve, estimator, X, y,
exploit_incremental_learning=True)
def test_learning_curve_incremental_learning():
X, y = make_classification(n_samples=30, n_features=1, n_informative=1,
n_redundant=0, n_classes=2,
n_clusters_per_class=1, random_state=0)
estimator = MockIncrementalImprovingEstimator(20)
train_sizes, train_scores, test_scores = learning_curve(
estimator, X, y, cv=3, exploit_incremental_learning=True,
train_sizes=np.linspace(0.1, 1.0, 10))
assert_array_equal(train_sizes, np.linspace(2, 20, 10))
assert_array_almost_equal(train_scores.mean(axis=1),
np.linspace(1.9, 1.0, 10))
assert_array_almost_equal(test_scores.mean(axis=1),
np.linspace(0.1, 1.0, 10))
def test_learning_curve_incremental_learning_unsupervised():
X, _ = make_classification(n_samples=30, n_features=1, n_informative=1,
n_redundant=0, n_classes=2,
n_clusters_per_class=1, random_state=0)
estimator = MockIncrementalImprovingEstimator(20)
train_sizes, train_scores, test_scores = learning_curve(
estimator, X, y=None, cv=3, exploit_incremental_learning=True,
train_sizes=np.linspace(0.1, 1.0, 10))
assert_array_equal(train_sizes, np.linspace(2, 20, 10))
assert_array_almost_equal(train_scores.mean(axis=1),
np.linspace(1.9, 1.0, 10))
assert_array_almost_equal(test_scores.mean(axis=1),
np.linspace(0.1, 1.0, 10))
def test_learning_curve_batch_and_incremental_learning_are_equal():
X, y = make_classification(n_samples=30, n_features=1, n_informative=1,
n_redundant=0, n_classes=2,
n_clusters_per_class=1, random_state=0)
train_sizes = np.linspace(0.2, 1.0, 5)
estimator = PassiveAggressiveClassifier(n_iter=1, shuffle=False)
train_sizes_inc, train_scores_inc, test_scores_inc = \
learning_curve(
estimator, X, y, train_sizes=train_sizes,
cv=3, exploit_incremental_learning=True)
train_sizes_batch, train_scores_batch, test_scores_batch = \
learning_curve(
estimator, X, y, cv=3, train_sizes=train_sizes,
exploit_incremental_learning=False)
assert_array_equal(train_sizes_inc, train_sizes_batch)
assert_array_almost_equal(train_scores_inc.mean(axis=1),
train_scores_batch.mean(axis=1))
assert_array_almost_equal(test_scores_inc.mean(axis=1),
test_scores_batch.mean(axis=1))
def test_learning_curve_n_sample_range_out_of_bounds():
X, y = make_classification(n_samples=30, n_features=1, n_informative=1,
n_redundant=0, n_classes=2,
n_clusters_per_class=1, random_state=0)
estimator = MockImprovingEstimator(20)
assert_raises(ValueError, learning_curve, estimator, X, y, cv=3,
train_sizes=[0, 1])
assert_raises(ValueError, learning_curve, estimator, X, y, cv=3,
train_sizes=[0.0, 1.0])
assert_raises(ValueError, learning_curve, estimator, X, y, cv=3,
train_sizes=[0.1, 1.1])
assert_raises(ValueError, learning_curve, estimator, X, y, cv=3,
train_sizes=[0, 20])
assert_raises(ValueError, learning_curve, estimator, X, y, cv=3,
train_sizes=[1, 21])
def test_learning_curve_remove_duplicate_sample_sizes():
X, y = make_classification(n_samples=3, n_features=1, n_informative=1,
n_redundant=0, n_classes=2,
n_clusters_per_class=1, random_state=0)
estimator = MockImprovingEstimator(2)
train_sizes, _, _ = assert_warns(
RuntimeWarning, learning_curve, estimator, X, y, cv=3,
train_sizes=np.linspace(0.33, 1.0, 3))
assert_array_equal(train_sizes, [1, 2])
def test_learning_curve_with_boolean_indices():
X, y = make_classification(n_samples=30, n_features=1, n_informative=1,
n_redundant=0, n_classes=2,
n_clusters_per_class=1, random_state=0)
estimator = MockImprovingEstimator(20)
cv = KFold(n=30, n_folds=3)
train_sizes, train_scores, test_scores = learning_curve(
estimator, X, y, cv=cv, train_sizes=np.linspace(0.1, 1.0, 10))
assert_array_equal(train_sizes, np.linspace(2, 20, 10))
assert_array_almost_equal(train_scores.mean(axis=1),
np.linspace(1.9, 1.0, 10))
assert_array_almost_equal(test_scores.mean(axis=1),
np.linspace(0.1, 1.0, 10))
def test_validation_curve():
X, y = make_classification(n_samples=2, n_features=1, n_informative=1,
n_redundant=0, n_classes=2,
n_clusters_per_class=1, random_state=0)
param_range = np.linspace(0, 1, 10)
with warnings.catch_warnings(record=True) as w:
train_scores, test_scores = validation_curve(
MockEstimatorWithParameter(), X, y, param_name="param",
param_range=param_range, cv=2
)
if len(w) > 0:
raise RuntimeError("Unexpected warning: %r" % w[0].message)
assert_array_almost_equal(train_scores.mean(axis=1), param_range)
assert_array_almost_equal(test_scores.mean(axis=1), 1 - param_range)
| bsd-3-clause |
justincely/lightcurve_pipeline | setup.py | 1 | 1816 | from setuptools import setup
from setuptools import find_packages
# Command line scripts
scripts = ['reset_hstlc_filesystem = lightcurve_pipeline.scripts.reset_hstlc_filesystem:main',
'reset_hstlc_database = lightcurve_pipeline.scripts.reset_hstlc_database:main',
'download_hstlc = lightcurve_pipeline.scripts.download_hstlc:main',
'ingest_hstlc = lightcurve_pipeline.scripts.ingest_hstlc:main',
'build_stats_table = lightcurve_pipeline.scripts.build_stats_table:main',
'make_hstlc_plots = lightcurve_pipeline.scripts.make_hstlc_plots:main']
entry_points = {}
entry_points['console_scripts'] = scripts
setup(
name = 'lightcurve-pipeline',
description = 'Create lightcurves from HST/COS and HST/STIS data',
url = 'https://github.com/justincely/lightcurve_pipeline.git',
author = 'Matthew Bourque',
author_email = '[email protected]',
keywords = ['astronomy'],
classifiers = ['Programming Language :: Python',
'Development Status :: 1 - Planning',
'Intended Audience :: Science/Research',
'Topic :: Scientific/Engineering :: Astronomy',
'Topic :: Scientific/Engineering :: Physics',
'Topic :: Software Development :: Libraries :: Python Modules'],
packages = find_packages(),
install_requires = ['lightcurve>=0.6.0',
'numpy',
'scipy',
'astropy',
'sqlalchemy',
'pymysql',
'pyyaml',
'matplotlib',
'bokeh',
'pandas'],
scripts = ['scripts/hstlc_pipeline'],
entry_points = entry_points,
version = 1.0
)
| bsd-3-clause |
ningchi/scikit-learn | sklearn/neighbors/tests/test_kd_tree.py | 13 | 7914 | import numpy as np
from numpy.testing import assert_array_almost_equal
from sklearn.neighbors.kd_tree import (KDTree, NeighborsHeap,
simultaneous_sort, kernel_norm,
nodeheap_sort, DTYPE, ITYPE)
from sklearn.neighbors.dist_metrics import DistanceMetric
from sklearn.utils.testing import SkipTest, assert_allclose
V = np.random.random((3, 3))
V = np.dot(V, V.T)
DIMENSION = 3
METRICS = {'euclidean': {},
'manhattan': {},
'chebyshev': {},
'minkowski': dict(p=3)}
def brute_force_neighbors(X, Y, k, metric, **kwargs):
D = DistanceMetric.get_metric(metric, **kwargs).pairwise(Y, X)
ind = np.argsort(D, axis=1)[:, :k]
dist = D[np.arange(Y.shape[0])[:, None], ind]
return dist, ind
def test_kd_tree_query():
np.random.seed(0)
X = np.random.random((40, DIMENSION))
Y = np.random.random((10, DIMENSION))
def check_neighbors(dualtree, breadth_first, k, metric, kwargs):
kdt = KDTree(X, leaf_size=1, metric=metric, **kwargs)
dist1, ind1 = kdt.query(Y, k, dualtree=dualtree,
breadth_first=breadth_first)
dist2, ind2 = brute_force_neighbors(X, Y, k, metric, **kwargs)
# don't check indices here: if there are any duplicate distances,
# the indices may not match. Distances should not have this problem.
assert_array_almost_equal(dist1, dist2)
for (metric, kwargs) in METRICS.items():
for k in (1, 3, 5):
for dualtree in (True, False):
for breadth_first in (True, False):
yield (check_neighbors,
dualtree, breadth_first,
k, metric, kwargs)
def test_kd_tree_query_radius(n_samples=100, n_features=10):
np.random.seed(0)
X = 2 * np.random.random(size=(n_samples, n_features)) - 1
query_pt = np.zeros(n_features, dtype=float)
eps = 1E-15 # roundoff error can cause test to fail
kdt = KDTree(X, leaf_size=5)
rad = np.sqrt(((X - query_pt) ** 2).sum(1))
for r in np.linspace(rad[0], rad[-1], 100):
ind = kdt.query_radius(query_pt, r + eps)[0]
i = np.where(rad <= r + eps)[0]
ind.sort()
i.sort()
assert_array_almost_equal(i, ind)
def test_kd_tree_query_radius_distance(n_samples=100, n_features=10):
np.random.seed(0)
X = 2 * np.random.random(size=(n_samples, n_features)) - 1
query_pt = np.zeros(n_features, dtype=float)
eps = 1E-15 # roundoff error can cause test to fail
kdt = KDTree(X, leaf_size=5)
rad = np.sqrt(((X - query_pt) ** 2).sum(1))
for r in np.linspace(rad[0], rad[-1], 100):
ind, dist = kdt.query_radius(query_pt, r + eps, return_distance=True)
ind = ind[0]
dist = dist[0]
d = np.sqrt(((query_pt - X[ind]) ** 2).sum(1))
assert_array_almost_equal(d, dist)
def compute_kernel_slow(Y, X, kernel, h):
d = np.sqrt(((Y[:, None, :] - X) ** 2).sum(-1))
norm = kernel_norm(h, X.shape[1], kernel)
if kernel == 'gaussian':
return norm * np.exp(-0.5 * (d * d) / (h * h)).sum(-1)
elif kernel == 'tophat':
return norm * (d < h).sum(-1)
elif kernel == 'epanechnikov':
return norm * ((1.0 - (d * d) / (h * h)) * (d < h)).sum(-1)
elif kernel == 'exponential':
return norm * (np.exp(-d / h)).sum(-1)
elif kernel == 'linear':
return norm * ((1 - d / h) * (d < h)).sum(-1)
elif kernel == 'cosine':
return norm * (np.cos(0.5 * np.pi * d / h) * (d < h)).sum(-1)
else:
raise ValueError('kernel not recognized')
def test_kd_tree_kde(n_samples=100, n_features=3):
np.random.seed(0)
X = np.random.random((n_samples, n_features))
Y = np.random.random((n_samples, n_features))
kdt = KDTree(X, leaf_size=10)
for kernel in ['gaussian', 'tophat', 'epanechnikov',
'exponential', 'linear', 'cosine']:
for h in [0.01, 0.1, 1]:
dens_true = compute_kernel_slow(Y, X, kernel, h)
def check_results(kernel, h, atol, rtol, breadth_first):
dens = kdt.kernel_density(Y, h, atol=atol, rtol=rtol,
kernel=kernel,
breadth_first=breadth_first)
assert_allclose(dens, dens_true, atol=atol,
rtol=max(rtol, 1e-7))
for rtol in [0, 1E-5]:
for atol in [1E-6, 1E-2]:
for breadth_first in (True, False):
yield (check_results, kernel, h, atol, rtol,
breadth_first)
def test_gaussian_kde(n_samples=1000):
# Compare gaussian KDE results to scipy.stats.gaussian_kde
from scipy.stats import gaussian_kde
np.random.seed(0)
x_in = np.random.normal(0, 1, n_samples)
x_out = np.linspace(-5, 5, 30)
for h in [0.01, 0.1, 1]:
kdt = KDTree(x_in[:, None])
try:
gkde = gaussian_kde(x_in, bw_method=h / np.std(x_in))
except TypeError:
raise SkipTest("Old scipy, does not accept explicit bandwidth.")
dens_kdt = kdt.kernel_density(x_out[:, None], h) / n_samples
dens_gkde = gkde.evaluate(x_out)
assert_array_almost_equal(dens_kdt, dens_gkde, decimal=3)
def test_kd_tree_two_point(n_samples=100, n_features=3):
np.random.seed(0)
X = np.random.random((n_samples, n_features))
Y = np.random.random((n_samples, n_features))
r = np.linspace(0, 1, 10)
kdt = KDTree(X, leaf_size=10)
D = DistanceMetric.get_metric("euclidean").pairwise(Y, X)
counts_true = [(D <= ri).sum() for ri in r]
def check_two_point(r, dualtree):
counts = kdt.two_point_correlation(Y, r=r, dualtree=dualtree)
assert_array_almost_equal(counts, counts_true)
for dualtree in (True, False):
yield check_two_point, r, dualtree
def test_kd_tree_pickle():
import pickle
np.random.seed(0)
X = np.random.random((10, 3))
kdt1 = KDTree(X, leaf_size=1)
ind1, dist1 = kdt1.query(X)
def check_pickle_protocol(protocol):
s = pickle.dumps(kdt1, protocol=protocol)
kdt2 = pickle.loads(s)
ind2, dist2 = kdt2.query(X)
assert_array_almost_equal(ind1, ind2)
assert_array_almost_equal(dist1, dist2)
for protocol in (0, 1, 2):
yield check_pickle_protocol, protocol
def test_neighbors_heap(n_pts=5, n_nbrs=10):
heap = NeighborsHeap(n_pts, n_nbrs)
for row in range(n_pts):
d_in = np.random.random(2 * n_nbrs).astype(DTYPE)
i_in = np.arange(2 * n_nbrs, dtype=ITYPE)
for d, i in zip(d_in, i_in):
heap.push(row, d, i)
ind = np.argsort(d_in)
d_in = d_in[ind]
i_in = i_in[ind]
d_heap, i_heap = heap.get_arrays(sort=True)
assert_array_almost_equal(d_in[:n_nbrs], d_heap[row])
assert_array_almost_equal(i_in[:n_nbrs], i_heap[row])
def test_node_heap(n_nodes=50):
vals = np.random.random(n_nodes).astype(DTYPE)
i1 = np.argsort(vals)
vals2, i2 = nodeheap_sort(vals)
assert_array_almost_equal(i1, i2)
assert_array_almost_equal(vals[i1], vals2)
def test_simultaneous_sort(n_rows=10, n_pts=201):
dist = np.random.random((n_rows, n_pts)).astype(DTYPE)
ind = (np.arange(n_pts) + np.zeros((n_rows, 1))).astype(ITYPE)
dist2 = dist.copy()
ind2 = ind.copy()
# simultaneous sort rows using function
simultaneous_sort(dist, ind)
# simultaneous sort rows using numpy
i = np.argsort(dist2, axis=1)
row_ind = np.arange(n_rows)[:, None]
dist2 = dist2[row_ind, i]
ind2 = ind2[row_ind, i]
assert_array_almost_equal(dist, dist2)
assert_array_almost_equal(ind, ind2)
if __name__ == '__main__':
import nose
nose.runmodule()
| bsd-3-clause |
LevinJ/ud730-Deep-Learning | A1_notmnistdataset/p5_findduplication.py | 1 | 4322 |
import pickle
import math
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
from A1_notmnistdataset import p2_checkimage
class DataExploration:
def __init__(self):
with open('../A1_notmnistdataset/notMNIST_3.pickle', 'rb') as f:
self.dataset = pickle.load(f)
self.train_dataset = self.dataset['train_dataset']
self.train_labels = self.dataset['train_labels']
self.valid_dataset = self.dataset['valid_dataset']
self.valid_labels = self.dataset['valid_labels']
self.test_dataset = self.dataset['test_dataset']
self.test_labels = self.dataset['test_labels']
self.train_filepaths = self.dataset['train_filepaths']
self.test_filepaths = self.dataset['test_filepaths']
self.valid_filepaths = self.dataset['valid_filepaths']
# print('Training:', self.train_dataset.shape, self.train_labels.shape)
# print('Validation:', self.valid_dataset.shape, self.valid_labels.shape)
# print('Testing:', self.test_dataset.shape, self.test_labels.shape)
return
def checkDupwithin(self, images, groupname, image_paths=None):
temp_dict = {}
dup_dict={}
images.flags.writeable = False
count = 0
for idx, item in enumerate(images):
if item.data in temp_dict:
count = count + 1
# print("duplicate {}:{}".format(count, idx))
existingId = temp_dict[item.data]
if not (existingId in dup_dict):
dup_dict[existingId] = []
dup_dict[existingId].append(idx)
continue
temp_dict[item.data] = idx
print("{} has {} duplicate items, {} total items".format(groupname, count, images.shape[0]))
# print(dup_dict) images[dup_dict[25]]
# di = dup_dict[128]
# di = dup_dict[1018]
# self.dispImages(image_paths[di], images[di])
return
def checkDupBetween(self,datasetA, datasetB,lablesB, A,B, Apaths = None, Bpaths=None):
temp_dict = {}
dup_dict={}
datasetA.flags.writeable = False
datasetB.flags.writeable = False
count = 0
#build up base table for datasetA in temp_dict
for idx, item in enumerate(datasetA):
if item.data in temp_dict:
continue
temp_dict[item.data] = idx
for idx,img in enumerate(datasetB):
if img.data in temp_dict:
count = count + 1
existingId = temp_dict[img.data]
if not (existingId in dup_dict):
dup_dict[existingId] = []
dup_dict[existingId].append(idx)
print("{} {} duplicate {}, total count {}, total count {}".format(A, B, count, datasetA.shape[0], datasetB.shape[0]))
# print(Apaths[16812])
# print(Bpaths[dup_dict[16812]])
return
def getDiffLableCount(self, dup_table, labelsB):
count = 0
for key, value in dup_table.iteritems():
truelabels = [labelsB[item] for item in value]
allthesame = all(truelabels[0] == item for item in truelabels)
if not allthesame:
count = count + 1
print(truelabels)
return count
def dispImages(self, filepaths, images):
ci = p2_checkimage.CheckImage()
ci.dispImages_filepath(filepaths, 1)
ci.dispImages(images, 2)
return
def run(self):
self.checkDupwithin(self.train_dataset,'train_dataset', self.train_filepaths)
self.checkDupwithin(self.test_dataset,'test_dataset')
self.checkDupwithin(self.valid_dataset,'validation_dataset')
self.checkDupBetween(self.train_dataset, self.test_dataset,self.test_labels,'train_dataset','test_dataset', self.train_filepaths, self.test_filepaths)
self.checkDupBetween(self.train_dataset, self.valid_dataset,self.test_labels,'train_dataset','validation_dataset')
self.checkDupBetween(self.valid_dataset, self.test_dataset,self.test_labels,'validation_dataset','test_dataset')
plt.show()
return
if __name__ == "__main__":
obj= DataExploration()
obj.run()
| gpl-2.0 |
rajat1994/scikit-learn | sklearn/gaussian_process/gaussian_process.py | 83 | 34544 | # -*- coding: utf-8 -*-
# Author: Vincent Dubourg <[email protected]>
# (mostly translation, see implementation details)
# Licence: BSD 3 clause
from __future__ import print_function
import numpy as np
from scipy import linalg, optimize
from ..base import BaseEstimator, RegressorMixin
from ..metrics.pairwise import manhattan_distances
from ..utils import check_random_state, check_array, check_X_y
from ..utils.validation import check_is_fitted
from . import regression_models as regression
from . import correlation_models as correlation
MACHINE_EPSILON = np.finfo(np.double).eps
def l1_cross_distances(X):
"""
Computes the nonzero componentwise L1 cross-distances between the vectors
in X.
Parameters
----------
X: array_like
An array with shape (n_samples, n_features)
Returns
-------
D: array with shape (n_samples * (n_samples - 1) / 2, n_features)
The array of componentwise L1 cross-distances.
ij: arrays with shape (n_samples * (n_samples - 1) / 2, 2)
The indices i and j of the vectors in X associated to the cross-
distances in D: D[k] = np.abs(X[ij[k, 0]] - Y[ij[k, 1]]).
"""
X = check_array(X)
n_samples, n_features = X.shape
n_nonzero_cross_dist = n_samples * (n_samples - 1) // 2
ij = np.zeros((n_nonzero_cross_dist, 2), dtype=np.int)
D = np.zeros((n_nonzero_cross_dist, n_features))
ll_1 = 0
for k in range(n_samples - 1):
ll_0 = ll_1
ll_1 = ll_0 + n_samples - k - 1
ij[ll_0:ll_1, 0] = k
ij[ll_0:ll_1, 1] = np.arange(k + 1, n_samples)
D[ll_0:ll_1] = np.abs(X[k] - X[(k + 1):n_samples])
return D, ij
class GaussianProcess(BaseEstimator, RegressorMixin):
"""The Gaussian Process model class.
Read more in the :ref:`User Guide <gaussian_process>`.
Parameters
----------
regr : string or callable, optional
A regression function returning an array of outputs of the linear
regression functional basis. The number of observations n_samples
should be greater than the size p of this basis.
Default assumes a simple constant regression trend.
Available built-in regression models are::
'constant', 'linear', 'quadratic'
corr : string or callable, optional
A stationary autocorrelation function returning the autocorrelation
between two points x and x'.
Default assumes a squared-exponential autocorrelation model.
Built-in correlation models are::
'absolute_exponential', 'squared_exponential',
'generalized_exponential', 'cubic', 'linear'
beta0 : double array_like, optional
The regression weight vector to perform Ordinary Kriging (OK).
Default assumes Universal Kriging (UK) so that the vector beta of
regression weights is estimated using the maximum likelihood
principle.
storage_mode : string, optional
A string specifying whether the Cholesky decomposition of the
correlation matrix should be stored in the class (storage_mode =
'full') or not (storage_mode = 'light').
Default assumes storage_mode = 'full', so that the
Cholesky decomposition of the correlation matrix is stored.
This might be a useful parameter when one is not interested in the
MSE and only plan to estimate the BLUP, for which the correlation
matrix is not required.
verbose : boolean, optional
A boolean specifying the verbose level.
Default is verbose = False.
theta0 : double array_like, optional
An array with shape (n_features, ) or (1, ).
The parameters in the autocorrelation model.
If thetaL and thetaU are also specified, theta0 is considered as
the starting point for the maximum likelihood estimation of the
best set of parameters.
Default assumes isotropic autocorrelation model with theta0 = 1e-1.
thetaL : double array_like, optional
An array with shape matching theta0's.
Lower bound on the autocorrelation parameters for maximum
likelihood estimation.
Default is None, so that it skips maximum likelihood estimation and
it uses theta0.
thetaU : double array_like, optional
An array with shape matching theta0's.
Upper bound on the autocorrelation parameters for maximum
likelihood estimation.
Default is None, so that it skips maximum likelihood estimation and
it uses theta0.
normalize : boolean, optional
Input X and observations y are centered and reduced wrt
means and standard deviations estimated from the n_samples
observations provided.
Default is normalize = True so that data is normalized to ease
maximum likelihood estimation.
nugget : double or ndarray, optional
Introduce a nugget effect to allow smooth predictions from noisy
data. If nugget is an ndarray, it must be the same length as the
number of data points used for the fit.
The nugget is added to the diagonal of the assumed training covariance;
in this way it acts as a Tikhonov regularization in the problem. In
the special case of the squared exponential correlation function, the
nugget mathematically represents the variance of the input values.
Default assumes a nugget close to machine precision for the sake of
robustness (nugget = 10. * MACHINE_EPSILON).
optimizer : string, optional
A string specifying the optimization algorithm to be used.
Default uses 'fmin_cobyla' algorithm from scipy.optimize.
Available optimizers are::
'fmin_cobyla', 'Welch'
'Welch' optimizer is dued to Welch et al., see reference [WBSWM1992]_.
It consists in iterating over several one-dimensional optimizations
instead of running one single multi-dimensional optimization.
random_start : int, optional
The number of times the Maximum Likelihood Estimation should be
performed from a random starting point.
The first MLE always uses the specified starting point (theta0),
the next starting points are picked at random according to an
exponential distribution (log-uniform on [thetaL, thetaU]).
Default does not use random starting point (random_start = 1).
random_state: integer or numpy.RandomState, optional
The generator used to shuffle the sequence of coordinates of theta in
the Welch optimizer. If an integer is given, it fixes the seed.
Defaults to the global numpy random number generator.
Attributes
----------
theta_ : array
Specified theta OR the best set of autocorrelation parameters (the \
sought maximizer of the reduced likelihood function).
reduced_likelihood_function_value_ : array
The optimal reduced likelihood function value.
Examples
--------
>>> import numpy as np
>>> from sklearn.gaussian_process import GaussianProcess
>>> X = np.array([[1., 3., 5., 6., 7., 8.]]).T
>>> y = (X * np.sin(X)).ravel()
>>> gp = GaussianProcess(theta0=0.1, thetaL=.001, thetaU=1.)
>>> gp.fit(X, y) # doctest: +ELLIPSIS
GaussianProcess(beta0=None...
...
Notes
-----
The presentation implementation is based on a translation of the DACE
Matlab toolbox, see reference [NLNS2002]_.
References
----------
.. [NLNS2002] `H.B. Nielsen, S.N. Lophaven, H. B. Nielsen and J.
Sondergaard. DACE - A MATLAB Kriging Toolbox.` (2002)
http://www2.imm.dtu.dk/~hbn/dace/dace.pdf
.. [WBSWM1992] `W.J. Welch, R.J. Buck, J. Sacks, H.P. Wynn, T.J. Mitchell,
and M.D. Morris (1992). Screening, predicting, and computer
experiments. Technometrics, 34(1) 15--25.`
http://www.jstor.org/pss/1269548
"""
_regression_types = {
'constant': regression.constant,
'linear': regression.linear,
'quadratic': regression.quadratic}
_correlation_types = {
'absolute_exponential': correlation.absolute_exponential,
'squared_exponential': correlation.squared_exponential,
'generalized_exponential': correlation.generalized_exponential,
'cubic': correlation.cubic,
'linear': correlation.linear}
_optimizer_types = [
'fmin_cobyla',
'Welch']
def __init__(self, regr='constant', corr='squared_exponential', beta0=None,
storage_mode='full', verbose=False, theta0=1e-1,
thetaL=None, thetaU=None, optimizer='fmin_cobyla',
random_start=1, normalize=True,
nugget=10. * MACHINE_EPSILON, random_state=None):
self.regr = regr
self.corr = corr
self.beta0 = beta0
self.storage_mode = storage_mode
self.verbose = verbose
self.theta0 = theta0
self.thetaL = thetaL
self.thetaU = thetaU
self.normalize = normalize
self.nugget = nugget
self.optimizer = optimizer
self.random_start = random_start
self.random_state = random_state
def fit(self, X, y):
"""
The Gaussian Process model fitting method.
Parameters
----------
X : double array_like
An array with shape (n_samples, n_features) with the input at which
observations were made.
y : double array_like
An array with shape (n_samples, ) or shape (n_samples, n_targets)
with the observations of the output to be predicted.
Returns
-------
gp : self
A fitted Gaussian Process model object awaiting data to perform
predictions.
"""
# Run input checks
self._check_params()
self.random_state = check_random_state(self.random_state)
# Force data to 2D numpy.array
X, y = check_X_y(X, y, multi_output=True, y_numeric=True)
self.y_ndim_ = y.ndim
if y.ndim == 1:
y = y[:, np.newaxis]
# Check shapes of DOE & observations
n_samples, n_features = X.shape
_, n_targets = y.shape
# Run input checks
self._check_params(n_samples)
# Normalize data or don't
if self.normalize:
X_mean = np.mean(X, axis=0)
X_std = np.std(X, axis=0)
y_mean = np.mean(y, axis=0)
y_std = np.std(y, axis=0)
X_std[X_std == 0.] = 1.
y_std[y_std == 0.] = 1.
# center and scale X if necessary
X = (X - X_mean) / X_std
y = (y - y_mean) / y_std
else:
X_mean = np.zeros(1)
X_std = np.ones(1)
y_mean = np.zeros(1)
y_std = np.ones(1)
# Calculate matrix of distances D between samples
D, ij = l1_cross_distances(X)
if (np.min(np.sum(D, axis=1)) == 0.
and self.corr != correlation.pure_nugget):
raise Exception("Multiple input features cannot have the same"
" target value.")
# Regression matrix and parameters
F = self.regr(X)
n_samples_F = F.shape[0]
if F.ndim > 1:
p = F.shape[1]
else:
p = 1
if n_samples_F != n_samples:
raise Exception("Number of rows in F and X do not match. Most "
"likely something is going wrong with the "
"regression model.")
if p > n_samples_F:
raise Exception(("Ordinary least squares problem is undetermined "
"n_samples=%d must be greater than the "
"regression model size p=%d.") % (n_samples, p))
if self.beta0 is not None:
if self.beta0.shape[0] != p:
raise Exception("Shapes of beta0 and F do not match.")
# Set attributes
self.X = X
self.y = y
self.D = D
self.ij = ij
self.F = F
self.X_mean, self.X_std = X_mean, X_std
self.y_mean, self.y_std = y_mean, y_std
# Determine Gaussian Process model parameters
if self.thetaL is not None and self.thetaU is not None:
# Maximum Likelihood Estimation of the parameters
if self.verbose:
print("Performing Maximum Likelihood Estimation of the "
"autocorrelation parameters...")
self.theta_, self.reduced_likelihood_function_value_, par = \
self._arg_max_reduced_likelihood_function()
if np.isinf(self.reduced_likelihood_function_value_):
raise Exception("Bad parameter region. "
"Try increasing upper bound")
else:
# Given parameters
if self.verbose:
print("Given autocorrelation parameters. "
"Computing Gaussian Process model parameters...")
self.theta_ = self.theta0
self.reduced_likelihood_function_value_, par = \
self.reduced_likelihood_function()
if np.isinf(self.reduced_likelihood_function_value_):
raise Exception("Bad point. Try increasing theta0.")
self.beta = par['beta']
self.gamma = par['gamma']
self.sigma2 = par['sigma2']
self.C = par['C']
self.Ft = par['Ft']
self.G = par['G']
if self.storage_mode == 'light':
# Delete heavy data (it will be computed again if required)
# (it is required only when MSE is wanted in self.predict)
if self.verbose:
print("Light storage mode specified. "
"Flushing autocorrelation matrix...")
self.D = None
self.ij = None
self.F = None
self.C = None
self.Ft = None
self.G = None
return self
def predict(self, X, eval_MSE=False, batch_size=None):
"""
This function evaluates the Gaussian Process model at x.
Parameters
----------
X : array_like
An array with shape (n_eval, n_features) giving the point(s) at
which the prediction(s) should be made.
eval_MSE : boolean, optional
A boolean specifying whether the Mean Squared Error should be
evaluated or not.
Default assumes evalMSE = False and evaluates only the BLUP (mean
prediction).
batch_size : integer, optional
An integer giving the maximum number of points that can be
evaluated simultaneously (depending on the available memory).
Default is None so that all given points are evaluated at the same
time.
Returns
-------
y : array_like, shape (n_samples, ) or (n_samples, n_targets)
An array with shape (n_eval, ) if the Gaussian Process was trained
on an array of shape (n_samples, ) or an array with shape
(n_eval, n_targets) if the Gaussian Process was trained on an array
of shape (n_samples, n_targets) with the Best Linear Unbiased
Prediction at x.
MSE : array_like, optional (if eval_MSE == True)
An array with shape (n_eval, ) or (n_eval, n_targets) as with y,
with the Mean Squared Error at x.
"""
check_is_fitted(self, "X")
# Check input shapes
X = check_array(X)
n_eval, _ = X.shape
n_samples, n_features = self.X.shape
n_samples_y, n_targets = self.y.shape
# Run input checks
self._check_params(n_samples)
if X.shape[1] != n_features:
raise ValueError(("The number of features in X (X.shape[1] = %d) "
"should match the number of features used "
"for fit() "
"which is %d.") % (X.shape[1], n_features))
if batch_size is None:
# No memory management
# (evaluates all given points in a single batch run)
# Normalize input
X = (X - self.X_mean) / self.X_std
# Initialize output
y = np.zeros(n_eval)
if eval_MSE:
MSE = np.zeros(n_eval)
# Get pairwise componentwise L1-distances to the input training set
dx = manhattan_distances(X, Y=self.X, sum_over_features=False)
# Get regression function and correlation
f = self.regr(X)
r = self.corr(self.theta_, dx).reshape(n_eval, n_samples)
# Scaled predictor
y_ = np.dot(f, self.beta) + np.dot(r, self.gamma)
# Predictor
y = (self.y_mean + self.y_std * y_).reshape(n_eval, n_targets)
if self.y_ndim_ == 1:
y = y.ravel()
# Mean Squared Error
if eval_MSE:
C = self.C
if C is None:
# Light storage mode (need to recompute C, F, Ft and G)
if self.verbose:
print("This GaussianProcess used 'light' storage mode "
"at instantiation. Need to recompute "
"autocorrelation matrix...")
reduced_likelihood_function_value, par = \
self.reduced_likelihood_function()
self.C = par['C']
self.Ft = par['Ft']
self.G = par['G']
rt = linalg.solve_triangular(self.C, r.T, lower=True)
if self.beta0 is None:
# Universal Kriging
u = linalg.solve_triangular(self.G.T,
np.dot(self.Ft.T, rt) - f.T,
lower=True)
else:
# Ordinary Kriging
u = np.zeros((n_targets, n_eval))
MSE = np.dot(self.sigma2.reshape(n_targets, 1),
(1. - (rt ** 2.).sum(axis=0)
+ (u ** 2.).sum(axis=0))[np.newaxis, :])
MSE = np.sqrt((MSE ** 2.).sum(axis=0) / n_targets)
# Mean Squared Error might be slightly negative depending on
# machine precision: force to zero!
MSE[MSE < 0.] = 0.
if self.y_ndim_ == 1:
MSE = MSE.ravel()
return y, MSE
else:
return y
else:
# Memory management
if type(batch_size) is not int or batch_size <= 0:
raise Exception("batch_size must be a positive integer")
if eval_MSE:
y, MSE = np.zeros(n_eval), np.zeros(n_eval)
for k in range(max(1, n_eval / batch_size)):
batch_from = k * batch_size
batch_to = min([(k + 1) * batch_size + 1, n_eval + 1])
y[batch_from:batch_to], MSE[batch_from:batch_to] = \
self.predict(X[batch_from:batch_to],
eval_MSE=eval_MSE, batch_size=None)
return y, MSE
else:
y = np.zeros(n_eval)
for k in range(max(1, n_eval / batch_size)):
batch_from = k * batch_size
batch_to = min([(k + 1) * batch_size + 1, n_eval + 1])
y[batch_from:batch_to] = \
self.predict(X[batch_from:batch_to],
eval_MSE=eval_MSE, batch_size=None)
return y
def reduced_likelihood_function(self, theta=None):
"""
This function determines the BLUP parameters and evaluates the reduced
likelihood function for the given autocorrelation parameters theta.
Maximizing this function wrt the autocorrelation parameters theta is
equivalent to maximizing the likelihood of the assumed joint Gaussian
distribution of the observations y evaluated onto the design of
experiments X.
Parameters
----------
theta : array_like, optional
An array containing the autocorrelation parameters at which the
Gaussian Process model parameters should be determined.
Default uses the built-in autocorrelation parameters
(ie ``theta = self.theta_``).
Returns
-------
reduced_likelihood_function_value : double
The value of the reduced likelihood function associated to the
given autocorrelation parameters theta.
par : dict
A dictionary containing the requested Gaussian Process model
parameters:
sigma2
Gaussian Process variance.
beta
Generalized least-squares regression weights for
Universal Kriging or given beta0 for Ordinary
Kriging.
gamma
Gaussian Process weights.
C
Cholesky decomposition of the correlation matrix [R].
Ft
Solution of the linear equation system : [R] x Ft = F
G
QR decomposition of the matrix Ft.
"""
check_is_fitted(self, "X")
if theta is None:
# Use built-in autocorrelation parameters
theta = self.theta_
# Initialize output
reduced_likelihood_function_value = - np.inf
par = {}
# Retrieve data
n_samples = self.X.shape[0]
D = self.D
ij = self.ij
F = self.F
if D is None:
# Light storage mode (need to recompute D, ij and F)
D, ij = l1_cross_distances(self.X)
if (np.min(np.sum(D, axis=1)) == 0.
and self.corr != correlation.pure_nugget):
raise Exception("Multiple X are not allowed")
F = self.regr(self.X)
# Set up R
r = self.corr(theta, D)
R = np.eye(n_samples) * (1. + self.nugget)
R[ij[:, 0], ij[:, 1]] = r
R[ij[:, 1], ij[:, 0]] = r
# Cholesky decomposition of R
try:
C = linalg.cholesky(R, lower=True)
except linalg.LinAlgError:
return reduced_likelihood_function_value, par
# Get generalized least squares solution
Ft = linalg.solve_triangular(C, F, lower=True)
try:
Q, G = linalg.qr(Ft, econ=True)
except:
#/usr/lib/python2.6/dist-packages/scipy/linalg/decomp.py:1177:
# DeprecationWarning: qr econ argument will be removed after scipy
# 0.7. The economy transform will then be available through the
# mode='economic' argument.
Q, G = linalg.qr(Ft, mode='economic')
pass
sv = linalg.svd(G, compute_uv=False)
rcondG = sv[-1] / sv[0]
if rcondG < 1e-10:
# Check F
sv = linalg.svd(F, compute_uv=False)
condF = sv[0] / sv[-1]
if condF > 1e15:
raise Exception("F is too ill conditioned. Poor combination "
"of regression model and observations.")
else:
# Ft is too ill conditioned, get out (try different theta)
return reduced_likelihood_function_value, par
Yt = linalg.solve_triangular(C, self.y, lower=True)
if self.beta0 is None:
# Universal Kriging
beta = linalg.solve_triangular(G, np.dot(Q.T, Yt))
else:
# Ordinary Kriging
beta = np.array(self.beta0)
rho = Yt - np.dot(Ft, beta)
sigma2 = (rho ** 2.).sum(axis=0) / n_samples
# The determinant of R is equal to the squared product of the diagonal
# elements of its Cholesky decomposition C
detR = (np.diag(C) ** (2. / n_samples)).prod()
# Compute/Organize output
reduced_likelihood_function_value = - sigma2.sum() * detR
par['sigma2'] = sigma2 * self.y_std ** 2.
par['beta'] = beta
par['gamma'] = linalg.solve_triangular(C.T, rho)
par['C'] = C
par['Ft'] = Ft
par['G'] = G
return reduced_likelihood_function_value, par
def _arg_max_reduced_likelihood_function(self):
"""
This function estimates the autocorrelation parameters theta as the
maximizer of the reduced likelihood function.
(Minimization of the opposite reduced likelihood function is used for
convenience)
Parameters
----------
self : All parameters are stored in the Gaussian Process model object.
Returns
-------
optimal_theta : array_like
The best set of autocorrelation parameters (the sought maximizer of
the reduced likelihood function).
optimal_reduced_likelihood_function_value : double
The optimal reduced likelihood function value.
optimal_par : dict
The BLUP parameters associated to thetaOpt.
"""
# Initialize output
best_optimal_theta = []
best_optimal_rlf_value = []
best_optimal_par = []
if self.verbose:
print("The chosen optimizer is: " + str(self.optimizer))
if self.random_start > 1:
print(str(self.random_start) + " random starts are required.")
percent_completed = 0.
# Force optimizer to fmin_cobyla if the model is meant to be isotropic
if self.optimizer == 'Welch' and self.theta0.size == 1:
self.optimizer = 'fmin_cobyla'
if self.optimizer == 'fmin_cobyla':
def minus_reduced_likelihood_function(log10t):
return - self.reduced_likelihood_function(
theta=10. ** log10t)[0]
constraints = []
for i in range(self.theta0.size):
constraints.append(lambda log10t, i=i:
log10t[i] - np.log10(self.thetaL[0, i]))
constraints.append(lambda log10t, i=i:
np.log10(self.thetaU[0, i]) - log10t[i])
for k in range(self.random_start):
if k == 0:
# Use specified starting point as first guess
theta0 = self.theta0
else:
# Generate a random starting point log10-uniformly
# distributed between bounds
log10theta0 = (np.log10(self.thetaL)
+ self.random_state.rand(*self.theta0.shape)
* np.log10(self.thetaU / self.thetaL))
theta0 = 10. ** log10theta0
# Run Cobyla
try:
log10_optimal_theta = \
optimize.fmin_cobyla(minus_reduced_likelihood_function,
np.log10(theta0).ravel(), constraints,
iprint=0)
except ValueError as ve:
print("Optimization failed. Try increasing the ``nugget``")
raise ve
optimal_theta = 10. ** log10_optimal_theta
optimal_rlf_value, optimal_par = \
self.reduced_likelihood_function(theta=optimal_theta)
# Compare the new optimizer to the best previous one
if k > 0:
if optimal_rlf_value > best_optimal_rlf_value:
best_optimal_rlf_value = optimal_rlf_value
best_optimal_par = optimal_par
best_optimal_theta = optimal_theta
else:
best_optimal_rlf_value = optimal_rlf_value
best_optimal_par = optimal_par
best_optimal_theta = optimal_theta
if self.verbose and self.random_start > 1:
if (20 * k) / self.random_start > percent_completed:
percent_completed = (20 * k) / self.random_start
print("%s completed" % (5 * percent_completed))
optimal_rlf_value = best_optimal_rlf_value
optimal_par = best_optimal_par
optimal_theta = best_optimal_theta
elif self.optimizer == 'Welch':
# Backup of the given atrributes
theta0, thetaL, thetaU = self.theta0, self.thetaL, self.thetaU
corr = self.corr
verbose = self.verbose
# This will iterate over fmin_cobyla optimizer
self.optimizer = 'fmin_cobyla'
self.verbose = False
# Initialize under isotropy assumption
if verbose:
print("Initialize under isotropy assumption...")
self.theta0 = check_array(self.theta0.min())
self.thetaL = check_array(self.thetaL.min())
self.thetaU = check_array(self.thetaU.max())
theta_iso, optimal_rlf_value_iso, par_iso = \
self._arg_max_reduced_likelihood_function()
optimal_theta = theta_iso + np.zeros(theta0.shape)
# Iterate over all dimensions of theta allowing for anisotropy
if verbose:
print("Now improving allowing for anisotropy...")
for i in self.random_state.permutation(theta0.size):
if verbose:
print("Proceeding along dimension %d..." % (i + 1))
self.theta0 = check_array(theta_iso)
self.thetaL = check_array(thetaL[0, i])
self.thetaU = check_array(thetaU[0, i])
def corr_cut(t, d):
return corr(check_array(np.hstack([optimal_theta[0][0:i],
t[0],
optimal_theta[0][(i +
1)::]])),
d)
self.corr = corr_cut
optimal_theta[0, i], optimal_rlf_value, optimal_par = \
self._arg_max_reduced_likelihood_function()
# Restore the given atrributes
self.theta0, self.thetaL, self.thetaU = theta0, thetaL, thetaU
self.corr = corr
self.optimizer = 'Welch'
self.verbose = verbose
else:
raise NotImplementedError("This optimizer ('%s') is not "
"implemented yet. Please contribute!"
% self.optimizer)
return optimal_theta, optimal_rlf_value, optimal_par
def _check_params(self, n_samples=None):
# Check regression model
if not callable(self.regr):
if self.regr in self._regression_types:
self.regr = self._regression_types[self.regr]
else:
raise ValueError("regr should be one of %s or callable, "
"%s was given."
% (self._regression_types.keys(), self.regr))
# Check regression weights if given (Ordinary Kriging)
if self.beta0 is not None:
self.beta0 = check_array(self.beta0)
if self.beta0.shape[1] != 1:
# Force to column vector
self.beta0 = self.beta0.T
# Check correlation model
if not callable(self.corr):
if self.corr in self._correlation_types:
self.corr = self._correlation_types[self.corr]
else:
raise ValueError("corr should be one of %s or callable, "
"%s was given."
% (self._correlation_types.keys(), self.corr))
# Check storage mode
if self.storage_mode != 'full' and self.storage_mode != 'light':
raise ValueError("Storage mode should either be 'full' or "
"'light', %s was given." % self.storage_mode)
# Check correlation parameters
self.theta0 = check_array(self.theta0)
lth = self.theta0.size
if self.thetaL is not None and self.thetaU is not None:
self.thetaL = check_array(self.thetaL)
self.thetaU = check_array(self.thetaU)
if self.thetaL.size != lth or self.thetaU.size != lth:
raise ValueError("theta0, thetaL and thetaU must have the "
"same length.")
if np.any(self.thetaL <= 0) or np.any(self.thetaU < self.thetaL):
raise ValueError("The bounds must satisfy O < thetaL <= "
"thetaU.")
elif self.thetaL is None and self.thetaU is None:
if np.any(self.theta0 <= 0):
raise ValueError("theta0 must be strictly positive.")
elif self.thetaL is None or self.thetaU is None:
raise ValueError("thetaL and thetaU should either be both or "
"neither specified.")
# Force verbose type to bool
self.verbose = bool(self.verbose)
# Force normalize type to bool
self.normalize = bool(self.normalize)
# Check nugget value
self.nugget = np.asarray(self.nugget)
if np.any(self.nugget) < 0.:
raise ValueError("nugget must be positive or zero.")
if (n_samples is not None
and self.nugget.shape not in [(), (n_samples,)]):
raise ValueError("nugget must be either a scalar "
"or array of length n_samples.")
# Check optimizer
if self.optimizer not in self._optimizer_types:
raise ValueError("optimizer should be one of %s"
% self._optimizer_types)
# Force random_start type to int
self.random_start = int(self.random_start)
| bsd-3-clause |
conorkcorbin/tractometry | call_stats.py | 1 | 1798 | from stats import bundleModel
import nibabel as nib
import pandas as pd
import argparse
import numpy as np
from dipy.tracking.streamline import set_number_of_points
parser = argparse.ArgumentParser()
parser.add_argument('-templateBundle', '--templateBundle', required = True)
parser.add_argument('-bundleName', '--bundleName', required = True)
parser.add_argument('-metricName', '--metricName', required = True)
parser.add_argument('-subjectInfo', '--subjectInfo', required = True)
parser.add_argument('-metrics', '--metrics', required = True)
parser.add_argument('-alpha', '--alpha', type=float, required = True)
parser.add_argument('-out', '--out', required = True)
parser.add_argument('-outPvals', '--outPvals', required = True)
args = parser.parse_args()
tracks, hdr = nib.trackvis.read(args.templateBundle)
streamlines = [trk[0] for trk in tracks]
streamlines = set_number_of_points(streamlines,50)
subjectInfo = pd.read_csv(args.subjectInfo)
metrics = pd.read_csv(args.metrics)
### START EDITING HERE
# EDIT TO INCLUDE SUBJECT INFO YOU WANT TO REGRESS (JUST ADD THE COLUMN NAMES LIKE SHOWN BELOW)
subjectInfo = subjectInfo[['Group','Age','Sex','IntracranialVolume']]
# CONVERT ANY CATEGORICAL VARIABLES TO DUMMMY VARIABLES LIKE SO
subjectInfo['Group'] = [1 if g == 'PD' else 0 for g in subjectInfo['Group'].values]
subjectInfo['Sex'] = [1 if g == 'M' else 0 for g in subjectInfo['Sex'].values]
covariate2project = 'Group' # This is the variable whose p values you want on the tracks
### STOP EDITTING HERE
bm = bundleModel(subjectInfo,
metrics,
streamlines,
hdr,
args.bundleName,
args.metricName,
args.alpha)
bm.regress()
bm.correct()
tracks = bm.project2Tracks(covariate2project)
nib.trackvis.write(args.out,tracks,hdr)
np.savez(args.outPvals,bm.pvalues) | mit |
phoebe-project/phoebe2 | phoebe/dependencies/autofig/call.py | 1 | 114666 | import numpy as np
import astropy.units as u
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from matplotlib.collections import LineCollection, PolyCollection
from mpl_toolkits.mplot3d.art3d import Line3DCollection, Poly3DCollection
from . import common
from . import callbacks
def _map_none(value):
if isinstance(value, str):
if value.lower() == 'none':
return 'None'
else:
return value
else:
# NOTE: including None - we want this to fallback on the cycler
return value
def _to_linebreak_list(thing, N=1):
if isinstance(thing, list):
return thing
else:
return [thing]*N
class CallGroup(common.Group):
def __init__(self, items):
super(CallGroup, self).__init__(Call, [], items)
@property
def callbacks(self):
"""
Returns
---------
* (list) a list of <autofig.call.Call.callbacks> for each child
<autofig.call.Call>
"""
return self._get_attrs('callbacks')
def connect_callback(self, callback):
for call in self._items:
call.connect_callback(callback)
@property
def i(self):
"""
Returns
---------
* (list) a list of <autofig.call.Call.i> for each child
<autofig.call.Call>
"""
return CallDimensionGroup(self._get_attrs('i'))
@property
def x(self):
"""
Returns
---------
* (list) a list of <autofig.call.Call.x> for each child
<autofig.call.Call>
"""
return CallDimensionGroup(self._get_attrs('x'))
@property
def y(self):
"""
Returns
---------
* (list) a list of <autofig.call.Call.y> for each child
<autofig.call.Call>
"""
return CallDimensionGroup(self._get_attrs('y'))
@property
def z(self):
"""
Returns
---------
* (list) a list of <autofig.call.Call.z> for each child
<autofig.call.Call>
"""
return CallDimensionGroup(self._get_attrs('z'))
@property
def consider_for_limits(self):
"""
Returns
---------
* (list) a list of <autofig.call.Call.consider_for_limits> for each child
<autofig.call.Call>
"""
return self._get_attrs('consider_for_limits')
@consider_for_limits.setter
def consider_for_limits(self, consider_for_limits):
return self._set_attrs('consider_for_limits', consider_for_limits)
def draw(self, *args, **kwargs):
"""
Calls <autofig.call.Plot.draw> or <autofig.call.Mesh.draw> for each
<autofig.call.Call> in the <autofig.call.CallGroup>.
See also:
* <autofig.draw>
* <autofig.figure.Figure.draw>
* <autofig.axes.Axes.draw>
* <autofig.call.Plot.draw>
* <autofig.call.Mesh.draw>
Arguments
------------
* `*args`: all arguments are passed on to each <autofig.call.Call>.
* `**kwargs`: all keyword arguments are passed on to each
<autofig.call.Call>.
Returns
-----------
* (list): list of all created matplotlib artists
"""
# CallGroup.draw
return_artists = []
for call in self._items:
artists = call.draw(*args, **kwargs)
return_artists += artists
return return_artists
class PlotGroup(CallGroup):
@property
def s(self):
"""
Returns
---------
* (list) a list of <autofig.call.Plot.s> for each child
<autofig.call.Plot>
"""
return CallDimensionSGroup(self._get_attrs('s'))
@property
def c(self):
"""
Returns
---------
* (list) a list of <autofig.call.Plot.c> for each child
<autofig.call.Plot>
"""
return CallDimensionCGroup(self._get_attrs('c'))
@property
def size_scale(self):
"""
Returns
---------
* (list) a list of <autofig.call.Plot.size_scale> for each child
<autofig.call.Plot>
"""
return self._get_attrs('size_scale')
@size_scale.setter
def size_scale(self, size_scale):
return self._set_attrs('size_scale', size_scale)
class MeshGroup(CallGroup):
@property
def fc(self):
"""
Returns
---------
* (list) a list of <autofig.call.Mesh.fc> for each child
<autofig.call.Mesh>
"""
return CallDimensionCGroup(self._get_attrs('fc'))
@property
def ec(self):
"""
Returns
---------
* (list) a list of <autofig.call.Mesh.ec> for each child
<autofig.call.Mesh>
"""
return CallDimensionCGroup(self._get_attrs('ec'))
def make_callgroup(items):
if np.all([isinstance(item, Plot) for item in items]):
return PlotGroup(items)
elif np.all([isinstance(item, Mesh) for item in items]):
return MeshGroup(items)
else:
return CallGroup(items)
class Call(object):
def __init__(self, x=None, y=None, z=None, i=None,
xerror=None, xunit=None, xlabel=None, xnormals=None,
yerror=None, yunit=None, ylabel=None, ynormals=None,
zerror=None, zunit=None, zlabel=None, znormals=None,
iunit=None, itol=0.0,
axorder=None, axpos=None,
title=None,
label=None,
consider_for_limits=True,
uncover=False,
trail=False,
**kwargs):
"""
Create a <autofig.call.Call> object which defines a single call to
matplotlib.
Arguments
-------------
* `x` (list/array, optional, default=None): array of values for the x-axes.
Access via <autofig.call.Call.x>.
* `y` (list/array, optional, default=None): array of values for the y-axes.
Access via <autofig.call.Call.y>.
* `z` (list/array, optional, default=None): array of values for the z-axes.
Access via <autofig.call.Call.z>
* `i` (list/array or string, optional, default=None): array of values for
the independent-variable. If a string, can be one of: 'x', 'y', 'z'
to reference an existing array. Access via <autofig.call.Call.i>.
* `xerror` (float or list/array, optional, default=None): errors for `x`.
See <autofig.call.Call.x> and <autofig.call.CallDimensionX.error>.
* `xunit` (string or astropy unit, optional, default=None): units for `x`.
See <autofig.call.Call.x> and <autofig.call.CallDimensionX.unit>.
* `xlabel` (strong, optional, default=None): label for `x`.
See <autofig.call.Call.x> and <autofig.call.CallDimensionX.label>.
* `xnormals` (list/array, optional, default=None): normals for `x`.
Currently ignored.
* `yerror` (float or list/array, optional, default=None): errors for `y`.
See <autofig.call.Call.y> and <autofig.call.CallDimensionY.error>.
* `yunit` (string or astropy unit, optional, default=None): units for `y`.
See <autofig.call.Call.y> and <autofig.call.CallDimensionY.unit>.
* `ylabel` (strong, optional, default=None): label for `y`.
See <autofig.call.Call.y> and <autofig.call.CallDimensionY.label>.
* `ynormals` (list/array, optional, default=None): normals for `y`.
Currently ignored.
* `zerror` (float or list/array, optional, default=None): errors for `z`.
See <autofig.call.Call.z> and <autofig.call.CallDimensionZ.error>.
* `zunit` (string or astropy unit, optional, default=None): units for `z`.
See <autofig.call.Call.z> and <autofig.call.CallDimensionZ.unit>.
* `zlabel` (strong, optional, default=None): label for `x`.
See <autofig.call.Call.z> and <autofig.call.CallDimensionZ.label>.
* `znormals` (list/array, optional, default=None): normals for `z`.
Currently only used for <autofig.call.Mesh>.
* `iunit` (string or astropy unit, optional, default=None): units for `i`.
See <autofig.call.Call.i> and <autofig.call.CallDimensionI.unit>.
* `itol` (float, optional, default=0.0): see <autofig.call.DimensionI.tol>.
* `axorder` (int, optional, default=None): see <autofig.call.Call.axorder>.
* `axpos` (tuple, optional, default=None): see <autofig.call.Call.axpos>.
* `title` (string, optional, default=None): see <autofig.call.Call.title>.
* `label` (string, optional, default=None): see <autofig.call.Call.label>.
* `consider_for_limits` (bool, optional, default=True): see
<autofig.call.Call.consider_for_limits>.
* `uncover` (bool, optional, default=False): see <autofig.call.Call.uncover>.
* `trail` (bool or Float, optional, default=False): see
<autofig.call.Call.trail>.
* `**kwargs`: additional keyword arguments are stored and passed on when
attaching to a parent axes. See <autofig.axes.Axes.add_call>.
Returns
---------
* the instantiated <autofig.call.Call> object.
"""
self._class = 'Call' # just to avoid circular import in order to use isinstance
self._axes = None
self._backend_objects = []
self._callbacks = []
self._x = CallDimensionX(self, x, xerror, xunit, xlabel, xnormals)
self._y = CallDimensionY(self, y, yerror, yunit, ylabel, ynormals)
self._z = CallDimensionZ(self, z, zerror, zunit, zlabel, znormals)
# defined last so all other dimensions are in place in case indep
# is a reference and needs to access units, etc
self._i = CallDimensionI(self, i, iunit, itol)
self.consider_for_limits = consider_for_limits
self.uncover = uncover
self.trail = trail
self.axorder = axorder
self.axpos = axpos
self.title = title
self.label = label
self.kwargs = kwargs
# TODO: add style
def _get_backend_object():
return self._backend_artists
@property
def callbacks(self):
return self._callbacks
def connect_callback(self, callback):
if not isinstance(callback, str):
callback = callback.__name__
if callback not in self.callbacks:
self._callbacks.append(callback)
@property
def axes(self):
"""
Returns
--------
* (<autofig.axes.Axes> or None): the parent axes, if applicable.
"""
# no setter as this can only be set internally when attaching to an axes
return self._axes
@property
def figure(self):
"""
Returns
--------
* (<autofig.figure.Figure> or None): the parent figure, if applicable.
"""
# no setter as this can only be set internally when attaching to an axes
if self.axes is None:
return None
return self.axes.figure
@property
def i(self):
"""
Returns
----------
* <autofig.call.CallDimensionI>
"""
return self._i
@property
def indep(self):
"""
Shortcut to <autofig.call.Call.i>
Returns
----------
* <autofig.call.CallDimensionI>
"""
return self.i
@property
def x(self):
"""
Returns
----------
* <autofig.call.CallDimensionX>
"""
return self._x
@property
def y(self):
"""
Returns
----------
* <autofig.call.CallDimensionY>
"""
return self._y
@property
def z(self):
"""
Returns
----------
* <autofig.call.CallDimensionZ>
"""
return self._z
@property
def consider_for_limits(self):
"""
Returns
-----------
* (bool): whether the data in this <autofig.call.Call> should be considered
when determining axes limits.
"""
return self._consider_for_limits
@consider_for_limits.setter
def consider_for_limits(self, consider):
if not isinstance(consider, bool):
raise TypeError("consider_for_limits must be of type bool")
self._consider_for_limits = consider
@property
def uncover(self):
"""
Returns
---------
* (bool): whether uncover is enabled
"""
return self._uncover
@uncover.setter
def uncover(self, uncover):
if not isinstance(uncover, bool):
raise TypeError("uncover must be of type bool")
self._uncover = uncover
@property
def trail(self):
"""
Returns
---------
* (bool or Float): whether trail is enabled. If a float, then a value
between 0 and 1 indicating the length of the trail.
"""
return self._trail
@trail.setter
def trail(self, trail):
if not (isinstance(trail, bool) or isinstance(trail, float)):
if isinstance(trail, int):
trail = float(trail)
else:
raise TypeError("trail must be of type bool or float")
if trail < 0 or trail > 1:
raise ValueError("trail must be between 0 and 1")
self._trail = trail
@property
def axorder(self):
"""
See tutorial:
* [Subplot/Axes Positioning](../../tutorials/subplot_positioning.md)
Returns
--------
* (int or None)
"""
return self._axorder
@axorder.setter
def axorder(self, axorder):
if axorder is None:
self._axorder = None
return
if not isinstance(axorder, int):
raise TypeError("axorder must be of type int")
self._axorder = axorder
@property
def axpos(self):
"""
See tutorial:
* [Subplot/Axes Positioning](../../tutorials/subplot_positioning.md)
Returns
--------
* (tuple or None)
"""
return self._axpos
@axpos.setter
def axpos(self, axpos):
if axpos is None:
self._axpos = axpos
return
if isinstance(axpos, list) or isinstance(axpos, np.ndarray):
axpos = tuple(axpos)
if isinstance(axpos, tuple) and (len(axpos) == 3 or len(axpos) == 6) and np.all(isinstance(ap, int) for ap in axpos):
self._axpos = axpos
elif isinstance(axpos, int) and axpos >= 100 and axpos < 1000:
self._axpos = (int(axpos/100), int(axpos/10 % 10), int(axpos % 10))
elif isinstance(axpos, int) and axpos >= 110011 and axpos < 999999:
self._axpos = tuple([int(ap) for ap in str(axpos)])
else:
raise ValueError("axpos must be of type int or tuple between 100 and 999 (subplot syntax: ncols, nrows, ind) or 110011 and 999999 (gridspec syntax: ncols, nrows, indx, indy, widthx, widthy)")
@property
def title(self):
"""
Returns
-----------
* (str): title used for axes title
"""
return self._title
@title.setter
def title(self, title):
if title is None:
self._title = title
return
if not isinstance(title, str):
raise TypeError("title must be of type str")
self._title = title
@property
def label(self):
"""
Returns
-----------
* (str): label used for legends
"""
return self._label
@label.setter
def label(self, label):
if label is None:
self._label = label
return
if not isinstance(label, str):
raise TypeError("label must be of type str")
self._label = label
class Plot(Call):
def __init__(self, x=None, y=None, z=None, c=None, s=None, i=None,
xerror=None, xunit=None, xlabel=None,
yerror=None, yunit=None, ylabel=None,
zerror=None, zunit=None, zlabel=None,
cunit=None, clabel=None, cmap=None,
sunit=None, slabel=None, smap=None, smode=None,
iunit=None, itol=0.0,
axorder=None, axpos=None,
title=None,
label=None,
marker=None,
linestyle=None, linebreak=None,
highlight=True, uncover=False, trail=False,
consider_for_limits=True,
**kwargs):
"""
Create a <autofig.call.Plot> object which defines a single call to
matplotlib.
See also:
* <autofig.call.Mesh>
Note that the following keyword arguments are not allowed and will raise
an error suggesting the appropriate autofig argument:
* `markersize` or `ms`: use `size` or `s`
* `linewidth` or `lw`: use `size` or `s`
Arguments
-------------
* `x` (list/array, optional, default=None): array of values for the x-axes.
Access via <autofig.call.Plot.x>.
* `y` (list/array, optional, default=None): array of values for the y-axes.
Access via <autofig.call.Plot.y>.
* `z` (list/array, optional, default=None): array of values for the z-axes.
Access via <autofig.call.Plot.z>
* `c` or `color` (list/array, optional, default=None): array of values for the
color-direction. Access via <autofig.call.Plot.c>. Note: `color`
takes precedence over `c` if both are provided.
* `s` or `size` (list/array, optional, default=None): array of values for the
size-direction. Access via <autofig.call.Plot.s>. Note: `size` takes
precedence over `s` if both are provided.
* `i` (list/array or string, optional, default=None): array of values for
the independent-variable. If a string, can be one of: 'x', 'y', 'z',
'c', 's' to reference an existing array. Access via
<autofig.call.Plot.i>.
* `xerror` (float or list/array, optional, default=None): errors for `x`.
See <autofig.call.Plot.x> and <autofig.call.CallDimensionX.error>.
* `xunit` (string or astropy unit, optional, default=None): units for `x`.
See <autofig.call.Plot.x> and <autofig.call.CallDimensionX.unit>.
* `xlabel` (strong, optional, default=None): label for `x`.
See <autofig.call.Plot.x> and <autofig.call.CallDimensionX.label>.
* `yerror` (float or list/array, optional, default=None): errors for `y`.
See <autofig.call.Plot.y> and <autofig.call.CallDimensionY.error>.
* `yunit` (string or astropy unit, optional, default=None): units for `y`.
See <autofig.call.Plot.y> and <autofig.call.CallDimensionY.unit>.
* `ylabel` (strong, optional, default=None): label for `y`.
See <autofig.call.Plot.y> and <autofig.call.CallDimensionY.label>.
* `zerror` (float or list/array, optional, default=None): errors for `z`.
See <autofig.call.Plot.z> and <autofig.call.CallDimensionZ.error>.
* `zunit` (string or astropy unit, optional, default=None): units for `z`.
See <autofig.call.Plot.z> and <autofig.call.CallDimensionZ.unit>.
* `zlabel` (strong, optional, default=None): label for `x`.
See <autofig.call.Plot.z> and <autofig.call.CallDimensionZ.label>.
* `cerror` (float or list/array, optional, default=None): errors for `c`.
See <autofig.call.Plot.c> and <autofig.call.CallDimensionC.error>.
* `cunit` (string or astropy unit, optional, default=None): units for `c`.
See <autofig.call.Plot.c> and <autofig.call.CallDimensionC.unit>.
* `clabel` (strong, optional, default=None): label for `c`.
See <autofig.call.Plot.c> and <autofig.call.CallDimensionC.label>.
* `serror` (float or list/array, optional, default=None): errors for `s`.
See <autofig.call.Plot.s> and <autofig.call.CallDimensionS.error>.
* `sunit` (string or astropy unit, optional, default=None): units for `s`.
See <autofig.call.Plot.s> and <autofig.call.CallDimensionS.unit>.
* `slabel` (strong, optional, default=None): label for `s`.
See <autofig.call.Plot.s> and <autofig.call.CallDimensionS.label>.
* `iunit` (string or astropy unit, optional, default=None): units for `i`.
See <autofig.call.Plot.i> and <autofig.call.CallDimensionI.unit>.
* `itol` (float, optional, default=0.0): see <autofig.call.DimensionI.tol>.
* `axorder` (int, optional, default=None): see <autofig.call.Plot.axorder>.
* `axpos` (tuple, optional, default=None): see <autofig.call.Plot.axpos>.
* `title` (string, optional, default=None): see <autofig.call.Plot.title>.
* `label` (string, optional, default=None): see <autofig.call.Plot.label>.
* `marker` or `m` (string, optional, default=None): see <autofig.call.Plot.marker>.
Note: `marker` takes precedence over `m` if both are provided.
* `linestyle` or `ls` (string, optional, default=None): see
<autofig.call.Plot.linestyle>. Note: `linestyle` takes precedence
over `ls` if both are provided.
* `linebreak` (string, optional, default=None): see <autofig.call.Plot.linebreak>.
* `highlight` (bool, optional, default=False): see <autofig.call.Plot.highlight>.
* `highlight_marker` (string, optional, default=None)
* `highlight_linestyle` or `highlight_ls` (string, optional, default=None):
Note: `highlight_linestyle` takes precedence over `highlight_ls` if
both are provided.
* `highlight_size` or `highlight_s` (float, optional, default=None):
Note: `highlight_size` takes precedence over `highlight_s` if both
are provided.
* `highlight_color` or `highlight_c` (string, optional, default=None):
Note: `highlight_color` takes precedence over `highlight_c` if both
are provided.
* `consider_for_limits` (bool, optional, default=True): see
<autofig.call.Call.consider_for_limits>.
* `uncover` (bool, optional, default=False): see <autofig.call.Call.uncover>.
* `trail` (bool or Float, optional, default=False): see
<autofig.call.Call.trail>.
* `**kwargs`: additional keyword arguments are stored and passed on when
attaching to a parent axes. See <autofig.axes.Axes.add_call>.
Returns
---------
* the instantiated <autofig.call.Plot> object.
"""
if 'markersize' in kwargs.keys():
raise ValueError("use 'size' or 's' instead of 'markersize'")
if 'ms' in kwargs.keys():
raise ValueError("use 'size' or 's' instead of 'ms'")
if 'linewidth' in kwargs.keys():
raise ValueError("use 'size' or 's' instead of 'linewidth'")
if 'lw' in kwargs.keys():
raise ValueError("use 'size' or 's' instead of 'lw'")
size = kwargs.pop('size', None)
s = size if size is not None else s
smap = kwargs.pop('sizemap', smap)
self._s = CallDimensionS(self, s, None, sunit, slabel,
smap=smap, mode=smode)
color = kwargs.pop('color', None)
c = color if color is not None else c
cmap = kwargs.pop('colormap', cmap)
self._c = CallDimensionC(self, c, None, cunit, clabel, cmap=cmap)
self._axes = None # super will do this again, but we need it for setting marker, etc
self._axes_c = None
self._axes_s = None
self.highlight = highlight
highlight_marker = kwargs.pop('highlight_marker', None)
self.highlight_marker = highlight_marker
highlight_s = kwargs.pop('highlight_s', None)
highlight_size = kwargs.pop('highlight_size', highlight_s)
self.highlight_size = highlight_size
highlight_c = kwargs.pop('highlight_c', None)
highlight_color = kwargs.pop('highlight_color', highlight_c)
self.highlight_color = highlight_color
highlight_ls = kwargs.pop('highlight_ls', None)
highlight_linestyle = kwargs.pop('highlight_linestyle', highlight_ls)
self.highlight_linestyle = highlight_linestyle
m = kwargs.pop('m', None)
self.marker = marker if marker is not None else m
ls = kwargs.pop('ls', None)
self.linestyle = linestyle if linestyle is not None else ls
self.linebreak = linebreak
super(Plot, self).__init__(i=i, iunit=iunit, itol=itol,
x=x, xerror=xerror, xunit=xunit, xlabel=xlabel,
y=y, yerror=yerror, yunit=yunit, ylabel=ylabel,
z=z, zerror=zerror, zunit=zunit, zlabel=zlabel,
consider_for_limits=consider_for_limits,
uncover=uncover, trail=trail,
axorder=axorder, axpos=axpos,
title=title, label=label,
**kwargs
)
self.connect_callback(callbacks.update_sizes)
def __repr__(self):
dirs = []
for direction in ['i', 'x', 'y', 'z', 's', 'c']:
if getattr(self, direction).value is not None:
dirs.append(direction)
return "<Call:Plot | dims: {}>".format(", ".join(dirs))
@classmethod
def from_dict(cls, dict):
return cls(**dict)
def to_dict(self):
return {'classname': self.__class__.__name__,
'x': self.x.to_dict(),
'y': self.y.to_dict(),
'z': self.z.to_dict(),
'c': self.c.to_dict(),
's': self.s.to_dict(),
'i': self.i.to_dict(),
'axorder': self._axorder,
'axpos': self._axpos,
'title': self._title,
'label': self._label,
'marker': self._marker,
'linestyle': self._linestyle,
'linebreak': self._linebreak,
'highlight': self._highlight,
'highlight_linestyle': self._highlight_linestyle,
'highlight_size': self._highlight_size,
'highlight_color': self._highlight_color,
'highlight_marker': self._highlight_marker,
'uncover': self._uncover,
'trail': self._trail,
'consider_for_limits': self._consider_for_limits}
@property
def axes_c(self):
# currently no setter as this really should be handle by axes.add_call
return self._axes_c
@property
def axes_s(self):
# currently no setter as this really should be handle by axes.add_call
return self._axes_s
@property
def do_sizescale(self):
x = self.x.get_value()
y = self.y.get_value()
z = self.z.get_value()
s = self.s.get_value()
# DETERMINE WHICH SCALINGS WE NEED TO USE
if x is not None and y is not None:
return s is not None and not (isinstance(s, float) or isinstance(s, int))
else:
return False
@property
def do_colorscale(self):
x = self.x.get_value()
y = self.y.get_value()
z = self.z.get_value()
c = self.c.get_value()
# DETERMINE WHICH SCALINGS WE NEED TO USE
if x is not None and y is not None:
return c is not None and not isinstance(c, str)
else:
return False
@property
def highlight(self):
return self._highlight
@highlight.setter
def highlight(self, highlight):
if not isinstance(highlight, bool):
raise TypeError("highlight must be of type bool")
self._highlight = highlight
@property
def highlight_size(self):
if self._highlight_size is None:
# then default to twice the non-highlight size plus an offset
# so that small markers still have a considerably larger marker
# TODO: can we make this dependent on i?
if self.s.mode == 'pt':
return np.mean(self.get_sizes())*2
else:
return np.mean(self.get_sizes())*2
return self._highlight_size
@highlight_size.setter
def highlight_size(self, highlight_size):
if highlight_size is None:
self._highlight_size = None
return
if not (isinstance(highlight_size, float) or isinstance(highlight_size, int)):
raise TypeError("highlight_size must be of type float or int")
if highlight_size <= 0:
raise ValueError("highlight_size must be > 0")
self._highlight_size = highlight_size
@property
def highlight_marker(self):
if self._highlight_marker is None:
return 'o'
return self._highlight_marker
@highlight_marker.setter
def highlight_marker(self, highlight_marker):
if highlight_marker is None:
self._highlight_marker = None
return
if not isinstance(highlight_marker, str):
raise TypeError("highlight_marker must be of type str")
# TODO: make sure valid marker?
self._highlight_marker = highlight_marker
@property
def highlight_color(self):
# if self._highlight_color is None:
# return self.get_color()
return self._highlight_color
@highlight_color.setter
def highlight_color(self, highlight_color):
if highlight_color is None:
self._highlight_color = None
return
if not isinstance(highlight_color, str):
raise TypeError("highlight_color must be of type str")
self._highlight_color = common.coloralias.map(highlight_color)
@property
def highlight_linestyle(self):
if self._highlight_linestyle is None:
return 'None'
return self._highlight_linestyle
@highlight_linestyle.setter
def highlight_linestyle(self, highlight_linestyle):
if highlight_linestyle is None:
self._highlight_linestyle = None
return
if not isinstance(highlight_linestyle, str):
raise TypeError("highlight_linestyle must be of type str")
# TODO: make sure value ls?
self._highlight_linestyle = highlight_linestyle
def get_sizes(self, i=None):
s = self.s.get_value(i=i, unit=self.axes_s.unit if self.axes_s is not None else None)
if self.do_sizescale:
if self.axes_s is not None:
sizes = self.axes_s.normalize(s, i=i)
else:
# fallback on 0.01-0.05 mapping for just this call
sall = self.s.get_value(unit=self.axes_s.unit if self.axes_s is not None else None)
norm = plt.Normalize(np.nanmin(sall), np.nanmax(sall))
sizes = norm(s) * 0.04+0.01
else:
if s is not None:
sizes = s
elif self.s.mode == 'pt':
sizes = 1
else:
sizes = 0.02
return sizes
@property
def s(self):
return self._s
@property
def c(self):
return self._c
def get_color(self, colorcycler=None):
if isinstance(self.c.value, str):
color = self.c.value
else:
# then we'll defer to the cycler. If we want to color by
# the dimension, we should call self.c directly
color = None
if color is None and colorcycler is not None:
color = colorcycler.next_tmp
return color
@property
def color(self):
return self.get_color()
@color.setter
def color(self, color):
# TODO: type and cycler checks
color = common.coloralias.map(_map_none(color))
if self.axes is not None:
self.axes._colorcycler.replace_used(self.get_color(), color)
self._c.value = color
def get_cmap(self, cmapcycler=None):
if isinstance(self.c.value, str):
return None
if self.c.value is None:
return None
cmap = self.c.cmap
if cmap is None and cmapcycler is not None:
cmap = cmapcycler.next_tmp
return cmap
def get_marker(self, markercycler=None):
marker = self._marker
if marker is None:
if markercycler is not None:
marker = markercycler.next_tmp
else:
marker = '.'
return marker
@property
def marker(self):
return self.get_marker()
@marker.setter
def marker(self, marker):
# TODO: type and cycler checks
marker = _map_none(marker)
if self.axes is not None:
self.axes._markercycler.replace_used(self.get_marker(), marker)
self._marker = marker
def get_linestyle(self, linestylecycler=None):
ls = self._linestyle
if ls is None and linestylecycler is not None:
ls = linestylecycler.next_tmp
return ls
@property
def linestyle(self):
return self.get_linestyle()
@linestyle.setter
def linestyle(self, linestyle):
# TODO: type and cycler checks
linestyle = common.linestylealias.map(_map_none(linestyle))
if self.axes is not None:
self.axes._linestylecycler.replace_used(self.get_linestyle(), linestyle)
self._linestyle = linestyle
@property
def linebreak(self):
if self._linebreak is None:
return False
return self._linebreak
@linebreak.setter
def linebreak(self, linebreak):
if linebreak is None:
self._linebreak = linebreak
return
if not isinstance(linebreak, str):
raise TypeError("linebreak must be of type str, found {} {}".format(type(linebreak), linebreak))
if not len(linebreak)==2:
raise ValueError("linebreak must be of length 2")
if linebreak[0] not in common.dimensions:
raise ValueError("linebreak must start with one of {}".format(common.dimensions))
acceptable_ends = ['+', '-']
if linebreak[1] not in acceptable_ends:
raise ValueError("linebreak must end with one of {}".format(acceptable_ends))
self._linebreak = linebreak
def draw(self, ax=None, i=None,
colorcycler=None, markercycler=None, linestylecycler=None):
"""
See also:
* <autofig.draw>
* <autofig.figure.Figure.draw>
* <autofig.axes.Axes.draw>
* <autofig.call.Mesh.draw>
Arguments
-----------
* `ax`
* `i`
* `colorcycler`
* `markercycler`
* `linestylecycler`
"""
# Plot.draw
if ax is None:
ax = plt.gca()
else:
if not isinstance(ax, plt.Axes):
raise TypeError("ax must be of type plt.Axes")
if not (i is None or isinstance(i, float) or isinstance(i, int) or isinstance(i, u.Quantity) or isinstance(i, list) or isinstance(i, np.ndarray)):
raise TypeError("i must be of type float/int/list/None")
kwargs = self.kwargs.copy()
# determine 2D or 3D
axes_3d = isinstance(ax, Axes3D)
if (axes_3d and self.axes.projection=='2d') or (not axes_3d and self.axes.projection=='3d'):
raise ValueError("axes and projection do not agree")
# marker
marker = self.get_marker(markercycler=markercycler)
# linestyle - 'linestyle' has priority over 'ls'
ls = self.get_linestyle(linestylecycler=linestylecycler)
# color (NOTE: not necessarily the dimension c)
color = self.get_color(colorcycler=colorcycler)
# PREPARE FOR PLOTTING AND GET DATA
return_artists = []
# TODO: handle getting in correct units (possibly passed from axes?)
x = self.x.get_value(i=i, unit=self.axes.x.unit)
xerr = self.x.get_error(i=i, unit=self.axes.x.unit)
y = self.y.get_value(i=i, unit=self.axes.y.unit)
yerr = self.y.get_error(i=i, unit=self.axes.y.unit)
z = self.z.get_value(i=i, unit=self.axes.z.unit)
# zerr is handled below, only if axes_3ds
c = self.c.get_value(i=i, unit=self.axes_c.unit if self.axes_c is not None else None)
s = self.s.get_value(i=i, unit=self.axes_s.unit if self.axes_s is not None else None)
# bail on cases where we can't plot. This could possibly be due to
# sending Nones or Nans
# if x is None and y is None:
# return []
# if x is None and len(y) > 1:
# return []
# if y is None and len(x) > 1:
# return []
if axes_3d:
zerr = self.z.get_error(i=i, unit=self.axes.z.unit)
else:
zerr = None
# then we need to loop over the linebreaks
if isinstance(x, list) or isinstance(y, list):
linebreak_n = len(x) if isinstance(x, list) else len(y)
else:
linebreak_n = 1
xs = _to_linebreak_list(x, linebreak_n)
xerrs = _to_linebreak_list(xerr, linebreak_n)
ys = _to_linebreak_list(y, linebreak_n)
yerrs = _to_linebreak_list(yerr, linebreak_n)
zs = _to_linebreak_list(z, linebreak_n)
# zerrs = _to_linebreak_list(zerr, linebreak_n)
cs = _to_linebreak_list(c, linebreak_n)
ss = _to_linebreak_list(s, linebreak_n)
for loop1,(x,xerr,y,yerr,z,c,s) in enumerate(zip(xs, xerrs, ys, yerrs, zs, cs, ss)):
if axes_3d:
data = np.array([x, y, z])
points = np.array([x, y, z]).T.reshape(-1, 1, 3)
else:
data = np.array([x, y])
points = np.array([x, y]).T.reshape(-1, 1, 2)
# segments are used for LineCollection
segments = np.concatenate([points[:-1], points[1:]], axis=1)
# DETERMINE WHICH SCALINGS WE NEED TO USE
do_colorscale = self.do_colorscale
do_sizescale = self.do_sizescale
if x is not None and y is not None:
do_colorscale = c is not None and not isinstance(c, str)
do_sizescale = s is not None and not (isinstance(s, float) or isinstance(s, int))
else:
do_colorscale = False
do_sizescale = False
# DETERMINE PER-DATAPOINT Z-ORDERS
zorders, do_zorder = self.axes.z.get_zorders(z, i=i)
if axes_3d:
# TODO: we probably want to re-implement zorder, but then we need to
# sort in the *projected* z rather than data-z. We'll also need to
# figure out why LineCollection is complaining about the input shape
do_zorder = False
# ALLOW ACCESS TO COLOR FOR I OR LOOP
# TODO: in theory these could be exposed (maybe not the loop, but i)
def get_color_i(i, default=color):
if do_colorscale and self.axes_c is not None:
cmap = self.axes_c.cmap
norm = self.axes_c.get_norm(i=i)
ci = self.axes.c.get_value(i=i)
return plt.get_cmap(cmap)(norm(ci))
else:
return default
def get_color_loop(loop, do_zorder, default=color):
if do_colorscale and self.axes_c is not None:
cmap = self.axes_c.cmap
norm = self.axes_c.get_norm(i=i)
if do_zorder:
cloop = c[loop]
else:
cloop = c
return plt.get_cmap(cmap)(norm(cloop))
else:
return default
# BUILD KWARGS NEEDED FOR EACH CALL TO ERRORBAR
def error_kwargs_loop(xerr, yerr, zerr, loop, do_zorder):
def _get_error(errorarray, loop, do_zorder):
if errorarray is None:
return None
elif do_zorder:
return errorarray[loop]
else:
return errorarray
error_kwargs = {'xerr': _get_error(xerr, loop, do_zorder),
'yerr': _get_error(yerr, loop, do_zorder)}
if axes_3d:
error_kwargs['zerr'] = _get_error(zerr, loop, do_zorder)
error_kwargs['ecolor'] = get_color_loop(loop, do_zorder)
# not so sure that we want the errorbar linewidth to adjust based
# on size-scaling... but in theory we could do something like this:
# error_kwargs['elinewidth'] = sizes[loop]
return error_kwargs
# BUILD KWARGS NEEDED FOR EACH CALL TO LINECOLLECTION
lc_kwargs_const = {}
lc_kwargs_const['linestyle'] = ls
if do_colorscale:
lc_kwargs_const['norm'] = self.axes_c.get_norm(i=i) if self.axes_c is not None else None
lc_kwargs_const['cmap'] = self.axes_c.cmap if self.axes_c is not None else None
else:
lc_kwargs_const['color'] = color
# also set self._sizes so its accessible from the callback which
# will actually handle setting the sizes
sizes = self.get_sizes(i)
self._sizes = sizes
def sizes_loop(loop, do_zorder):
if do_zorder:
if isinstance(sizes, float):
return sizes
return sizes[loop]
else:
return sizes
def lc_kwargs_loop(lc_kwargs, loop, do_zorder):
if do_colorscale:
# nothing to do here, the norm and map are passed rather than values
pass
if do_sizescale:
# linewidth is handled by the callback
pass
return lc_kwargs
# BUILD KWARGS NEEDED FOR EACH CALL TO SCATTER
sc_kwargs_const = {}
sc_kwargs_const['marker'] = marker
sc_kwargs_const['linewidths'] = 0 # linewidths = 0 removes the black edge
sc_kwargs_const['edgecolors'] = 'none'
if do_colorscale:
sc_kwargs_const['norm'] = self.axes_c.get_norm(i=i) if self.axes_c is not None else None
sc_kwargs_const['cmap'] = self.axes_c.cmap if self.axes_c is not None else None
# we'll set sc_kwargs['cmap'] per-loop in the function below
else:
sc_kwargs_const['c'] = color
def sc_kwargs_loop(sc_kwargs, loop, do_zorder):
if do_colorscale:
if do_zorder:
sc_kwargs['c'] = c[loop]
else:
sc_kwargs['c'] = c
# if do_sizescale:
# if do_zorder:
# sc_kwargs['s'] = self.get_markersize(sizes[loop], scatter=True)
# else:
# sc_kwargs['s'] = self.get_markersize(sizes, scatter=True)
return sc_kwargs
# DRAW IF X AND Y ARE ARRAYS
if isinstance(x, np.ndarray) and isinstance(y, np.ndarray):
# LOOP OVER DATAPOINTS so that each can be drawn with its own zorder
if do_zorder:
datas = data.T
segments = segments
zorders = zorders
else:
datas = [data]
zorders = [zorders]
segments = [segments]
for loop2, (datapoint, segment, zorder) in enumerate(zip(datas, segments, zorders)):
return_artists_this_loop = []
# DRAW ERRORBARS, if applicable
# NOTE: we never pass a label here to avoid duplicate entries
# the actual datapoints are handled and labeled separately.
# Unfortunately this means the error bar will not be included
# in the styling of the legend.
if xerr is not None or yerr is not None or zerr is not None:
artists = ax.errorbar(*datapoint,
fmt='', linestyle='None',
zorder=zorder,
label=None,
**error_kwargs_loop(xerr, yerr, zerr, loop2, do_zorder))
# NOTE: these are currently not included in return_artists
# so they don't scale according to per-element sizes.
# But we may want to return them for completeness and may
# want some way of setting the size of the errobars,
# maybe similar to how highlight_size is handled
# errorbar actually returns a Container object of artists,
# so we need to cast to a list
# for artist_list in list(artists):
# if isinstance(artist_list, tuple):
# return_artists += list(artist_list)
# else:
# return_artists += [artist_list]
if do_colorscale or do_sizescale or do_zorder or marker in ['x', '+']:
# DRAW LINECOLLECTION, if applicable
if ls.lower() != 'none':
# TODO: color and zorder are assigned from the LEFT point in
# the segment. It may be nice to interpolate from LEFT-RIGHT
# by accessing zorder[loop+1] and c[loop+1]
if do_zorder:
segments = (segment,)
else:
segments = segment
if axes_3d:
lccall = Line3DCollection
else:
lccall = LineCollection
# we'll only include this in the legend for the first loop
# and if the marker isn't going to get its own entry.
# Unfortunately this means in these cases the
# marker will get precedent in the legend if both
# marker and linestyle are present
lc = lccall(segments,
zorder=zorder,
label=self.label if loop1==0 and loop2==0 and marker.lower()=='none' else None,
**lc_kwargs_loop(lc_kwargs_const, loop2, do_zorder))
if do_colorscale:
if do_zorder:
lc.set_array(np.array([c[loop2]]))
else:
lc.set_array(c)
return_artists_this_loop.append(lc)
ax.add_collection(lc)
# DRAW SCATTER, if applicable
if marker.lower() != 'none':
artist = ax.scatter(*datapoint,
zorder=zorder,
label=self.label if loop1==0 and loop2==0 else None,
**sc_kwargs_loop(sc_kwargs_const, loop2, do_zorder))
return_artists_this_loop.append(artist)
else:
# let's use plot whenever possible... it'll be faster
# and will guarantee that the linestyle looks correct
artists = ax.plot(*datapoint,
marker=marker,
ls=ls,
mec='none',
color=color,
label=self.label if loop1==0 and loop2==0 else None)
return_artists_this_loop += artists
size_this_loop = sizes_loop(loop2, do_zorder)
for artist in return_artists_this_loop:
# store the sizes so they can be rescaled appropriately by
# the callback
artist._af_sizes = size_this_loop
return_artists += return_artists_this_loop
# DRAW IF X OR Y ARE NOT ARRAYS
if not (isinstance(x, np.ndarray) and isinstance(y, np.ndarray)):
# TODO: can we do anything in 3D?
if x is not None:
artist = ax.axvline(x, ls=ls, color=color, label=self.label)
return_artists += [artist]
if y is not None:
artist = ax.axhline(y, ls=ls, color=color, label=self.label)
return_artists += [artist]
# DRAW HIGHLIGHT, if applicable (outside per-datapoint loop)
if self.highlight and i is not None:
if self.highlight_linestyle != 'None' and self.i.is_reference:
i_direction = self.i.reference
if i_direction == 'x':
linefunc = 'axvline'
elif i_direction == 'y':
linefunc = 'axhline'
else:
# TODO: can we do anything if in z?
linefunc = None
if linefunc is not None:
artist = getattr(ax, linefunc)(i,
ls=self.highlight_linestyle,
color=self.highlight_color if self.highlight_color is not None else color)
artist._af_highlight = True
return_artists += [artist]
if axes_3d:
# I do not understand why, but matplotlib requires these to be
# iterable when in 3d projection
highlight_data = ([self.x.highlight_at_i(i)],
[self.y.highlight_at_i(i)],
[self.z.highlight_at_i(i)])
else:
highlight_data = (self.x.highlight_at_i(i),
self.y.highlight_at_i(i))
artists = ax.plot(*highlight_data,
marker=self.highlight_marker,
ls=self.highlight_linestyle,
color=self.highlight_color if self.highlight_color is not None else color)
for artist in artists:
artist._af_highlight=True
return_artists += artists
self._backend_objects = return_artists
for artist in return_artists:
callbacks._connect_to_autofig(self, artist)
for callback in self.callbacks:
callback_callable = getattr(callbacks, callback)
callback_callable(artist, self)
return return_artists
class FillBetween(Call):
def __init__(self, x=None, y=None, c=None, i=None,
xunit=None, xlabel=None,
yunit=None, ylabel=None,
cunit=None, clabel=None, cmap=None,
iunit=None, itol=0.0,
axorder=None, axpos=None,
title=None,
label=None,
linebreak=None,
uncover=False, trail=False,
consider_for_limits=True,
**kwargs):
"""
Create a <autofig.call.FillBetween> object which defines a single call to
matplotlib.
See also:
* <autofig.call.Plot>
* <autofig.call.Mesh>
Arguments
-------------
* `x` (list/array, optional, default=None): array of values for the x-axes.
Access via <autofig.call.FillBetween.x>.
* `y` (list/array, optional, default=None): array of values for the y-axes.
Must have shape (len(x), 2)
Access via <autofig.call.FillBetween.y>.
* `c` or `color` (list/array, optional, default=None): array of values for the
color-direction. Access via <autofig.call.FillBetween.c>. Note: `color`
takes precedence over `c` if both are provided.
* `i` (list/array or string, optional, default=None): array of values for
the independent-variable. If a string, can be one of: 'x', 'y', 'z',
'c', 's' to reference an existing array. Access via
<autofig.call.FillBetween.i>.
* `xunit` (string or astropy unit, optional, default=None): units for `x`.
See <autofig.call.FillBetween.x> and <autofig.call.CallDimensionX.unit>.
* `xlabel` (strong, optional, default=None): label for `x`.
See <autofig.call.FillBetween.x> and <autofig.call.CallDimensionX.label>.
* `yunit` (string or astropy unit, optional, default=None): units for `y`.
See <autofig.call.FillBetween.y> and <autofig.call.CallDimensionY.unit>.
* `ylabel` (strong, optional, default=None): label for `y`.
See <autofig.call.FillBetween.y> and <autofig.call.CallDimensionY.label>.
* `iunit` (string or astropy unit, optional, default=None): units for `i`.
See <autofig.call.FillBetween.i> and <autofig.call.CallDimensionI.unit>.
* `itol` (float, optional, default=0.0): see <autofig.call.DimensionI.tol>.
* `axorder` (int, optional, default=None): see <autofig.call.FillBetween.axorder>.
* `axpos` (tuple, optional, default=None): see <autofig.call.FillBetween.axpos>.
* `title` (string, optional, default=None): see <autofig.call.FillBetween.title>.
* `label` (string, optional, default=None): see <autofig.call.FillBetween.label>.
* `linebreak` (string, optional, default=None): see <autofig.call.FillBetween.linebreak>.
* `consider_for_limits` (bool, optional, default=True): see
<autofig.call.Call.consider_for_limits>.
* `uncover` (bool, optional, default=False): see <autofig.call.Call.uncover>.
* `trail` (bool or Float, optional, default=False): see
<autofig.call.Call.trail>.
* `**kwargs`: additional keyword arguments are stored and passed on when
attaching to a parent axes. See <autofig.axes.Axes.add_call>.
Returns
---------
* the instantiated <autofig.call.FillBetween> object.
"""
color = kwargs.pop('color', None)
c = color if color is not None else c
cmap = kwargs.pop('colormap', cmap)
self._c = CallDimensionC(self, c, None, cunit, clabel, cmap=cmap)
self._axes = None # super will do this again, but we need it for setting marker, etc
self._axes_c = None
color = kwargs.pop('color', None)
c = color if color is not None else c
cmap = kwargs.pop('colormap', cmap)
self._c = CallDimensionC(self, c, None, cunit, clabel, cmap=cmap)
self.linebreak = linebreak
if x is None:
raise TypeError("x cannot be None for FillBetween")
x = np.asarray(x)
if y is None:
raise TypeError("y cannot be None for FillBetween")
y = np.asarray(y)
if y.shape not in [(len(x), 2), (len(x), 3)]:
raise ValueError("y must be of shape ({}, 2) or ({}, 3), not {}".format(len(x), len(x), y.shape))
super(FillBetween, self).__init__(i=i, iunit=iunit, itol=itol,
x=x, xunit=xunit, xlabel=xlabel,
y=y, yunit=yunit, ylabel=ylabel,
consider_for_limits=consider_for_limits,
uncover=uncover, trail=trail,
axorder=axorder, axpos=axpos,
title=title, label=label,
**kwargs
)
# self.connect_callback(callbacks.update_sizes)
def __repr__(self):
dirs = []
for direction in ['i', 'x', 'y', 'c']:
if getattr(self, direction).value is not None:
dirs.append(direction)
return "<Call:FillBetween | dims: {}>".format(", ".join(dirs))
@classmethod
def from_dict(cls, dict):
return cls(**dict)
def to_dict(self):
return {'classname': self.__class__.__name__,
'x': self.x.to_dict(),
'y': self.y.to_dict(),
'c': self.c.to_dict(),
'i': self.i.to_dict(),
'axorder': self._axorder,
'axpos': self._axpos,
'title': self._title,
'label': self._label,
'uncover': self._uncover,
'trail': self._trail,
'consider_for_limits': self._consider_for_limits}
@property
def axes_c(self):
# currently no setter as this really should be handle by axes.add_call
return self._axes_c
@property
def do_colorscale(self):
x = self.x.get_value()
y = self.y.get_value()
c = self.c.get_value()
# DETERMINE WHICH SCALINGS WE NEED TO USE
if x is not None and y is not None:
return c is not None and not isinstance(c, str)
else:
return False
@property
def c(self):
return self._c
def get_color(self, colorcycler=None):
if isinstance(self.c.value, str):
color = self.c.value
else:
# then we'll defer to the cycler. If we want to color by
# the dimension, we should call self.c directly
color = None
if color is None and colorcycler is not None:
color = colorcycler.next_tmp
return color
@property
def color(self):
return self.get_color()
@color.setter
def color(self, color):
# TODO: type and cycler checks
color = common.coloralias.map(_map_none(color))
if self.axes is not None:
self.axes._colorcycler.replace_used(self.get_color(), color)
self._c.value = color
def get_cmap(self, cmapcycler=None):
if isinstance(self.c.value, str):
return None
if self.c.value is None:
return None
cmap = self.c.cmap
if cmap is None and cmapcycler is not None:
cmap = cmapcycler.next_tmp
return cmap
@property
def linebreak(self):
if self._linebreak is None:
return False
return self._linebreak
@linebreak.setter
def linebreak(self, linebreak):
if linebreak is None:
self._linebreak = linebreak
return
if not isinstance(linebreak, str):
raise TypeError("linebreak must be of type str, found {} {}".format(type(linebreak), linebreak))
if not len(linebreak)==2:
raise ValueError("linebreak must be of length 2")
if linebreak[0] not in common.dimensions:
raise ValueError("linebreak must start with one of {}".format(common.dimensions))
acceptable_ends = ['+', '-']
if linebreak[1] not in acceptable_ends:
raise ValueError("linebreak must end with one of {}".format(acceptable_ends))
self._linebreak = linebreak
def draw(self, ax=None, i=None,
colorcycler=None, markercycler=None, linestylecycler=None):
"""
See also:
* <autofig.draw>
* <autofig.figure.Figure.draw>
* <autofig.axes.Axes.draw>
Arguments
-----------
* `ax`
* `i`
* `colorcycler`
* `markercycler`: ignored
* `linestylecycler`: ignored
"""
# Plot.draw
if ax is None:
ax = plt.gca()
else:
if not isinstance(ax, plt.Axes):
raise TypeError("ax must be of type plt.Axes")
if not (i is None or isinstance(i, float) or isinstance(i, int) or isinstance(i, u.Quantity) or isinstance(i, list) or isinstance(i, np.ndarray)):
raise TypeError("i must be of type float/int/list/None")
kwargs = self.kwargs.copy()
# determine 2D or 3D
axes_3d = isinstance(ax, Axes3D)
if (axes_3d and self.axes.projection=='2d') or (not axes_3d and self.axes.projection=='3d'):
raise ValueError("axes and projection do not agree")
# color (NOTE: not necessarily the dimension c)
color = self.get_color(colorcycler=colorcycler)
# PREPARE FOR PLOTTING AND GET DATA
return_artists = []
# TODO: handle getting in correct units (possibly passed from axes?)
x = self.x.get_value(i=i, unit=self.axes.x.unit)
y = self.y.get_value(i=i, unit=self.axes.y.unit)
if isinstance(y, list):
y = [yi.T for yi in y]
else:
y = y.T
c = self.c.get_value(i=i, unit=self.axes_c.unit if self.axes_c is not None else None)
# then we need to loop over the linebreaks
if isinstance(x, list) or isinstance(y, list):
linebreak_n = len(x) if isinstance(x, list) else len(y)
else:
linebreak_n = 1
xs = _to_linebreak_list(x, linebreak_n)
ys = _to_linebreak_list(y, linebreak_n)
cs = _to_linebreak_list(c, linebreak_n)
for loop1,(x,y,c) in enumerate(zip(xs, ys, cs)):
data = np.array([x, y[0], y[-1]])
if len(y) == 3:
data_middle = np.array([x, y[1]])
else:
data_middle = None
# points = np.array([x, y1, y2]).T.reshape(-1, 1, 3)
# segments are used for LineCollection
# segments = np.concatenate([points[:-1], points[1:]], axis=1)
# DETERMINE WHICH SCALINGS WE NEED TO USE
do_colorscale = self.do_colorscale
if x is not None and y is not None:
do_colorscale = c is not None and not isinstance(c, str)
else:
do_colorscale = False
# ALLOW ACCESS TO COLOR FOR I OR LOOP
# TODO: in theory these could be exposed (maybe not the loop, but i)
# def get_color_i(i, default=color):
# if do_colorscale and self.axes_c is not None:
# cmap = self.axes_c.cmap
# norm = self.axes_c.get_norm(i=i)
# ci = self.axes.c.get_value(i=i)
# return plt.get_cmap(cmap)(norm(ci))
# else:
# return default
#
# def get_color_loop(loop, do_zorder, default=color):
# if do_colorscale and self.axes_c is not None:
# cmap = self.axes_c.cmap
# norm = self.axes_c.get_norm(i=i)
# if do_zorder:
# cloop = c[loop]
# else:
# cloop = c
# return plt.get_cmap(cmap)(norm(cloop))
# else:
# return default
fb_kwargs = {}
fb_kwargs['color'] = color
fb_kwargs['alpha'] = 0.6 # TODO: make this an option
if do_colorscale:
fb_kwargs['norm'] = self.axes_c.get_norm(i=i) if self.axes_c is not None else None
fb_kwargs['cmap'] = self.axes_c.cmap if self.axes_c is not None else None
artist = ax.fill_between(*data, **fb_kwargs)
return_artists += [artist]
if data_middle is not None:
_ = fb_kwargs.pop('alpha')
fb_kwargs['linestyle'] = 'solid'
fb_kwargs['marker'] = 'None'
artists = ax.plot(*data_middle, **fb_kwargs)
return_artists += artists
self._backend_objects = return_artists
for artist in return_artists:
callbacks._connect_to_autofig(self, artist)
for callback in self.callbacks:
callback_callable = getattr(callbacks, callback)
callback_callable(artist, self)
return return_artists
class Mesh(Call):
def __init__(self, x=None, y=None, z=None, fc=None, ec=None, i=None,
xerror=None, xunit=None, xlabel=None, xnormals=None,
yerror=None, yunit=None, ylabel=None, ynormals=None,
zerror=None, zunit=None, zlabel=None, znormals=None,
fcunit=None, fclabel=None, fcmap=None,
ecunit=None, eclabel=None, ecmap=None,
iunit=None, itol=0.0,
axorder=None, axpos=None,
title=None, label=None,
linestyle=None,
consider_for_limits=True,
uncover=True,
trail=0,
exclude_back=False,
**kwargs):
"""
Create a <autofig.call.Mesh> object which defines a single call to
matplotlib.
See also:
* <autofig.call.Plot>
Arguments
-------------
* `x` (list/array, optional, default=None): array of values for the x-axes.
Access via <autofig.call.Mesh.x>.
* `y` (list/array, optional, default=None): array of values for the y-axes.
Access via <autofig.call.Mesh.y>.
* `z` (list/array, optional, default=None): array of values for the z-axes.
Access via <autofig.call.Mesh.z>
* `fc` or `facecolor` (list/array, optional, default=None): array of values for the
facecolor-direction. Access via <autofig.call.Mesh.fc>. Note: `facecolor`
takes precedence over `fc` if both are provided.
* `ec` or `edgecolor` (list/array, optional, default=None): array of values for the
edgecolor-direction. Access via <autofig.call.Mesh.ec>. Note: `edgecolor`
takes precedence over `ec` if both are provided.
* `i` (list/array or string, optional, default=None): array of values for
the independent-variable. If a string, can be one of: 'x', 'y', 'z',
'fc', 'ec' to reference an existing array. Access via
<autofig.call.Mesh.i>.
* `xerror` (float or list/array, optional, default=None): errors for `x`.
See <autofig.call.Mesh.x> and <autofig.call.CallDimensionX.error>.
* `xunit` (string or astropy unit, optional, default=None): units for `x`.
See <autofig.call.Mesh.x> and <autofig.call.CallDimensionX.unit>.
* `xlabel` (strong, optional, default=None): label for `x`.
See <autofig.call.Mesh.x> and <autofig.call.CallDimensionX.label>.
* `xnormals` (list/array, optional, default=None): normals for `x`.
Currently ignored.
See <autofig.call.Mesh.x> and <autofig.call.CallDimensionX.normals>.
* `yerror` (float or list/array, optional, default=None): errors for `y`.
See <autofig.call.Mesh.y> and <autofig.call.CallDimensionY.error>.
* `yunit` (string or astropy unit, optional, default=None): units for `y`.
See <autofig.call.Mesh.y> and <autofig.call.CallDimensionY.unit>.
* `ylabel` (strong, optional, default=None): label for `y`.
See <autofig.call.Mesh.y> and <autofig.call.CallDimensionY.label>.
* `ynormals` (list/array, optional, default=None): normals for `y`.
Currently ignored.
See <autofig.call.Mesh.y> and <autofig.call.CallDimensionY.normals>.
* `zerror` (float or list/array, optional, default=None): errors for `z`.
See <autofig.call.Mesh.z> and <autofig.call.CallDimensionZ.error>.
* `zunit` (string or astropy unit, optional, default=None): units for `z`.
See <autofig.call.Mesh.z> and <autofig.call.CallDimensionZ.unit>.
* `zlabel` (strong, optional, default=None): label for `x`.
See <autofig.call.Mesh.z> and <autofig.call.CallDimensionZ.label>.
* `znormals` (list/array, optional, default=None): normals for `z`.
If provided then the back of the mesh can be ignored by setting
`exclude_back=True`.
See <autofig.call.Mesh.z> and <autofig.call.CallDimensionZ.normals>.
* `fcerror` (float or list/array, optional, default=None): errors for `fc`.
See <autofig.call.Mesh.fc> and <autofig.call.CallDimensionC.error>.
* `fcunit` (string or astropy unit, optional, default=None): units for `fc`.
See <autofig.call.Mesh.fc> and <autofig.call.CallDimensionC.unit>.
* `fclabel` (strong, optional, default=None): label for `fc`.
See <autofig.call.Mesh.fc> and <autofig.call.CallDimensionC.label>.
* `ecerror` (float or list/array, optional, default=None): errors for `ec`.
See <autofig.call.Mesh.ec> and <autofig.call.CallDimensionC.error>.
* `ecunit` (string or astropy unit, optional, default=None): units for `ec`.
See <autofig.call.Mesh.ec> and <autofig.call.CallDimensionC.unit>.
* `eclabel` (strong, optional, default=None): label for `ec`.
See <autofig.call.Mesh.ec> and <autofig.call.CallDimensionC.label>.
* `iunit` (string or astropy unit, optional, default=None): units for `i`.
See <autofig.call.Mesh.i> and <autofig.call.CallDimensionI.unit>.
* `itol` (float, optional, default=0.0): see <autofig.call.DimensionI.tol>.
* `axorder` (int, optional, default=None): see <autofig.call.Mesh.axorder>.
* `axpos` (tuple, optional, default=None): see <autofig.call.Mesh.axpos>.
* `title` (string, optional, default=None): see <autofig.call.Mesh.title>.
* `label` (string, optional, default=None): see <autofig.call.Mesh.label>.
* `linestyle` or `ls` (string, optional, default='solid'): see
<autofig.call.Mesh.linestyle>. Note: `linestyle` takes precedence
over `ls` if both are provided. So technically `ls` defaults
to 'solid' and `linestyle` defaults to None.
* `consider_for_limits` (bool, optional, default=True): see
<autofig.call.Call.consider_for_limits>.
* `exclude_back` (bool, optional, default=False): whether to exclude
any elements pointing away from the screen. This will be ignored
for 3d projections or if `znormals` is not provided. Setting this
to True can save significant time in drawing the mesh in matplotlib,
and is especially useful for closed surfaces if `fc` is not 'none'.
* `**kwargs`: additional keyword arguments are stored and passed on when
attaching to a parent axes. See <autofig.axes.Axes.add_call>.
Returns
---------
* the instantiated <autofig.call.Mesh> object.
"""
self._axes_fc = None
self._axes_ec = None
facecolor = kwargs.pop('facecolor', None)
fc = facecolor if facecolor is not None else fc
self._fc = CallDimensionC(self, fc, None, fcunit, fclabel, cmap=fcmap)
edgecolor = kwargs.pop('edgecolor', None)
ec = edgecolor if edgecolor is not None else ec
self._ec = CallDimensionC(self, ec, None, ecunit, eclabel, cmap=ecmap)
ls = kwargs.pop('ls', 'solid')
self.linestyle = linestyle if linestyle is not None else ls
self.linebreak = False
self.exclude_back = exclude_back
if hasattr(i, '__iter__') and not isinstance(i, u.Quantity):
raise ValueError("i as an iterable not supported for Meshes, make separate calls for each value of i")
super(Mesh, self).__init__(i=i, iunit=iunit, itol=itol,
x=x, xerror=xerror, xunit=xunit, xlabel=xlabel, xnormals=xnormals,
y=y, yerror=yerror, yunit=yunit, ylabel=ylabel, ynormals=ynormals,
z=z, zerror=zerror, zunit=zunit, zlabel=zlabel, znormals=znormals,
consider_for_limits=consider_for_limits,
uncover=uncover, trail=trail,
axorder=axorder, axpos=axpos,
title=title, label=label,
**kwargs
)
def __repr__(self):
dirs = []
for direction in ['i', 'x', 'y', 'z', 'fc', 'ec']:
if getattr(self, direction).value is not None:
dirs.append(direction)
return "<Call:Mesh | dims: {}>".format(", ".join(dirs))
@classmethod
def from_dict(cls, dict):
return cls(**dict)
def to_dict(self):
return {'classname': self.__class__.__name__,
'x': self.x.to_dict(),
'y': self.y.to_dict(),
'z': self.z.to_dict(),
'fc': self.fc.to_dict(),
'ec': self.ec.to_dict(),
'i': self.i.to_dict(),
'axorder': self._axorder,
'axpos': self._axpos,
'title': self._title,
'label': self._label,
'uncover': self._uncover,
'trail': self._trail,
'consider_for_limits': self._consider_for_limits,
'exclude_back': self._exclude_back}
@property
def axes_fc(self):
# currently no setter as this really should be handle by axes.add_call
return self._axes_fc
@property
def axes_ec(self):
# currently no setter as this really should be handle by axes.add_call
return self._axes_ec
@property
def c(self):
"""
Returns
---------
* <autofig.call.CallDimensionCGroup> of <autofig.call.Mesh.fc> and
<autofig.call.Mesh.ec>
"""
return CallDimensionCGroup([self.fc, self.ec])
@property
def fc(self):
"""
See also:
* <autofig.call.Mesh.get_facecolor>
Returns
----------
* <autofig.call.CallDimensionC>
"""
return self._fc
def get_facecolor(self, colorcycler=None):
"""
See also:
* <autofig.call.Mesh.fc>
Arguments
-----------
* `colorcycler` (optional, default=None): **IGNORED** (only included
to have a similar calling signature as other methods that do
account for color cyclers)
Returns
----------
* (string): 'none' if <autofig.call.Mesh.fc> is not a string.
"""
if isinstance(self.fc.value, str):
color = self.fc.value
else:
# then we'll default to 'none'. If we want to color by
# the dimension, we should call self.c directly
color = 'none'
# we won't use the colorcycler for facecolor
return color
@property
def facecolor(self):
"""
Shortcut to <autofig.call.Mesh.get_facecolor>.
See also:
* <autofig.call.Mesh.fc>
Returns
----------
* (string)
"""
return self.get_facecolor()
@facecolor.setter
def facecolor(self, facecolor):
# TODO: type and cycler checks
facecolor = common.coloralias.map(_map_none(facecolor))
if self.axes is not None:
self.axes._colorcycler.replace_used(self.get_facecolor(), facecolor)
self._fc.value = facecolor
def get_fcmap(self, cmapcycler=None):
if isinstance(self.fc.value, str):
return None
if self.fc.value is None:
return None
cmap = self.fc.cmap
if cmap is None and cmapcycler is not None:
cmap = cmapcycler.next_tmp
return cmap
@property
def ec(self):
"""
See also:
* <autofig.call.Mesh.get_edgecolor>
Returns
----------
* <autofig.call.CallDimensionC>
"""
return self._ec
def get_edgecolor(self, colorcycler=None):
"""
See also:
* <autofig.call.Mesh.ec>
Arguments
-----------
* `colorcycler` (optional, default=None): **IGNORED** (only included
to have a similar calling signature as other methods that do
account for color cyclers)
Returns
----------
* (string): 'black' if <autofig.call.Mesh.ec> is not a string.
"""
if isinstance(self.ec.value, str):
color = self.ec.value
else:
# then we'll default to black. If we want to color by
# the dimension, we should call self.c directly
color = 'black'
# we won't use the colorcycler for edgecolor
return color
@property
def edgecolor(self):
"""
Shortcut to <autofig.call.Mesh.get_edgecolor>.
See also:
* <autofig.call.Mesh.ec>
Returns
----------
* (string)
"""
return self.get_edgecolor()
@edgecolor.setter
def edgecolor(self, edgecolor):
# TODO: type and cycler checks
if edgecolor in ['face']:
self._ec.value = edgecolor
return
edgecolor = common.coloralias.map(_map_none(edgecolor))
if self.axes is not None:
self.axes._colorcycler.replace_used(self.get_edgecolor(), edgecolor)
self._ec.value = edgecolor
def get_ecmap(self, cmapcycler=None):
if isinstance(self.ec.value, str):
return None
if self.ec.value is None:
return None
cmap = self.ec.cmap
if cmap is None and cmapcycler is not None:
cmap = cmapcycler.next_tmp
return cmap
@property
def exclude_back(self):
return self._exclude_back
@exclude_back.setter
def exclude_back(self, exclude_back):
if not isinstance(exclude_back, bool):
raise TypeError("exclude back must be of type bool")
self._exclude_back = exclude_back
def draw(self, ax=None, i=None,
colorcycler=None, markercycler=None, linestylecycler=None):
"""
See also:
* <autofig.draw>
* <autofig.figure.Figure.draw>
* <autofig.axes.Axes.draw>
* <autofig.call.Plot.draw>
Arguments
----------
* `ax`
* `i`
* `colorcycler`
* `markercycler`
* `linestylecycler`
"""
# Mesh.draw
if ax is None:
ax = plt.gca()
else:
if not isinstance(ax, plt.Axes):
raise TypeError("ax must be of type plt.Axes")
if not (i is None or isinstance(i, float) or isinstance(i, int) or isinstance(i, u.Quantity)):
raise TypeError("i must be of type float/int/None")
# determine 2D or 3D
axes_3d = isinstance(ax, Axes3D)
kwargs = self.kwargs.copy()
# PLOTTING
return_artists = []
x = self.x.get_value(i=i, sort_by_indep=False, exclude_back=self.exclude_back, unit=self.axes.x.unit)
y = self.y.get_value(i=i, sort_by_indep=False, exclude_back=self.exclude_back, unit=self.axes.y.unit)
z = self.z.get_value(i=i, sort_by_indep=False, exclude_back=self.exclude_back, unit=self.axes.z.unit)
fc = self.fc.get_value(i=i, sort_by_indep=False, exclude_back=self.exclude_back, unit=self.axes_fc.unit if self.axes_fc is not None else None)
ec = self.ec.get_value(i=i, sort_by_indep=False, exclude_back=self.exclude_back, unit=self.axes_ec.unit if self.axes_ec is not None else None)
# DETERMINE PER-DATAPOINT Z-ORDERS
zorders, do_zorder = self.axes.z.get_zorders(z, i=i)
if do_zorder:
# we can perhaps skip doing the zorder loop if there are no other
# calls within the axes
if len(self.axes.calls) == 1:
do_zorder = False
zorders = np.mean(zorders)
if axes_3d:
if x is not None and y is not None and z is not None:
polygons = np.concatenate((x[:,:,np.newaxis], y[:,:,np.newaxis], z[:,:,np.newaxis]), axis=2)
else:
# there isn't anything to plot here, the current i probably
# filtered this call out
return []
pccall = Poly3DCollection
else:
if x is not None and y is not None:
polygons = np.concatenate((x[:,:,np.newaxis], y[:,:,np.newaxis]), axis=2)
if not do_zorder and z is not None:
# then we'll handle zorder within this Mesh call by
# sorting instead of looping. This is MUCH quicking
# and less memory instensive
sortinds = np.mean(z, axis=1).argsort()
polygons = polygons[sortinds, :, :]
if isinstance(fc, np.ndarray):
fc = fc[sortinds]
if isinstance(ec, np.ndarray):
ec = ec[sortinds]
else:
# there isn't anything to plot here, the current i probably
# filtered this call out
return []
pccall = PolyCollection
do_facecolorscale = fc is not None and not isinstance(fc, str)
do_edgecolorscale = ec is not None and not isinstance(ec, str)
if do_edgecolorscale:
if self.axes_ec is None:
raise NotImplementedError("currently only support edgecolor once attached to axes")
else:
edgenorm = self.axes_ec.get_norm(i=i)
edgecmap = self.axes_ec.cmap
edgecolors = plt.get_cmap(edgecmap)(edgenorm(ec))
else:
edgecolors = self.get_edgecolor(colorcycler=colorcycler)
if do_facecolorscale:
if self.axes_fc is None:
raise NotImplementedError("currently only support facecolor once attached to axes")
else:
facenorm = self.axes_fc.get_norm(i=i)
facecmap = self.axes_fc.cmap
facecolors = plt.get_cmap(facecmap)(facenorm(fc))
else:
facecolors = self.get_facecolor(colorcycler=colorcycler)
if do_zorder:
# LOOP THROUGH POLYGONS so each can be assigned its own zorder
if isinstance(edgecolors, str):
edgecolors = [edgecolors] * len(zorders)
if isinstance(facecolors, str):
facecolors = [facecolors] * len(zorders)
for loop, (polygon, zorder, edgecolor, facecolor) in enumerate(zip(polygons, zorders, edgecolors, facecolors)):
pc = pccall((polygon,),
linestyle=self.linestyle,
edgecolors=edgecolor,
facecolors=facecolor,
zorder=zorder,
label=self.label if loop==0 else None)
ax.add_collection(pc)
return_artists += [pc]
else:
# DON'T LOOP as all have the same zorder, this should be faster
pc = pccall(polygons,
linestyle=self.linestyle,
edgecolors=edgecolors,
facecolors=facecolors,
zorder=zorders,
label=self.label)
ax.add_collection(pc)
return_artists += [pc]
self._backend_objects = return_artists
for artist in return_artists:
callbacks._connect_to_autofig(self, artist)
return return_artists
class CallDimensionGroup(common.Group):
def __init__(self, items):
super(CallDimensionGroup, self).__init__(CallDimension, [], items)
@property
def value(self):
"""
Returns
---------
* (list) a list of <autofig.call.CallDimension.value> for each child
<autofig.call.CallDimension>
"""
return np.array([c.value for c in self._items]).flatten()
@property
def units(self):
"""
"""
return [c.unit for c in self._items]
@property
def unit(self):
units = list(set(self.units))
if len(units) > 1:
raise ValueError("more than 1 units, see units")
else:
return units[0]
@property
def labels(self):
"""
"""
return [c.label for c in self._items]
@property
def label(self):
labels = list(set(self.labels))
if len(labels) > 1:
raise ValueError("more than 1 labels, see labels")
else:
return labels[0]
class CallDimensionCGroup(CallDimensionGroup):
@property
def cmap(self):
"""
Returns
---------
* (list) a list of <autofig.call.CallDimensionC.cmap> for each child
<autofig.call.CallDimensionC>
"""
return self._get_attrs('cmap')
@cmap.setter
def cmap(self, smap):
return self._set_attrs('cmap', cmap)
class CallDimensionSGroup(CallDimensionGroup):
@property
def smap(self):
"""
Returns
---------
* (list) a list of <autofig.call.CallDimensionS.smap> for each child
<autofig.call.CallDimensionS>
"""
return self._get_attrs('smap')
@smap.setter
def smap(self, smap):
return self._set_attrs('smap', smap)
@property
def mode(self):
"""
Returns
---------
* (list) a list of <autofig.call.CallDimensionS.mode> for each child
<autofig.call.CallDimensionS>
"""
return self._get_attrs('mode')
@mode.setter
def mode(self, mode):
return self._set_attrs('mode', mode)
def make_calldimensiongroup(items):
if np.all([isinstance(item, CallDimensionC) for item in items]):
return CallDimensionCGroup(items)
elif np.all([isinstance(item, CallDimensionS) for item in items]):
return CallDimensionSGroup(items)
else:
return CallDimensionGroup(items)
class CallDimension(object):
def __init__(self, direction, call, value, error=None, unit=None, label=None, normals=None):
if isinstance(value, dict):
error = value.get('error', error)
unit = value.get('unit', unit)
label = value.get('label', label)
normals = value.get('normals', normals)
value = value.get('value')
self._call = call
self.direction = direction
# unit must be set before value as setting value pulls the appropriate
# unit for CallDimensionI
self.unit = unit
self.value = value
self.error = error
self.label = label
self.normals = normals
# self.lim = lim
def __repr__(self):
if isinstance(self.value, np.ndarray):
info = "len: {}".format(len(self.value))
else:
info = "value: {}".format(self.value)
return "<{} | {} | type: {} | label: {}>".format(self.direction,
info,
self.unit.physical_type,
self.label)
@classmethod
def from_dict(cls, dict):
return cls(**dict)
def to_dict(self):
return {'direction': self.direction,
'unit': self.unit.to_string(),
'value': common.arraytolistrecursive(self._value),
'error': common.arraytolistrecursive(self._error),
'label': self._label,
'normals': common.arraytolistrecursive(self._normals)}
@property
def call(self):
"""
Returns
---------
* <autofig.call.Call> (<autofig.call.Plot> or <autofig.call.Mesh>): the
parent call object.
"""
return self._call
@property
def direction(self):
"""
Returns
-------------
* (str) one of 'i', 'x', 'y', 'z', 's', 'c'
"""
return self._direction
@direction.setter
def direction(self, direction):
"""
set the direction
"""
if not isinstance(direction, str):
raise TypeError("direction must be of type str")
accepted_values = ['i', 'x', 'y', 'z', 's', 'c']
if direction not in accepted_values:
raise ValueError("must be one of: {}".format(accepted_values))
self._direction = direction
def _to_unit(self, value, unit=None):
if isinstance(value, str):
return value
if value is None:
return value
if unit is not None and unit!=u.dimensionless_unscaled:
unit = common._convert_unit(unit)
value = value*self.unit.to(unit)
return value
def interpolate_at_i(self, i, unit=None):
"""
Access the interpolated value at a given value of `i` (independent-variable).
Arguments
-----------
`i`
`unit` (unit or string, optional, default=None)
Returns
-------------
* (float): the interpolated value
Raises
------------
* ValueError: if there is a lenght mismatch
"""
if isinstance(self.call.i._value, float):
if self.call.i._value==i:
return self._to_unit(self._value, unit)
else:
return None
# we can't call i._value here because that may point to a string, and
# we want this to resolve the array
i_value = self.call.i.get_value(linebreak=False, sort_by_indep=False)
if len(i_value) != len(self._value):
raise ValueError("length mismatch with independent-variable")
sort_inds = i_value.argsort()
indep_value = i_value[sort_inds]
this_value = self._value[sort_inds]
if len(self._value.shape) > 1:
return np.asarray([self._to_unit(np.interp(i, indep_value, this_value_col, left=np.nan, right=np.nan), unit) for this_value_col in this_value.T]).T
return self._to_unit(np.interp(i, indep_value, this_value, left=np.nan, right=np.nan), unit)
def highlight_at_i(self, i, unit=None):
"""
"""
if len(self._value.shape)==1 and isinstance(self.call.i.value, np.ndarray):
return self.interpolate_at_i(i, unit=unit)
else:
return self._to_unit(self._value[self._filter_at_i(i,
uncover=True,
trail=0)].T,
unit)
def _do_linebreak(self, func='get_value', i=None, unit=None,
uncover=None, trail=None, linebreak=None,
sort_by_indep=None):
"""
"""
if linebreak is None:
linebreak = self.linebreak
this_array = getattr(self, func)(i=i,
unit=unit,
uncover=uncover,
trail=trail,
linebreak=False)
if linebreak is False:
return this_array
break_direction = linebreak[0]
# NOTE: we don't need the unit here since we just use it to find
# breakpoints
break_array = getattr(self.call, break_direction).get_value(i=i,
unit=None,
uncover=uncover,
trail=trail,
linebreak=False,
sort_by_indep=sort_by_indep)
if linebreak[1] == '+':
split_inds = np.where(break_array[1:]-break_array[:-1]>0)[0]
elif linebreak[1] == '-':
split_inds = np.where(break_array[1:]-break_array[:-1]<0)[0]
else:
raise NotImplementedError("linebreak='{}' not supported".format(linebreak))
return np.split(this_array, split_inds+1)
def _sort_by_indep(self, func='get_value', i=None, iunit=None, unit=None,
uncover=None, trail=None, linebreak=None,
sort_by_indep=None):
"""
must be called before (or within) _do_linebreak
"""
if sort_by_indep is None:
# TODO: add property of the call?
sort_by_indep = True
indep_array = self.call.i.get_value(i=i,
unit=iunit,
uncover=uncover,
trail=trail,
linebreak=False,
sort_by_indep=False)
this_array = getattr(self, func)(i=i,
unit=unit,
uncover=uncover,
trail=trail,
linebreak=False,
sort_by_indep=False)
if not (isinstance(indep_array, np.ndarray) and len(indep_array)==len(this_array)):
sort_by_indep = False
if sort_by_indep:
# TODO: it might be nice to buffer this at the call level, so making
# multiple get_value calls doesn't have to recompute the sort-order
sort_inds = indep_array.argsort()
return this_array[sort_inds]
else:
return this_array
def _get_trail_min(self, i, trail=None):
trail = self.call.trail if trail is None else trail
# determine length of the trail (if applicable)
if trail is not False:
if trail is True:
# then fallback on 10% default
trail_perc = 0.1
else:
trail_perc = float(trail)
if trail_perc == 0.0:
trail_i = i
else:
all_i = np.hstack(self.call.axes.calls.i.value)
trail_i = i - trail_perc*(np.nanmax(all_i) - np.nanmin(all_i))
if trail_i < np.nanmin(self.call.i.get_value(linebreak=False, sort_by_indep=False)):
# don't allow extraploating below the lower range
trail_i = np.nanmin(self.call.i.get_value(linebreak=False, sort_by_indep=False))
else:
trail_i = None
return trail_i
def _filter_at_i(self, i, uncover=None, trail=None):
uncover = self.call.uncover if uncover is None else uncover
trail = self.call.trail if trail is None else trail
# we can't call i._value here because that may point to a string, and
# we want this to resolve the array
i_value = self.call.i.get_value(linebreak=False, sort_by_indep=False)
if isinstance(i_value, np.ndarray):
trues = np.ones(i_value.shape, dtype=bool)
else:
trues = True
if trail is not False:
trail_i = self._get_trail_min(i=i, trail=trail)
left_filter = i_value >= trail_i - self.call.i.tol
else:
left_filter = trues
if uncover is not False:
right_filter = i_value <= i + self.call.i.tol
else:
right_filter = trues
return (left_filter & right_filter)
def get_value(self, i=None, unit=None,
uncover=None, trail=None,
linebreak=None, sort_by_indep=None,
exclude_back=False,
attr='_value'):
"""
Access the value for a given value of `i` (independent-variable) depending
on which effects (i.e. uncover) are enabled.
If `uncover`, `trail`, or `linebreak` are None (default), then the value from
the parent <autofig.call.Call> from <autofig.call.CallDimension.call>
(probably (<autofig.call.Plot>) will be used. See <autofig.call.Plot.uncover>,
<autofig.call.Plot.trail>, <autofig.call.Plot.linebreak>.
Arguments
-----------
* `i`
* `unit`
* `uncover`
* `trail`
* `linebreak`
* `sort_by_indep`
* `exclude_back`
* `attr`
Returns
----------
* (array or None)
"""
value = getattr(self, attr) # could be self._value or self._error
if value is None:
return None
if uncover is None:
uncover = self.call.uncover
if trail is None:
trail = self.call.trail
if linebreak is None:
linebreak = self.call.linebreak
if sort_by_indep is None:
# TODO: make this a property of the call?
sort_by_indep = True
if isinstance(value, str) or isinstance(value, float):
if i is None:
return self._to_unit(value, unit)
elif isinstance(self.call.i.value, float):
# then we still want to "select" based on the value of i
if self._filter_at_i(i):
return value
else:
return None
else:
# then we should show either way. For example - a color or
# axhline even with i given won't change in i
return self._to_unit(value, unit)
if isinstance(value, list) or isinstance(value, tuple):
value = np.asarray(value)
# from here on we're assuming the value is an array, so let's just check
# to be sure
if not isinstance(value, np.ndarray):
raise NotImplementedError("value/error must be a numpy array")
if exclude_back and self.call.z.normals is not None and self.call.axes.projection == '2d':
value = value[self.call.z.normals >= 0]
if linebreak is not False:
return self._do_linebreak(func='get{}'.format(attr),
i=i,
unit=unit,
uncover=uncover,
trail=trail,
linebreak=linebreak,
sort_by_indep=sort_by_indep)
if sort_by_indep is not False:
# if we've made it here, linebreak should already be False (if
# linebreak was True, then we'd be within _do_linebreak and those
# get_value calls pass linebreak=False)
return self._sort_by_indep(func='get{}'.format(attr),
i=i,
unit=unit,
uncover=uncover,
trail=trail,
linebreak=False,
sort_by_indep=sort_by_indep)
# from here on, linebreak==False and sort_by_indep==False (if either
# were True, then we're within those functions and asking for the original
# array)
if i is None:
if len(value.shape)==1:
return self._to_unit(value, unit)
else:
if isinstance(self.call, Plot):
return self._to_unit(value.T, unit)
else:
return self._to_unit(value, unit)
# filter the data as necessary
filter_ = self._filter_at_i(i, uncover=uncover, trail=trail)
if isinstance(self.call.i.value, float):
if filter_:
return self._to_unit(value, unit)
else:
return None
if len(value.shape)==1 or isinstance(self.call, FillBetween):
# then we're dealing with a flat 1D array
if attr == '_value':
if trail is not False:
trail_i = self._get_trail_min(i)
first_point = self.interpolate_at_i(trail_i)
if uncover:
last_point = self.interpolate_at_i(i)
else:
first_point = np.nan
last_point = np.nan
if uncover and trail is not False:
concat = (np.array([first_point]),
value[filter_],
np.array([last_point]))
elif uncover:
concat = (value[filter_],
np.array([last_point]))
elif trail:
concat = (np.array([first_point]),
value[filter_])
else:
return self._to_unit(value[filter_], unit)
return self._to_unit(np.concatenate(concat), unit)
else:
# then we need to "select" based on the indep and the value
if isinstance(self.call, Plot):
return self._to_unit(value[filter_].T, unit)
else:
return self._to_unit(value[filter_], unit)
# for value we need to define the property without decorators because of
# this: https://stackoverflow.com/questions/13595607/using-super-in-a-propertys-setter-method-when-using-the-property-decorator-r
# and the need to override these in the CallDimensionI class
def _get_value(self):
"""
access the value
"""
return self.get_value(i=None, unit=None)
def _set_value(self, value):
"""
set the value
"""
if value is None:
self._value = value
return
# handle casting to acceptable types
if isinstance(value, list) or isinstance(value, tuple):
value = np.asarray(value)
elif isinstance(value, int):
value = float(value)
if isinstance(value, u.Quantity):
if self.unit == u.dimensionless_unscaled:
# then take the unit from quantity and apply it
self.unit = value.unit
value = value.value
else:
# then convert to the requested unit
value = value.to(self.unit).value
# handle setting based on type
if isinstance(value, np.ndarray):
# if len(value.shape) != 1:
# raise ValueError("value must be a flat array")
self._value = value
elif isinstance(value, float):
# TODO: do we want to cast to np.array([value])??
# this will most likely be used for axhline/axvline
self._value = value
elif self.direction=='c' and isinstance(value, str):
self._value = common.coloralias.map(value)
else:
raise TypeError("value must be of type array (or similar), found {} {}".format(type(value), value))
value = property(_get_value, _set_value)
def get_error(self, i=None, unit=None,
uncover=None, trail=None,
linebreak=None, sort_by_indep=None):
"""
access the error for a given value of i (independent-variable) depending
on which effects (i.e. uncover) are enabled.
"""
return self.get_value(i=i, unit=unit,
uncover=uncover, trail=trail,
linebreak=linebreak, sort_by_indep=sort_by_indep,
attr='_error')
@property
def error(self):
"""
access the error
"""
return self._error
@error.setter
def error(self, error):
"""
set the error
"""
# TODO: check length with value?
# TODO: type checks (similar to value)
if self.direction not in ['x', 'y', 'z'] and error is not None:
raise ValueError("error only accepted for x, y, z dimensions")
if isinstance(error, u.Quantity):
error = error.to(self.unit).value
if isinstance(error, list) or isinstance(error, tuple):
error = np.asarray(error)
self._error = error
@property
def unit(self):
"""
access the unit
"""
return self._unit
@unit.setter
def unit(self, unit):
"""
set the unit
"""
unit = common._convert_unit(unit)
self._unit = unit
@property
def label(self):
"""
access the label
"""
return self._label
@label.setter
def label(self, label):
"""
set the label
"""
if self.direction in ['i'] and label is not None:
raise ValueError("label not accepted for indep dimension")
if label is None:
self._label = label
return
if not isinstance(label, str):
try:
label = str(label)
except:
raise TypeError("label must be of type str")
self._label = label
@property
def normals(self):
"""
access the normals
"""
return self._normals
@normals.setter
def normals(self, normals):
"""
set the normals
"""
if self.direction not in ['x', 'y', 'z'] and normals is not None:
raise ValueError("normals only accepted for x, y, z dimensions")
if normals is None:
self._normals = None
return
if not (isinstance(normals, list) or isinstance(normals, np.ndarray)):
raise TypeError("normals must be of type list or array")
self._normals = normals
class CallDimensionI(CallDimension):
def __init__(self, call, value, unit, tol):
if isinstance(value, dict):
tol = value.get('tol', tol)
self.tol = tol
super(CallDimensionI, self).__init__('i', call, value, unit)
@classmethod
def from_dict(cls, dict):
return cls(**dict)
def to_dict(self):
return {'direction': self.direction,
'unit': self.unit.to_string(),
'value': common.arraytolistrecursive(self._value),
'tol': self._tol}
@property
def tol(self):
"""
Returns
-----------
* (float) tolerance to use when selecting/uncover/trail
"""
if self._tol is None:
return 0.0
return self._tol
@tol.setter
def tol(self, tol):
if not isinstance(tol, float):
raise TypeError("tol must be of type float")
# TODO: handle units?
self._tol = tol
@property
def value(self):
"""
access the value
"""
if isinstance(self._value, str):
dimension = self._value
return getattr(self.call, dimension).value
return super(CallDimensionI, self)._get_value()
@value.setter
def value(self, value):
"""
set the value
"""
# for the indep direction we also allow a string which points to one
# of the other available dimensions
# TODO: support c, fc, ec?
if isinstance(value, common.basestring) and value in ['x', 'y', 'z']:
# we'll cast just to get rid of any python2 unicodes
self._value = str(value)
dimension = value
self._unit = getattr(self.call, dimension).unit
return
# NOTE: cannot do super on setter directly, see this python
# bug: https://bugs.python.org/issue14965 and discussion:
# https://mail.python.org/pipermail/python-dev/2010-April/099672.html
super(CallDimensionI, self)._set_value(value)
def get_value(self, *args, **kwargs):
if isinstance(self._value, str):
dimension = self._value
return getattr(self.call, dimension).get_value(*args, **kwargs)
return super(CallDimensionI, self).get_value(*args, **kwargs)
@property
def is_reference(self):
"""
whether referencing another dimension or its own
"""
return isinstance(self._value, str)
@property
def reference(self):
"""
reference (will return None if not is_reference)
"""
if self.is_reference:
return self._value
else:
return None
class CallDimensionX(CallDimension):
def __init__(self, *args):
super(CallDimensionX, self).__init__('x', *args)
class CallDimensionY(CallDimension):
def __init__(self, *args):
super(CallDimensionY, self).__init__('y', *args)
class CallDimensionZ(CallDimension):
def __init__(self, *args):
super(CallDimensionZ, self).__init__('z', *args)
class CallDimensionS(CallDimension):
def __init__(self, call, value, error=None, unit=None, label=None,
smap=None, mode=None):
if isinstance(value, dict):
error = value.get('error', error)
smap = value.get('smap', smap)
mode = value.get('mode', mode)
if error is not None:
raise ValueError("error not supported for 's' dimension")
self.smap = smap
self.mode = mode
super(CallDimensionS, self).__init__('s', call, value, error, unit,
label)
@classmethod
def from_dict(cls, dict):
return cls(**dict)
def to_dict(self):
return {'direction': self.direction,
'unit': self.unit.to_string(),
'value': common.arraytolistrecursive(self._value),
'error': common.arraytolistrecursive(self._error),
'label': self._label,
'smap': self._smap,
'mode': self._mode}
@property
def smap(self):
return self._smap
@smap.setter
def smap(self, smap):
if smap is None:
self._smap = smap
return
if not isinstance(smap, tuple):
try:
smap = tuple(smap)
except:
raise TypeError('smap must be of type tuple')
if not len(smap)==2:
raise ValueError('smap must have length 2')
self._smap = smap
def _mode_split(self, mode=None):
if mode is None:
mode = self.mode
split = mode.split(':')
mode_dims = split[0]
mode_obj = split[1] if len(split) > 1 else 'axes'
mode_mode = split[2] if len(split) > 2 else 'fixed'
return mode_dims, mode_obj, mode_mode
@property
def mode(self):
if self._mode is None:
return 'xy:figure:fixed'
return self._mode
@mode.setter
def mode(self, mode):
if mode is None:
self._mode = None
return
if not isinstance(mode, str):
raise TypeError("mode must be of type str")
split = mode.split(':')
mode_dims, mode_obj, mode_mode = self._mode_split(mode)
if len(split) > 3:
raise ValueError("mode not recognized")
if mode_dims == 'pt' and len(split) > 1:
raise ValueError("mode not recognized")
if mode_dims not in ['x', 'y', 'xy', 'pt']:
raise ValueError("mode not recognized")
if mode_obj not in ['axes', 'figure']:
raise ValueError("mode not recognized")
if mode_mode not in ['fixed', 'current', 'original']:
raise ValueError("mode not recognized")
if mode_dims == 'pt':
self._mode = mode
else:
self._mode = '{}:{}:{}'.format(mode_dims, mode_obj, mode_mode)
class CallDimensionC(CallDimension):
def __init__(self, call, value, error=None, unit=None, label=None, cmap=None):
if isinstance(value, dict):
error = value.get('error', error)
cmap = value.get('cmap', cmap)
if error is not None:
raise ValueError("error not supported for 'c' dimension")
self.cmap = cmap
super(CallDimensionC, self).__init__('c', call, value, error, unit,
label)
@classmethod
def from_dict(cls, dict):
return cls(**dict)
def to_dict(self):
return {'direction': self.direction,
'unit': self.unit.to_string(),
'value': common.arraytolistrecursive(self._value),
'error': common.arraytolistrecursive(self._error),
'label': self._label,
'cmap': self._cmap}
@property
def cmap(self):
return self._cmap
@cmap.setter
def cmap(self, cmap):
# print("setting call cmap: {}".format(cmap))
try:
cmap_ = plt.get_cmap(cmap)
except:
raise TypeError("could not find cmap")
self._cmap = cmap
| gpl-3.0 |
CforED/Machine-Learning | sklearn/gaussian_process/tests/test_kernels.py | 23 | 11813 | """Testing for kernels for Gaussian processes."""
# Author: Jan Hendrik Metzen <[email protected]>
# Licence: BSD 3 clause
from collections import Hashable
from sklearn.externals.funcsigs import signature
import numpy as np
from scipy.optimize import approx_fprime
from sklearn.metrics.pairwise \
import PAIRWISE_KERNEL_FUNCTIONS, euclidean_distances, pairwise_kernels
from sklearn.gaussian_process.kernels \
import (RBF, Matern, RationalQuadratic, ExpSineSquared, DotProduct,
ConstantKernel, WhiteKernel, PairwiseKernel, KernelOperator,
Exponentiation)
from sklearn.base import clone
from sklearn.utils.testing import (assert_equal, assert_almost_equal,
assert_not_equal, assert_array_equal,
assert_array_almost_equal)
X = np.random.RandomState(0).normal(0, 1, (10, 2))
Y = np.random.RandomState(0).normal(0, 1, (11, 2))
kernel_white = RBF(length_scale=2.0) + WhiteKernel(noise_level=3.0)
kernels = [RBF(length_scale=2.0), RBF(length_scale_bounds=(0.5, 2.0)),
ConstantKernel(constant_value=10.0),
2.0 * RBF(length_scale=0.33, length_scale_bounds="fixed"),
2.0 * RBF(length_scale=0.5), kernel_white,
2.0 * RBF(length_scale=[0.5, 2.0]),
2.0 * Matern(length_scale=0.33, length_scale_bounds="fixed"),
2.0 * Matern(length_scale=0.5, nu=0.5),
2.0 * Matern(length_scale=1.5, nu=1.5),
2.0 * Matern(length_scale=2.5, nu=2.5),
2.0 * Matern(length_scale=[0.5, 2.0], nu=0.5),
3.0 * Matern(length_scale=[2.0, 0.5], nu=1.5),
4.0 * Matern(length_scale=[0.5, 0.5], nu=2.5),
RationalQuadratic(length_scale=0.5, alpha=1.5),
ExpSineSquared(length_scale=0.5, periodicity=1.5),
DotProduct(sigma_0=2.0), DotProduct(sigma_0=2.0) ** 2]
for metric in PAIRWISE_KERNEL_FUNCTIONS:
if metric in ["additive_chi2", "chi2"]:
continue
kernels.append(PairwiseKernel(gamma=1.0, metric=metric))
def test_kernel_gradient():
""" Compare analytic and numeric gradient of kernels. """
for kernel in kernels:
K, K_gradient = kernel(X, eval_gradient=True)
assert_equal(K_gradient.shape[0], X.shape[0])
assert_equal(K_gradient.shape[1], X.shape[0])
assert_equal(K_gradient.shape[2], kernel.theta.shape[0])
K_gradient_approx = np.empty_like(K_gradient)
for i in range(K.shape[0]):
for j in range(K.shape[1]):
def eval_kernel_ij_for_theta(theta):
kernel_clone = kernel.clone_with_theta(theta)
K = kernel_clone(X, eval_gradient=False)
return K[i, j]
K_gradient_approx[i, j] = \
approx_fprime(kernel.theta, eval_kernel_ij_for_theta,
1e-10)
assert_almost_equal(K_gradient, K_gradient_approx, 4)
def test_kernel_theta():
""" Check that parameter vector theta of kernel is set correctly. """
for kernel in kernels:
if isinstance(kernel, KernelOperator) \
or isinstance(kernel, Exponentiation): # skip non-basic kernels
continue
theta = kernel.theta
_, K_gradient = kernel(X, eval_gradient=True)
# Determine kernel parameters that contribute to theta
init_sign = signature(kernel.__class__.__init__).parameters.values()
args = [p.name for p in init_sign if p.name != 'self']
theta_vars = map(lambda s: s.rstrip("_bounds"),
filter(lambda s: s.endswith("_bounds"), args))
assert_equal(
set(hyperparameter.name
for hyperparameter in kernel.hyperparameters),
set(theta_vars))
# Check that values returned in theta are consistent with
# hyperparameter values (being their logarithms)
for i, hyperparameter in enumerate(kernel.hyperparameters):
assert_equal(theta[i],
np.log(getattr(kernel, hyperparameter.name)))
# Fixed kernel parameters must be excluded from theta and gradient.
for i, hyperparameter in enumerate(kernel.hyperparameters):
# create copy with certain hyperparameter fixed
params = kernel.get_params()
params[hyperparameter.name + "_bounds"] = "fixed"
kernel_class = kernel.__class__
new_kernel = kernel_class(**params)
# Check that theta and K_gradient are identical with the fixed
# dimension left out
_, K_gradient_new = new_kernel(X, eval_gradient=True)
assert_equal(theta.shape[0], new_kernel.theta.shape[0] + 1)
assert_equal(K_gradient.shape[2], K_gradient_new.shape[2] + 1)
if i > 0:
assert_equal(theta[:i], new_kernel.theta[:i])
assert_array_equal(K_gradient[..., :i],
K_gradient_new[..., :i])
if i + 1 < len(kernel.hyperparameters):
assert_equal(theta[i+1:], new_kernel.theta[i:])
assert_array_equal(K_gradient[..., i+1:],
K_gradient_new[..., i:])
# Check that values of theta are modified correctly
for i, hyperparameter in enumerate(kernel.hyperparameters):
theta[i] = np.log(42)
kernel.theta = theta
assert_almost_equal(getattr(kernel, hyperparameter.name), 42)
setattr(kernel, hyperparameter.name, 43)
assert_almost_equal(kernel.theta[i], np.log(43))
def test_auto_vs_cross():
""" Auto-correlation and cross-correlation should be consistent. """
for kernel in kernels:
if kernel == kernel_white:
continue # Identity is not satisfied on diagonal
K_auto = kernel(X)
K_cross = kernel(X, X)
assert_almost_equal(K_auto, K_cross, 5)
def test_kernel_diag():
""" Test that diag method of kernel returns consistent results. """
for kernel in kernels:
K_call_diag = np.diag(kernel(X))
K_diag = kernel.diag(X)
assert_almost_equal(K_call_diag, K_diag, 5)
def test_kernel_operator_commutative():
""" Adding kernels and multiplying kernels should be commutative. """
# Check addition
assert_almost_equal((RBF(2.0) + 1.0)(X),
(1.0 + RBF(2.0))(X))
# Check multiplication
assert_almost_equal((3.0 * RBF(2.0))(X),
(RBF(2.0) * 3.0)(X))
def test_kernel_anisotropic():
""" Anisotropic kernel should be consistent with isotropic kernels."""
kernel = 3.0 * RBF([0.5, 2.0])
K = kernel(X)
X1 = np.array(X)
X1[:, 0] *= 4
K1 = 3.0 * RBF(2.0)(X1)
assert_almost_equal(K, K1)
X2 = np.array(X)
X2[:, 1] /= 4
K2 = 3.0 * RBF(0.5)(X2)
assert_almost_equal(K, K2)
# Check getting and setting via theta
kernel.theta = kernel.theta + np.log(2)
assert_array_equal(kernel.theta, np.log([6.0, 1.0, 4.0]))
assert_array_equal(kernel.k2.length_scale, [1.0, 4.0])
def test_kernel_stationary():
""" Test stationarity of kernels."""
for kernel in kernels:
if not kernel.is_stationary():
continue
K = kernel(X, X + 1)
assert_almost_equal(K[0, 0], np.diag(K))
def test_kernel_clone():
""" Test that sklearn's clone works correctly on kernels. """
for kernel in kernels:
kernel_cloned = clone(kernel)
assert_equal(kernel, kernel_cloned)
assert_not_equal(id(kernel), id(kernel_cloned))
for attr in kernel.__dict__.keys():
attr_value = getattr(kernel, attr)
attr_value_cloned = getattr(kernel_cloned, attr)
if attr.startswith("hyperparameter_"):
assert_equal(attr_value.name, attr_value_cloned.name)
assert_equal(attr_value.value_type,
attr_value_cloned.value_type)
assert_array_equal(attr_value.bounds,
attr_value_cloned.bounds)
assert_equal(attr_value.n_elements,
attr_value_cloned.n_elements)
elif np.iterable(attr_value):
for i in range(len(attr_value)):
if np.iterable(attr_value[i]):
assert_array_equal(attr_value[i],
attr_value_cloned[i])
else:
assert_equal(attr_value[i], attr_value_cloned[i])
else:
assert_equal(attr_value, attr_value_cloned)
if not isinstance(attr_value, Hashable):
# modifiable attributes must not be identical
assert_not_equal(id(attr_value), id(attr_value_cloned))
def test_matern_kernel():
""" Test consistency of Matern kernel for special values of nu. """
K = Matern(nu=1.5, length_scale=1.0)(X)
# the diagonal elements of a matern kernel are 1
assert_array_almost_equal(np.diag(K), np.ones(X.shape[0]))
# matern kernel for coef0==0.5 is equal to absolute exponential kernel
K_absexp = np.exp(-euclidean_distances(X, X, squared=False))
K = Matern(nu=0.5, length_scale=1.0)(X)
assert_array_almost_equal(K, K_absexp)
# test that special cases of matern kernel (coef0 in [0.5, 1.5, 2.5])
# result in nearly identical results as the general case for coef0 in
# [0.5 + tiny, 1.5 + tiny, 2.5 + tiny]
tiny = 1e-10
for nu in [0.5, 1.5, 2.5]:
K1 = Matern(nu=nu, length_scale=1.0)(X)
K2 = Matern(nu=nu + tiny, length_scale=1.0)(X)
assert_array_almost_equal(K1, K2)
def test_kernel_versus_pairwise():
"""Check that GP kernels can also be used as pairwise kernels."""
for kernel in kernels:
# Test auto-kernel
if kernel != kernel_white:
# For WhiteKernel: k(X) != k(X,X). This is assumed by
# pairwise_kernels
K1 = kernel(X)
K2 = pairwise_kernels(X, metric=kernel)
assert_array_almost_equal(K1, K2)
# Test cross-kernel
K1 = kernel(X, Y)
K2 = pairwise_kernels(X, Y, metric=kernel)
assert_array_almost_equal(K1, K2)
def test_set_get_params():
"""Check that set_params()/get_params() is consistent with kernel.theta."""
for kernel in kernels:
# Test get_params()
index = 0
params = kernel.get_params()
for hyperparameter in kernel.hyperparameters:
if hyperparameter.bounds is "fixed":
continue
size = hyperparameter.n_elements
if size > 1: # anisotropic kernels
assert_almost_equal(np.exp(kernel.theta[index:index+size]),
params[hyperparameter.name])
index += size
else:
assert_almost_equal(np.exp(kernel.theta[index]),
params[hyperparameter.name])
index += 1
# Test set_params()
index = 0
value = 10 # arbitrary value
for hyperparameter in kernel.hyperparameters:
if hyperparameter.bounds is "fixed":
continue
size = hyperparameter.n_elements
if size > 1: # anisotropic kernels
kernel.set_params(**{hyperparameter.name: [value]*size})
assert_almost_equal(np.exp(kernel.theta[index:index+size]),
[value]*size)
index += size
else:
kernel.set_params(**{hyperparameter.name: value})
assert_almost_equal(np.exp(kernel.theta[index]), value)
index += 1
| bsd-3-clause |
brandsoulmates/incubator-airflow | airflow/contrib/plugins/metastore_browser/main.py | 62 | 5773 | # -*- coding: utf-8 -*-
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from datetime import datetime
import json
from flask import Blueprint, request
from flask_admin import BaseView, expose
import pandas as pd
from airflow.hooks.hive_hooks import HiveMetastoreHook, HiveCliHook
from airflow.hooks.mysql_hook import MySqlHook
from airflow.hooks.presto_hook import PrestoHook
from airflow.plugins_manager import AirflowPlugin
from airflow.www import utils as wwwutils
METASTORE_CONN_ID = 'metastore_default'
METASTORE_MYSQL_CONN_ID = 'metastore_mysql'
PRESTO_CONN_ID = 'presto_default'
HIVE_CLI_CONN_ID = 'hive_default'
DEFAULT_DB = 'default'
DB_WHITELIST = None
DB_BLACKLIST = ['tmp']
TABLE_SELECTOR_LIMIT = 2000
# Keeping pandas from truncating long strings
pd.set_option('display.max_colwidth', -1)
# Creating a flask admin BaseView
class MetastoreBrowserView(BaseView, wwwutils.DataProfilingMixin):
@expose('/')
def index(self):
sql = """
SELECT
a.name as db, db_location_uri as location,
count(1) as object_count, a.desc as description
FROM DBS a
JOIN TBLS b ON a.DB_ID = b.DB_ID
GROUP BY a.name, db_location_uri, a.desc
""".format(**locals())
h = MySqlHook(METASTORE_MYSQL_CONN_ID)
df = h.get_pandas_df(sql)
df.db = (
'<a href="/admin/metastorebrowserview/db/?db=' +
df.db + '">' + df.db + '</a>')
table = df.to_html(
classes="table table-striped table-bordered table-hover",
index=False,
escape=False,
na_rep='',)
return self.render(
"metastore_browser/dbs.html", table=table)
@expose('/table/')
def table(self):
table_name = request.args.get("table")
m = HiveMetastoreHook(METASTORE_CONN_ID)
table = m.get_table(table_name)
return self.render(
"metastore_browser/table.html",
table=table, table_name=table_name, datetime=datetime, int=int)
@expose('/db/')
def db(self):
db = request.args.get("db")
m = HiveMetastoreHook(METASTORE_CONN_ID)
tables = sorted(m.get_tables(db=db), key=lambda x: x.tableName)
return self.render(
"metastore_browser/db.html", tables=tables, db=db)
@wwwutils.gzipped
@expose('/partitions/')
def partitions(self):
schema, table = request.args.get("table").split('.')
sql = """
SELECT
a.PART_NAME,
a.CREATE_TIME,
c.LOCATION,
c.IS_COMPRESSED,
c.INPUT_FORMAT,
c.OUTPUT_FORMAT
FROM PARTITIONS a
JOIN TBLS b ON a.TBL_ID = b.TBL_ID
JOIN DBS d ON b.DB_ID = d.DB_ID
JOIN SDS c ON a.SD_ID = c.SD_ID
WHERE
b.TBL_NAME like '{table}' AND
d.NAME like '{schema}'
ORDER BY PART_NAME DESC
""".format(**locals())
h = MySqlHook(METASTORE_MYSQL_CONN_ID)
df = h.get_pandas_df(sql)
return df.to_html(
classes="table table-striped table-bordered table-hover",
index=False,
na_rep='',)
@wwwutils.gzipped
@expose('/objects/')
def objects(self):
where_clause = ''
if DB_WHITELIST:
dbs = ",".join(["'" + db + "'" for db in DB_WHITELIST])
where_clause = "AND b.name IN ({})".format(dbs)
if DB_BLACKLIST:
dbs = ",".join(["'" + db + "'" for db in DB_BLACKLIST])
where_clause = "AND b.name NOT IN ({})".format(dbs)
sql = """
SELECT CONCAT(b.NAME, '.', a.TBL_NAME), TBL_TYPE
FROM TBLS a
JOIN DBS b ON a.DB_ID = b.DB_ID
WHERE
a.TBL_NAME NOT LIKE '%tmp%' AND
a.TBL_NAME NOT LIKE '%temp%' AND
b.NAME NOT LIKE '%tmp%' AND
b.NAME NOT LIKE '%temp%'
{where_clause}
LIMIT {LIMIT};
""".format(where_clause=where_clause, LIMIT=TABLE_SELECTOR_LIMIT)
h = MySqlHook(METASTORE_MYSQL_CONN_ID)
d = [
{'id': row[0], 'text': row[0]}
for row in h.get_records(sql)]
return json.dumps(d)
@wwwutils.gzipped
@expose('/data/')
def data(self):
table = request.args.get("table")
sql = "SELECT * FROM {table} LIMIT 1000;".format(table=table)
h = PrestoHook(PRESTO_CONN_ID)
df = h.get_pandas_df(sql)
return df.to_html(
classes="table table-striped table-bordered table-hover",
index=False,
na_rep='',)
@expose('/ddl/')
def ddl(self):
table = request.args.get("table")
sql = "SHOW CREATE TABLE {table};".format(table=table)
h = HiveCliHook(HIVE_CLI_CONN_ID)
return h.run_cli(sql)
v = MetastoreBrowserView(category="Plugins", name="Hive Metadata Browser")
# Creating a flask blueprint to intergrate the templates and static folder
bp = Blueprint(
"metastore_browser", __name__,
template_folder='templates',
static_folder='static',
static_url_path='/static/metastore_browser')
# Defining the plugin class
class MetastoreBrowserPlugin(AirflowPlugin):
name = "metastore_browser"
flask_blueprints = [bp]
admin_views = [v]
| apache-2.0 |
AartGoossens/wblib | wblib/models.py | 1 | 6916 | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from pandas.core.accessor import AccessorProperty
import pandas.plotting._core as gfx
from .client import WattbikeHubClient
from .tools import polar_force_column_labels, flatten
class WattbikeFramePlotMethods(gfx.FramePlotMethods):
polar_angles = np.arange(90, 451) / (180 / np.pi)
polar_force_columns = polar_force_column_labels()
def _plot_single_polar(self, ax, polar_forces, mean, *args, **kwargs):
if 'linewidth' in kwargs:
linewidth = kwargs.pop('linewidth')
elif mean:
linewidth = 3
else:
linewidth = 0.5
ax.plot(self.polar_angles, polar_forces, linewidth=linewidth, *args, **kwargs)
def polar(self, full=False, mean=True, *args, **kwargs):
ax = plt.subplot(111, projection='polar')
if full:
for i in range(0, len(self._data) - 50, 50):
forces = self._data.iloc[i:i + 50, self._data.columns.get_indexer(self.polar_force_columns)].mean()
self._plot_single_polar(ax, forces, mean=False, *args, **kwargs)
if mean:
forces = self._data[self.polar_force_columns].mean()
self._plot_single_polar(ax, forces, mean=True, *args, **kwargs)
xticks_num = 8
xticks = np.arange(0, xticks_num, 2 * np.pi / xticks_num)
ax.set_xticks(xticks)
rad_to_label = lambda i: '{}°'.format(int(i / (2 * np.pi) * 360 - 90) % 180)
ax.set_xticklabels([rad_to_label(i) for i in xticks])
ax.set_yticklabels([])
return ax
class WattbikeDataFrame(pd.DataFrame):
@property
def _constructor(self):
return WattbikeDataFrame
def load(self, session_id):
client = WattbikeHubClient()
if not isinstance(session_id, list):
session_id = [session_id]
for session in session_id:
session_data, ride_session = client.get_session(session)
wdf = self._raw_session_to_wdf(session_data, ride_session)
self = self.append(wdf)
return self
def load_for_user(self, user_id, before=None, after=None):
client = WattbikeHubClient()
if not isinstance(user_id, list):
user_id = [user_id]
for ID in user_id:
sessions = client.get_sessions_for_user(
user_id=ID, before=before, after=after
)
for session_data, ride_session in sessions:
wdf = self._raw_session_to_wdf(session_data, ride_session)
self = self.append(wdf)
return self
def _raw_session_to_wdf(self, session_data, ride_session):
wdf = WattbikeDataFrame(
[flatten(rev) for lap in session_data['laps'] for rev in lap['data']])
wdf['time'] = wdf.time.cumsum()
wdf['user_id'] = ride_session.get_user_id()
wdf['session_id'] = ride_session.get_session_id()
self._process(wdf)
return wdf
def _process(self, wdf):
wdf = wdf._columns_to_numeric()
wdf = wdf._add_polar_forces()
wdf = wdf._add_min_max_angles()
wdf = self._enrich_with_athlete_performance_state(wdf)
return wdf
def _columns_to_numeric(self):
for col in self.columns:
try:
self.iloc[:, self.columns.get_loc(col)] = pd.to_numeric(self.iloc[:, self.columns.get_loc(col)])
except ValueError:
continue
return self
def _add_polar_forces(self):
_df = pd.DataFrame()
new_angles = np.arange(0.0, 361.0)
column_labels = polar_force_column_labels()
if not '_0' in self.columns:
for label in column_labels:
self[label] = np.nan
for index, pf in self.polar_force.iteritems():
if not isinstance(pf, str):
continue
forces = [int(i) for i in pf.split(',')]
forces = np.array(forces + [forces[0]])
forces = forces/np.mean(forces)
angle_dx = 360.0 / (len(forces)-1)
forces_interp = np.interp(
x=new_angles,
xp=np.arange(0, 360.01, angle_dx),
fp=forces)
_df[index] = forces_interp
_df['angle'] = column_labels
_df.set_index('angle', inplace=True)
_df = _df.transpose()
for angle in column_labels:
self[angle] = _df[angle]
return self
def _add_min_max_angles(self):
# @TODO this method is quite memory inefficient. Row by row calculation is better
pf_columns = polar_force_column_labels()
pf_T = self.loc[:, pf_columns].transpose().reset_index(drop=True)
left_max_angle = pf_T.iloc[:180].idxmax()
right_max_angle = pf_T.iloc[180:].idxmax() - 180
left_min_angle = pd.concat([pf_T.iloc[:135], pf_T.iloc[315:]]).idxmin()
right_min_angle = pf_T.iloc[135:315].idxmin() - 180
self['left_max_angle'] = pd.DataFrame(left_max_angle)
self['right_max_angle'] = pd.DataFrame(right_max_angle)
self['left_min_angle'] = pd.DataFrame(left_min_angle)
self['right_min_angle'] = pd.DataFrame(right_min_angle)
return self
def _enrich_with_athlete_performance_state(self, wdf):
if not hasattr(self, 'peformance_states'):
self.performance_states = {}
percentage_of_mhr = []
percentage_of_mmp = []
percentage_of_ftp = []
for index, row in wdf.iterrows():
if row.user_id not in self.performance_states:
self.performance_states[row.user_id] = \
WattbikeHubClient().get_user_performance_state(row.user_id)
ps = self.performance_states[row.user_id]
percentage_of_mmp.append(row.power/ps.get_max_minute_power())
percentage_of_ftp.append(row.power/ps.get_ftp())
try:
percentage_of_mhr.append(row.heartrate/ps.get_max_hr())
except AttributeError:
percentage_of_mhr.append(np.nan)
wdf['percentage_of_mhr'] = percentage_of_mhr
wdf['percentage_of_mmp'] = percentage_of_mmp
wdf['percentage_of_ftp'] = percentage_of_ftp
return wdf
def average_by_session(self):
averaged = self._average_by_column('session_id')
averaged['user_id'] = averaged.session_id.apply(
lambda x: self.loc[self.session_id == x].iloc[0].user_id)
return averaged
def average_by_user(self):
return self._average_by_column('user_id')
def _average_by_column(self, column_name):
averaged_self = self.groupby(column_name).mean().reset_index()
return WattbikeDataFrame(averaged_self)
WattbikeDataFrame.plot = AccessorProperty(WattbikeFramePlotMethods,
WattbikeFramePlotMethods)
| mit |
thilbern/scikit-learn | sklearn/utils/validation.py | 11 | 13372 | """Utilities for input validation"""
# Authors: Olivier Grisel
# Gael Varoquaux
# Andreas Mueller
# Lars Buitinck
# Alexandre Gramfort
# Nicolas Tresegnie
# License: BSD 3 clause
import warnings
import numbers
import numpy as np
import scipy.sparse as sp
from ..externals import six
from inspect import getargspec
class DataConversionWarning(UserWarning):
"A warning on implicit data conversions happening in the code"
pass
warnings.simplefilter("always", DataConversionWarning)
class NonBLASDotWarning(UserWarning):
"A warning on implicit dispatch to numpy.dot"
pass
# Silenced by default to reduce verbosity. Turn on at runtime for
# performance profiling.
warnings.simplefilter('ignore', NonBLASDotWarning)
def _assert_all_finite(X):
"""Like assert_all_finite, but only for ndarray."""
X = np.asanyarray(X)
# First try an O(n) time, O(1) space solution for the common case that
# everything is finite; fall back to O(n) space np.isfinite to prevent
# false positives from overflow in sum method.
if (X.dtype.char in np.typecodes['AllFloat'] and not np.isfinite(X.sum())
and not np.isfinite(X).all()):
raise ValueError("Input contains NaN, infinity"
" or a value too large for %r." % X.dtype)
def assert_all_finite(X):
"""Throw a ValueError if X contains NaN or infinity.
Input MUST be an np.ndarray instance or a scipy.sparse matrix."""
_assert_all_finite(X.data if sp.issparse(X) else X)
def as_float_array(X, copy=True, force_all_finite=True):
"""Converts an array-like to an array of floats
The new dtype will be np.float32 or np.float64, depending on the original
type. The function can create a copy or modify the argument depending
on the argument copy.
Parameters
----------
X : {array-like, sparse matrix}
copy : bool, optional
If True, a copy of X will be created. If False, a copy may still be
returned if X's dtype is not a floating point type.
Returns
-------
XT : {array, sparse matrix}
An array of type np.float
"""
if isinstance(X, np.matrix) or (not isinstance(X, np.ndarray)
and not sp.issparse(X)):
return check_array(X, ['csr', 'csc', 'coo'], dtype=np.float64,
copy=copy, force_all_finite=force_all_finite,
ensure_2d=False)
elif sp.issparse(X) and X.dtype in [np.float32, np.float64]:
return X.copy() if copy else X
elif X.dtype in [np.float32, np.float64]: # is numpy array
return X.copy('F' if X.flags['F_CONTIGUOUS'] else 'C') if copy else X
else:
return X.astype(np.float32 if X.dtype == np.int32 else np.float64)
def _num_samples(x):
"""Return number of samples in array-like x."""
if not hasattr(x, '__len__') and not hasattr(x, 'shape'):
if hasattr(x, '__array__'):
x = np.asarray(x)
else:
raise TypeError("Expected sequence or array-like, got %r" % x)
return x.shape[0] if hasattr(x, 'shape') else len(x)
def check_consistent_length(*arrays):
"""Check that all arrays have consistent first dimensions.
Checks whether all objects in arrays have the same shape or length.
Parameters
----------
arrays : list or tuple of input objects.
Objects that will be checked for consistent length.
"""
uniques = np.unique([_num_samples(X) for X in arrays if X is not None])
if len(uniques) > 1:
raise ValueError("Found arrays with inconsistent numbers of samples: %s"
% str(uniques))
def indexable(*iterables):
"""Make arrays indexable for cross-validation.
Checks consistent length, passes through None, and ensures that everything
can be indexed by converting sparse matrices to csr and converting
non-interable objects to arrays.
Parameters
----------
iterables : lists, dataframes, arrays, sparse matrices
List of objects to ensure sliceability.
"""
result = []
for X in iterables:
if sp.issparse(X):
result.append(X.tocsr())
elif hasattr(X, "__getitem__") or hasattr(X, "iloc"):
result.append(X)
elif X is None:
result.append(X)
else:
result.append(np.array(X))
check_consistent_length(*result)
return result
def _ensure_sparse_format(spmatrix, accept_sparse, dtype, order, copy,
force_all_finite):
"""Convert a sparse matrix to a given format.
Checks the sparse format of spmatrix and converts if necessary.
Parameters
----------
spmatrix : scipy sparse matrix
Input to validate and convert.
accept_sparse : string, list of string or None (default=None)
String[s] representing allowed sparse matrix formats ('csc',
'csr', 'coo', 'dok', 'bsr', 'lil', 'dia'). None means that sparse
matrix input will raise an error. If the input is sparse but not in
the allowed format, it will be converted to the first listed format.
dtype : string, type or None (default=none)
Data type of result. If None, the dtype of the input is preserved.
order : 'F', 'C' or None (default=None)
Whether an array will be forced to be fortran or c-style.
copy : boolean (default=False)
Whether a forced copy will be triggered. If copy=False, a copy might
be triggered by a conversion.
force_all_finite : boolean (default=True)
Whether to raise an error on np.inf and np.nan in X.
Returns
-------
spmatrix_converted : scipy sparse matrix.
Matrix that is ensured to have an allowed type.
"""
if accept_sparse is None:
raise TypeError('A sparse matrix was passed, but dense '
'data is required. Use X.toarray() to '
'convert to a dense numpy array.')
sparse_type = spmatrix.format
if dtype is None:
dtype = spmatrix.dtype
if sparse_type in accept_sparse:
# correct type
if dtype == spmatrix.dtype:
# correct dtype
if copy:
spmatrix = spmatrix.copy()
else:
# convert dtype
spmatrix = spmatrix.astype(dtype)
else:
# create new
spmatrix = spmatrix.asformat(accept_sparse[0]).astype(dtype)
if force_all_finite:
if not hasattr(spmatrix, "data"):
warnings.warn("Can't check %s sparse matrix for nan or inf."
% spmatrix.format)
else:
_assert_all_finite(spmatrix.data)
if hasattr(spmatrix, "data"):
spmatrix.data = np.array(spmatrix.data, copy=False, order=order)
return spmatrix
def check_array(array, accept_sparse=None, dtype=None, order=None, copy=False,
force_all_finite=True, ensure_2d=True, allow_nd=False):
"""Input validation on an array, list, sparse matrix or similar.
By default, the input is converted to an at least 2nd numpy array.
Parameters
----------
array : object
Input object to check / convert.
accept_sparse : string, list of string or None (default=None)
String[s] representing allowed sparse matrix formats, such as 'csc',
'csr', etc. None means that sparse matrix input will raise an error.
If the input is sparse but not in the allowed format, it will be
converted to the first listed format.
dtype : string, type or None (default=none)
Data type of result. If None, the dtype of the input is preserved.
order : 'F', 'C' or None (default=None)
Whether an array will be forced to be fortran or c-style.
copy : boolean (default=False)
Whether a forced copy will be triggered. If copy=False, a copy might
be triggered by a conversion.
force_all_finite : boolean (default=True)
Whether to raise an error on np.inf and np.nan in X.
ensure_2d : boolean (default=True)
Whether to make X at least 2d.
allow_nd : boolean (default=False)
Whether to allow X.ndim > 2.
Returns
-------
X_converted : object
The converted and validated X.
"""
if isinstance(accept_sparse, str):
accept_sparse = [accept_sparse]
if sp.issparse(array):
array = _ensure_sparse_format(array, accept_sparse, dtype, order,
copy, force_all_finite)
else:
if ensure_2d:
array = np.atleast_2d(array)
array = np.array(array, dtype=dtype, order=order, copy=copy)
if not allow_nd and array.ndim >= 3:
raise ValueError("Found array with dim %d. Expected <= 2" %
array.ndim)
if force_all_finite:
_assert_all_finite(array)
return array
def check_X_y(X, y, accept_sparse=None, dtype=None, order=None, copy=False,
force_all_finite=True, ensure_2d=True, allow_nd=False,
multi_output=False):
"""Input validation for standard estimators.
Checks X and y for consistent length, enforces X 2d and y 1d.
Standard input checks are only applied to y. For multi-label y,
set multi_ouput=True to allow 2d and sparse y.
Parameters
----------
X : nd-array, list or sparse matrix
Input data.
y : nd-array, list or sparse matrix
Labels.
accept_sparse : string, list of string or None (default=None)
String[s] representing allowed sparse matrix formats, such as 'csc',
'csr', etc. None means that sparse matrix input will raise an error.
If the input is sparse but not in the allowed format, it will be
converted to the first listed format.
dtype : string, type or None (default=none)
Data type of result. If None, the dtype of the input is preserved.
order : 'F', 'C' or None (default=None)
Whether an array will be forced to be fortran or c-style.
copy : boolean (default=False)
Whether a forced copy will be triggered. If copy=False, a copy might
be triggered by a conversion.
force_all_finite : boolean (default=True)
Whether to raise an error on np.inf and np.nan in X.
ensure_2d : boolean (default=True)
Whether to make X at least 2d.
allow_nd : boolean (default=False)
Whether to allow X.ndim > 2.
multi_output : boolean (default=False)
Whether to allow 2-d y (array or sparse matrix). If false, y will be
validated as a vector.
Returns
-------
X_converted : object
The converted and validated X.
"""
X = check_array(X, accept_sparse, dtype, order, copy, force_all_finite,
ensure_2d, allow_nd)
if multi_output:
y = check_array(y, 'csr', force_all_finite=True, ensure_2d=False)
else:
y = column_or_1d(y, warn=True)
_assert_all_finite(y)
check_consistent_length(X, y)
return X, y
def column_or_1d(y, warn=False):
""" Ravel column or 1d numpy array, else raises an error
Parameters
----------
y : array-like
Returns
-------
y : array
"""
shape = np.shape(y)
if len(shape) == 1:
return np.ravel(y)
if len(shape) == 2 and shape[1] == 1:
if warn:
warnings.warn("A column-vector y was passed when a 1d array was"
" expected. Please change the shape of y to "
"(n_samples, ), for example using ravel().",
DataConversionWarning, stacklevel=2)
return np.ravel(y)
raise ValueError("bad input shape {0}".format(shape))
def warn_if_not_float(X, estimator='This algorithm'):
"""Warning utility function to check that data type is floating point.
Returns True if a warning was raised (i.e. the input is not float) and
False otherwise, for easier input validation.
"""
if not isinstance(estimator, six.string_types):
estimator = estimator.__class__.__name__
if X.dtype.kind != 'f':
warnings.warn("%s assumes floating point values as input, "
"got %s" % (estimator, X.dtype))
return True
return False
def check_random_state(seed):
"""Turn seed into a np.random.RandomState instance
If seed is None, return the RandomState singleton used by np.random.
If seed is an int, return a new RandomState instance seeded with seed.
If seed is already a RandomState instance, return it.
Otherwise raise ValueError.
"""
if seed is None or seed is np.random:
return np.random.mtrand._rand
if isinstance(seed, (numbers.Integral, np.integer)):
return np.random.RandomState(seed)
if isinstance(seed, np.random.RandomState):
return seed
raise ValueError('%r cannot be used to seed a numpy.random.RandomState'
' instance' % seed)
def has_fit_parameter(estimator, parameter):
""" Checks whether the estimator's fit method supports the given parameter.
Example
-------
>>> from sklearn.svm import SVC
>>> has_fit_parameter(SVC(), "sample_weight")
True
"""
return parameter in getargspec(estimator.fit)[0]
| bsd-3-clause |
jkarnows/scikit-learn | examples/classification/plot_lda_qda.py | 164 | 4806 | """
====================================================================
Linear and Quadratic Discriminant Analysis with confidence ellipsoid
====================================================================
Plot the confidence ellipsoids of each class and decision boundary
"""
print(__doc__)
from scipy import linalg
import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mpl
from matplotlib import colors
from sklearn.lda import LDA
from sklearn.qda import QDA
###############################################################################
# colormap
cmap = colors.LinearSegmentedColormap(
'red_blue_classes',
{'red': [(0, 1, 1), (1, 0.7, 0.7)],
'green': [(0, 0.7, 0.7), (1, 0.7, 0.7)],
'blue': [(0, 0.7, 0.7), (1, 1, 1)]})
plt.cm.register_cmap(cmap=cmap)
###############################################################################
# generate datasets
def dataset_fixed_cov():
'''Generate 2 Gaussians samples with the same covariance matrix'''
n, dim = 300, 2
np.random.seed(0)
C = np.array([[0., -0.23], [0.83, .23]])
X = np.r_[np.dot(np.random.randn(n, dim), C),
np.dot(np.random.randn(n, dim), C) + np.array([1, 1])]
y = np.hstack((np.zeros(n), np.ones(n)))
return X, y
def dataset_cov():
'''Generate 2 Gaussians samples with different covariance matrices'''
n, dim = 300, 2
np.random.seed(0)
C = np.array([[0., -1.], [2.5, .7]]) * 2.
X = np.r_[np.dot(np.random.randn(n, dim), C),
np.dot(np.random.randn(n, dim), C.T) + np.array([1, 4])]
y = np.hstack((np.zeros(n), np.ones(n)))
return X, y
###############################################################################
# plot functions
def plot_data(lda, X, y, y_pred, fig_index):
splot = plt.subplot(2, 2, fig_index)
if fig_index == 1:
plt.title('Linear Discriminant Analysis')
plt.ylabel('Data with fixed covariance')
elif fig_index == 2:
plt.title('Quadratic Discriminant Analysis')
elif fig_index == 3:
plt.ylabel('Data with varying covariances')
tp = (y == y_pred) # True Positive
tp0, tp1 = tp[y == 0], tp[y == 1]
X0, X1 = X[y == 0], X[y == 1]
X0_tp, X0_fp = X0[tp0], X0[~tp0]
X1_tp, X1_fp = X1[tp1], X1[~tp1]
xmin, xmax = X[:, 0].min(), X[:, 0].max()
ymin, ymax = X[:, 1].min(), X[:, 1].max()
# class 0: dots
plt.plot(X0_tp[:, 0], X0_tp[:, 1], 'o', color='red')
plt.plot(X0_fp[:, 0], X0_fp[:, 1], '.', color='#990000') # dark red
# class 1: dots
plt.plot(X1_tp[:, 0], X1_tp[:, 1], 'o', color='blue')
plt.plot(X1_fp[:, 0], X1_fp[:, 1], '.', color='#000099') # dark blue
# class 0 and 1 : areas
nx, ny = 200, 100
x_min, x_max = plt.xlim()
y_min, y_max = plt.ylim()
xx, yy = np.meshgrid(np.linspace(x_min, x_max, nx),
np.linspace(y_min, y_max, ny))
Z = lda.predict_proba(np.c_[xx.ravel(), yy.ravel()])
Z = Z[:, 1].reshape(xx.shape)
plt.pcolormesh(xx, yy, Z, cmap='red_blue_classes',
norm=colors.Normalize(0., 1.))
plt.contour(xx, yy, Z, [0.5], linewidths=2., colors='k')
# means
plt.plot(lda.means_[0][0], lda.means_[0][1],
'o', color='black', markersize=10)
plt.plot(lda.means_[1][0], lda.means_[1][1],
'o', color='black', markersize=10)
return splot
def plot_ellipse(splot, mean, cov, color):
v, w = linalg.eigh(cov)
u = w[0] / linalg.norm(w[0])
angle = np.arctan(u[1] / u[0])
angle = 180 * angle / np.pi # convert to degrees
# filled Gaussian at 2 standard deviation
ell = mpl.patches.Ellipse(mean, 2 * v[0] ** 0.5, 2 * v[1] ** 0.5,
180 + angle, color=color)
ell.set_clip_box(splot.bbox)
ell.set_alpha(0.5)
splot.add_artist(ell)
splot.set_xticks(())
splot.set_yticks(())
def plot_lda_cov(lda, splot):
plot_ellipse(splot, lda.means_[0], lda.covariance_, 'red')
plot_ellipse(splot, lda.means_[1], lda.covariance_, 'blue')
def plot_qda_cov(qda, splot):
plot_ellipse(splot, qda.means_[0], qda.covariances_[0], 'red')
plot_ellipse(splot, qda.means_[1], qda.covariances_[1], 'blue')
###############################################################################
for i, (X, y) in enumerate([dataset_fixed_cov(), dataset_cov()]):
# LDA
lda = LDA(solver="svd", store_covariance=True)
y_pred = lda.fit(X, y).predict(X)
splot = plot_data(lda, X, y, y_pred, fig_index=2 * i + 1)
plot_lda_cov(lda, splot)
plt.axis('tight')
# QDA
qda = QDA()
y_pred = qda.fit(X, y, store_covariances=True).predict(X)
splot = plot_data(qda, X, y, y_pred, fig_index=2 * i + 2)
plot_qda_cov(qda, splot)
plt.axis('tight')
plt.suptitle('LDA vs QDA')
plt.show()
| bsd-3-clause |
liqiangnlp/LiNMT | scripts/bar.figure/systems-bleu-score-cwmt.py | 1 | 3227 | #!/usr/bin/env python
#-*- coding: utf-8 -*-
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import rcParams
from matplotlib.ticker import MultipleLocator, FormatStrFormatter
rcParams['grid.linestyle'] = ':'
def autolabel(rects):
for rect in rects:
height = rect.get_height()
plt.text(rect.get_x()+rect.get_width()/4, height + 1.5, '%s' % float(height), rotation=90, fontsize=10)
def drawBarChartPoseRatio():
n_groups = 2
'''
bleu-4 / bleu-sbp-5
newsdev2017 cwmt2011 cwmt2009
2-2 NiuSMT.biglm : 14.20 17.59 30.90 31.29 31.50 32.61
3 NMT-UM-baseline : 17.40 20.44 37.71 36.85 39.75 38.88
4 NMT-UM-bpe : 17.60 20.91 37.30 36.66 39.90 39.43
4-1 NMT-UM-bpe-finetune : 17.70 20.86 37.80 37.18 40.00 39.53
5 NMT-UM-bpe-finetune-synthetic: 17.89 21.44 36.21 36.26 38.46 38.78
5-1 ensemble-4 &4-1&5 : 18.41 21.81 38.65 38.03 40.76 41.89
5-2 avg-4&4-1&5 : 18.24 21.61 38.28 37.67 40.88 40.39
9 NMT-NEU-bpe (nematus) : 16.73 20.73 34.06 36.51 40.27 39.94
'''
smt_um_biglm = (31.29, 32.61)
nmt_um_baseline = (36.85, 38.88)
nmt_um_bpe = (36.66, 39.43)
nmt_um_bpe_finetune = (37.18, 39.53)
nmt_um_bpe_finetune_syn = (36.26, 38.78)
nmt_um_ensemble = (38.03, 41.89)
nematus = (36.51, 39.94)
fig, ax = plt.subplots()
index = np.arange(n_groups)
bar_width = 0.2
#opacity = 0.4
opacity = 1
#plt.grid()
yminorLocator = MultipleLocator(1)
ax.yaxis.set_minor_locator(yminorLocator)
ax.yaxis.grid(True, which='minor')
rects1 = plt.bar(index, nmt_um_baseline, bar_width / 2, alpha=opacity, color='m', label='$li-baseline$')
rects2 = plt.bar(index + bar_width / 2, nmt_um_bpe, bar_width / 2, alpha=opacity, color='g', label='$li-bpe$')
rects3 = plt.bar(index + bar_width, nmt_um_bpe_finetune, bar_width / 2, alpha=opacity, color='c', label='$li-bpe-fine$')
rects4 = plt.bar(index + 1.5 * bar_width, nmt_um_bpe_finetune_syn, bar_width / 2, alpha=opacity, color='b', label='$li-bpe-fine-syn$')
rects5 = plt.bar(index + 2 * bar_width, nmt_um_ensemble, bar_width / 2, alpha=opacity, color='r', label='$li-ensemble$')
rects6 = plt.bar(index + 2.5 * bar_width, nematus, bar_width / 2, alpha=0.4, color='r', label='$ben-nematus$')
rects7 = plt.bar(index + 3 * bar_width, smt_um_biglm, bar_width / 2, alpha=0.2, color='r', label='$li-smt-biglm$')
autolabel(rects1)
autolabel(rects2)
autolabel(rects3)
autolabel(rects4)
autolabel(rects5)
autolabel(rects6)
autolabel(rects7)
#plt.xlabel('Category', fontsize=16)
plt.ylabel('BLEU-SBP-5', fontsize=16)
#plt.title('Scores by group and Category')
# plt.xticks(index - 0.2+ 2*bar_width, ('balde', 'bunny', 'dragon', 'happy', 'pillow'))
plt.xticks(index - 0.2 + 2.5 * bar_width, ('cwmt2011', 'cwmt2009'), fontsize = 16)
plt.yticks(fontsize=14) # change the num axis size
plt.ylim(30, 45) # The ceil
plt.legend()
#plt.tight_layout()
plt.show()
drawBarChartPoseRatio()
| mit |
fredhusser/scikit-learn | examples/exercises/plot_cv_diabetes.py | 231 | 2527 | """
===============================================
Cross-validation on diabetes Dataset Exercise
===============================================
A tutorial exercise which uses cross-validation with linear models.
This exercise is used in the :ref:`cv_estimators_tut` part of the
:ref:`model_selection_tut` section of the :ref:`stat_learn_tut_index`.
"""
from __future__ import print_function
print(__doc__)
import numpy as np
import matplotlib.pyplot as plt
from sklearn import cross_validation, datasets, linear_model
diabetes = datasets.load_diabetes()
X = diabetes.data[:150]
y = diabetes.target[:150]
lasso = linear_model.Lasso()
alphas = np.logspace(-4, -.5, 30)
scores = list()
scores_std = list()
for alpha in alphas:
lasso.alpha = alpha
this_scores = cross_validation.cross_val_score(lasso, X, y, n_jobs=1)
scores.append(np.mean(this_scores))
scores_std.append(np.std(this_scores))
plt.figure(figsize=(4, 3))
plt.semilogx(alphas, scores)
# plot error lines showing +/- std. errors of the scores
plt.semilogx(alphas, np.array(scores) + np.array(scores_std) / np.sqrt(len(X)),
'b--')
plt.semilogx(alphas, np.array(scores) - np.array(scores_std) / np.sqrt(len(X)),
'b--')
plt.ylabel('CV score')
plt.xlabel('alpha')
plt.axhline(np.max(scores), linestyle='--', color='.5')
##############################################################################
# Bonus: how much can you trust the selection of alpha?
# To answer this question we use the LassoCV object that sets its alpha
# parameter automatically from the data by internal cross-validation (i.e. it
# performs cross-validation on the training data it receives).
# We use external cross-validation to see how much the automatically obtained
# alphas differ across different cross-validation folds.
lasso_cv = linear_model.LassoCV(alphas=alphas)
k_fold = cross_validation.KFold(len(X), 3)
print("Answer to the bonus question:",
"how much can you trust the selection of alpha?")
print()
print("Alpha parameters maximising the generalization score on different")
print("subsets of the data:")
for k, (train, test) in enumerate(k_fold):
lasso_cv.fit(X[train], y[train])
print("[fold {0}] alpha: {1:.5f}, score: {2:.5f}".
format(k, lasso_cv.alpha_, lasso_cv.score(X[test], y[test])))
print()
print("Answer: Not very much since we obtained different alphas for different")
print("subsets of the data and moreover, the scores for these alphas differ")
print("quite substantially.")
plt.show()
| bsd-3-clause |
cgre-aachen/gempy | test/test_anisotropies/test_anisotropies.py | 1 | 4796 | import numpy as np
import pytest
import gempy as gp
import matplotlib.pyplot as plt
import os
# Input files
root = 'https://raw.githubusercontent.com/cgre-aachen/gempy_data/master/data/input_data/turner_syncline/'
path = os.path.dirname(__file__) + '/../input_data/'
orientations_file = root + 'orientations_clean.csv'
contacts_file = root + 'contacts_clean.csv'
fp = path + 'dtm_rp.tif'
series_file = root + 'all_sorts_clean.csv'
bbox = (500000, 7490000, 545000, 7520000)
model_base = -1500 # Original 3200
model_top = 800
gdal = pytest.importorskip("gdal")
@pytest.fixture(scope='module')
def model():
geo_model = gp.create_model('test_map2Loop')
gp.init_data(
geo_model,
extent=[bbox[0], bbox[2], bbox[1], bbox[3], model_base, model_top],
resolution=[50, 50, 80],
path_o=orientations_file,
path_i=contacts_file
)
# Load Topology
geo_model.set_topography(source='gdal', filepath=fp)
# Stack Processing
contents = np.genfromtxt(series_file,
delimiter=',', dtype='U100')[1:, 4:-1]
map_series_to_surfaces = {}
for pair in contents:
map_series_to_surfaces.setdefault(pair[1], []).append(pair[0])
gp.map_stack_to_surfaces(geo_model, map_series_to_surfaces,
remove_unused_series=False)
gp.plot_3d(geo_model, ve=None, show_topography=False
, image=True, show_lith=False,
kwargs_plot_data={'arrow_size': 300})
return geo_model
def test_axial_anisotropy_type_data(model):
geo_model = model
geo_model._rescaling.toggle_axial_anisotropy()
# gp.compute_model(geo_model, compute_mesh_options={'mask_topography': False})
geo_model.surface_points.df[['X', 'Y', 'Z']] = geo_model.surface_points.df[['X_c', 'Y_c',
'Z_c']]
geo_model.orientations.df[['X', 'Y', 'Z']] = geo_model.orientations.df[['X_c', 'Y_c',
'Z_c']]
# This is a hack
geo_model._grid.topography.extent = geo_model._grid.extent_c
geo_model.set_regular_grid(geo_model._grid.extent_c, [50, 50, 50])
gp.plot_3d(geo_model, ve=None, show_topography=False
, image=True, show_lith=False,
kwargs_plot_data={'arrow_size': 10})
def test_axial_anisotropy_type_extent(model):
geo_model = model
geo_model._rescaling.toggle_axial_anisotropy(type='extent')
# gp.compute_model(geo_model, compute_mesh_options={'mask_topography': False})
geo_model.surface_points.df[['X', 'Y', 'Z']] = geo_model.surface_points.df[['X_c', 'Y_c',
'Z_c']]
geo_model.orientations.df[['X', 'Y', 'Z']] = geo_model.orientations.df[['X_c', 'Y_c',
'Z_c']]
# This is a hack
geo_model._grid.topography.extent = geo_model._grid.extent_c
geo_model.set_regular_grid(geo_model._grid.extent_c, [50, 50, 50])
gp.plot_3d(geo_model, ve=None, show_topography=False
, image=True, show_lith=False,
kwargs_plot_data={'arrow_size': 10})
def test_axial_anisotropy(model):
# Location box
geo_model = model
geo_model._rescaling.toggle_axial_anisotropy()
# gp.compute_model(geo_model, compute_mesh_options={'mask_topography': False})
geo_model.surface_points.df[['X', 'Y', 'Z']] = geo_model.surface_points.df[['X_c', 'Y_c',
'Z_c']]
geo_model.orientations.df[['X', 'Y', 'Z']] = geo_model.orientations.df[['X_c', 'Y_c',
'Z_c']]
# This is a hack
geo_model._grid.topography.extent = geo_model._grid.extent_c
geo_model.set_regular_grid(geo_model._grid.extent_c, [50, 50, 50])
geo_model.modify_kriging_parameters('range', 0.1)
geo_model.modify_kriging_parameters('drift equations', [9, 9, 9, 9, 9])
geo_model.modify_surface_points(
geo_model.surface_points.df.index,
smooth=0.001
)
gp.set_interpolator(geo_model, theano_optimizer='fast_run', dtype='float64')
gp.compute_model(geo_model, compute_mesh_options={'mask_topography': False,
'masked_marching_cubes': False})
gp.plot_2d(geo_model,
section_names=['topography'],
show_topography=True,
)
plt.show()
gp.plot_3d(geo_model, ve=None, show_topography=False, image=True, show_lith=False,
kwargs_plot_data={'arrow_size': 10}
)
| lgpl-3.0 |
MechCoder/scikit-learn | sklearn/decomposition/truncated_svd.py | 13 | 8301 | """Truncated SVD for sparse matrices, aka latent semantic analysis (LSA).
"""
# Author: Lars Buitinck
# Olivier Grisel <[email protected]>
# Michael Becker <[email protected]>
# License: 3-clause BSD.
import numpy as np
import scipy.sparse as sp
from scipy.sparse.linalg import svds
from ..base import BaseEstimator, TransformerMixin
from ..utils import check_array, check_random_state
from ..utils.extmath import randomized_svd, safe_sparse_dot, svd_flip
from ..utils.sparsefuncs import mean_variance_axis
__all__ = ["TruncatedSVD"]
class TruncatedSVD(BaseEstimator, TransformerMixin):
"""Dimensionality reduction using truncated SVD (aka LSA).
This transformer performs linear dimensionality reduction by means of
truncated singular value decomposition (SVD). Contrary to PCA, this
estimator does not center the data before computing the singular value
decomposition. This means it can work with scipy.sparse matrices
efficiently.
In particular, truncated SVD works on term count/tf-idf matrices as
returned by the vectorizers in sklearn.feature_extraction.text. In that
context, it is known as latent semantic analysis (LSA).
This estimator supports two algorithms: a fast randomized SVD solver, and
a "naive" algorithm that uses ARPACK as an eigensolver on (X * X.T) or
(X.T * X), whichever is more efficient.
Read more in the :ref:`User Guide <LSA>`.
Parameters
----------
n_components : int, default = 2
Desired dimensionality of output data.
Must be strictly less than the number of features.
The default value is useful for visualisation. For LSA, a value of
100 is recommended.
algorithm : string, default = "randomized"
SVD solver to use. Either "arpack" for the ARPACK wrapper in SciPy
(scipy.sparse.linalg.svds), or "randomized" for the randomized
algorithm due to Halko (2009).
n_iter : int, optional (default 5)
Number of iterations for randomized SVD solver. Not used by ARPACK.
The default is larger than the default in `randomized_svd` to handle
sparse matrices that may have large slowly decaying spectrum.
random_state : int, RandomState instance or None, optional, default = None
If int, random_state is the seed used by the random number generator;
If RandomState instance, random_state is the random number generator;
If None, the random number generator is the RandomState instance used
by `np.random`.
tol : float, optional
Tolerance for ARPACK. 0 means machine precision. Ignored by randomized
SVD solver.
Attributes
----------
components_ : array, shape (n_components, n_features)
explained_variance_ : array, shape (n_components,)
The variance of the training samples transformed by a projection to
each component.
explained_variance_ratio_ : array, shape (n_components,)
Percentage of variance explained by each of the selected components.
singular_values_ : array, shape (n_components,)
The singular values corresponding to each of the selected components.
The singular values are equal to the 2-norms of the ``n_components``
variables in the lower-dimensional space.
Examples
--------
>>> from sklearn.decomposition import TruncatedSVD
>>> from sklearn.random_projection import sparse_random_matrix
>>> X = sparse_random_matrix(100, 100, density=0.01, random_state=42)
>>> svd = TruncatedSVD(n_components=5, n_iter=7, random_state=42)
>>> svd.fit(X) # doctest: +NORMALIZE_WHITESPACE
TruncatedSVD(algorithm='randomized', n_components=5, n_iter=7,
random_state=42, tol=0.0)
>>> print(svd.explained_variance_ratio_) # doctest: +ELLIPSIS
[ 0.0606... 0.0584... 0.0497... 0.0434... 0.0372...]
>>> print(svd.explained_variance_ratio_.sum()) # doctest: +ELLIPSIS
0.249...
>>> print(svd.singular_values_) # doctest: +ELLIPSIS
[ 2.5841... 2.5245... 2.3201... 2.1753... 2.0443...]
See also
--------
PCA
RandomizedPCA
References
----------
Finding structure with randomness: Stochastic algorithms for constructing
approximate matrix decompositions
Halko, et al., 2009 (arXiv:909) http://arxiv.org/pdf/0909.4061
Notes
-----
SVD suffers from a problem called "sign indeterminancy", which means the
sign of the ``components_`` and the output from transform depend on the
algorithm and random state. To work around this, fit instances of this
class to data once, then keep the instance around to do transformations.
"""
def __init__(self, n_components=2, algorithm="randomized", n_iter=5,
random_state=None, tol=0.):
self.algorithm = algorithm
self.n_components = n_components
self.n_iter = n_iter
self.random_state = random_state
self.tol = tol
def fit(self, X, y=None):
"""Fit LSI model on training data X.
Parameters
----------
X : {array-like, sparse matrix}, shape (n_samples, n_features)
Training data.
Returns
-------
self : object
Returns the transformer object.
"""
self.fit_transform(X)
return self
def fit_transform(self, X, y=None):
"""Fit LSI model to X and perform dimensionality reduction on X.
Parameters
----------
X : {array-like, sparse matrix}, shape (n_samples, n_features)
Training data.
Returns
-------
X_new : array, shape (n_samples, n_components)
Reduced version of X. This will always be a dense array.
"""
X = check_array(X, accept_sparse=['csr', 'csc'])
random_state = check_random_state(self.random_state)
if self.algorithm == "arpack":
U, Sigma, VT = svds(X, k=self.n_components, tol=self.tol)
# svds doesn't abide by scipy.linalg.svd/randomized_svd
# conventions, so reverse its outputs.
Sigma = Sigma[::-1]
U, VT = svd_flip(U[:, ::-1], VT[::-1])
elif self.algorithm == "randomized":
k = self.n_components
n_features = X.shape[1]
if k >= n_features:
raise ValueError("n_components must be < n_features;"
" got %d >= %d" % (k, n_features))
U, Sigma, VT = randomized_svd(X, self.n_components,
n_iter=self.n_iter,
random_state=random_state)
else:
raise ValueError("unknown algorithm %r" % self.algorithm)
self.components_ = VT
# Calculate explained variance & explained variance ratio
X_transformed = U * Sigma
self.explained_variance_ = exp_var = np.var(X_transformed, axis=0)
if sp.issparse(X):
_, full_var = mean_variance_axis(X, axis=0)
full_var = full_var.sum()
else:
full_var = np.var(X, axis=0).sum()
self.explained_variance_ratio_ = exp_var / full_var
self.singular_values_ = Sigma # Store the singular values.
return X_transformed
def transform(self, X):
"""Perform dimensionality reduction on X.
Parameters
----------
X : {array-like, sparse matrix}, shape (n_samples, n_features)
New data.
Returns
-------
X_new : array, shape (n_samples, n_components)
Reduced version of X. This will always be a dense array.
"""
X = check_array(X, accept_sparse='csr')
return safe_sparse_dot(X, self.components_.T)
def inverse_transform(self, X):
"""Transform X back to its original space.
Returns an array X_original whose transform would be X.
Parameters
----------
X : array-like, shape (n_samples, n_components)
New data.
Returns
-------
X_original : array, shape (n_samples, n_features)
Note that this is always a dense array.
"""
X = check_array(X)
return np.dot(X, self.components_)
| bsd-3-clause |
spark-test/spark | python/pyspark/sql/tests/test_pandas_udf_grouped_agg.py | 6 | 20725 | #
# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements. See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not use this file except in compliance with
# the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
import unittest
from pyspark.rdd import PythonEvalType
from pyspark.sql import Row
from pyspark.sql.functions import array, explode, col, lit, mean, sum, \
udf, pandas_udf, PandasUDFType
from pyspark.sql.types import *
from pyspark.sql.utils import AnalysisException
from pyspark.testing.sqlutils import ReusedSQLTestCase, have_pandas, have_pyarrow, \
pandas_requirement_message, pyarrow_requirement_message
from pyspark.testing.utils import QuietTest
if have_pandas:
import pandas as pd
from pandas.util.testing import assert_frame_equal
@unittest.skipIf(
not have_pandas or not have_pyarrow,
pandas_requirement_message or pyarrow_requirement_message)
class GroupedAggPandasUDFTests(ReusedSQLTestCase):
@property
def data(self):
return self.spark.range(10).toDF('id') \
.withColumn("vs", array([lit(i * 1.0) + col('id') for i in range(20, 30)])) \
.withColumn("v", explode(col('vs'))) \
.drop('vs') \
.withColumn('w', lit(1.0))
@property
def python_plus_one(self):
@udf('double')
def plus_one(v):
assert isinstance(v, (int, float))
return v + 1
return plus_one
@property
def pandas_scalar_plus_two(self):
@pandas_udf('double', PandasUDFType.SCALAR)
def plus_two(v):
assert isinstance(v, pd.Series)
return v + 2
return plus_two
@property
def pandas_agg_mean_udf(self):
@pandas_udf('double', PandasUDFType.GROUPED_AGG)
def avg(v):
return v.mean()
return avg
@property
def pandas_agg_sum_udf(self):
@pandas_udf('double', PandasUDFType.GROUPED_AGG)
def sum(v):
return v.sum()
return sum
@property
def pandas_agg_weighted_mean_udf(self):
import numpy as np
@pandas_udf('double', PandasUDFType.GROUPED_AGG)
def weighted_mean(v, w):
return np.average(v, weights=w)
return weighted_mean
def test_manual(self):
df = self.data
sum_udf = self.pandas_agg_sum_udf
mean_udf = self.pandas_agg_mean_udf
mean_arr_udf = pandas_udf(
self.pandas_agg_mean_udf.func,
ArrayType(self.pandas_agg_mean_udf.returnType),
self.pandas_agg_mean_udf.evalType)
result1 = df.groupby('id').agg(
sum_udf(df.v),
mean_udf(df.v),
mean_arr_udf(array(df.v))).sort('id')
expected1 = self.spark.createDataFrame(
[[0, 245.0, 24.5, [24.5]],
[1, 255.0, 25.5, [25.5]],
[2, 265.0, 26.5, [26.5]],
[3, 275.0, 27.5, [27.5]],
[4, 285.0, 28.5, [28.5]],
[5, 295.0, 29.5, [29.5]],
[6, 305.0, 30.5, [30.5]],
[7, 315.0, 31.5, [31.5]],
[8, 325.0, 32.5, [32.5]],
[9, 335.0, 33.5, [33.5]]],
['id', 'sum(v)', 'avg(v)', 'avg(array(v))'])
assert_frame_equal(expected1.toPandas(), result1.toPandas())
def test_basic(self):
df = self.data
weighted_mean_udf = self.pandas_agg_weighted_mean_udf
# Groupby one column and aggregate one UDF with literal
result1 = df.groupby('id').agg(weighted_mean_udf(df.v, lit(1.0))).sort('id')
expected1 = df.groupby('id').agg(mean(df.v).alias('weighted_mean(v, 1.0)')).sort('id')
assert_frame_equal(expected1.toPandas(), result1.toPandas())
# Groupby one expression and aggregate one UDF with literal
result2 = df.groupby((col('id') + 1)).agg(weighted_mean_udf(df.v, lit(1.0)))\
.sort(df.id + 1)
expected2 = df.groupby((col('id') + 1))\
.agg(mean(df.v).alias('weighted_mean(v, 1.0)')).sort(df.id + 1)
assert_frame_equal(expected2.toPandas(), result2.toPandas())
# Groupby one column and aggregate one UDF without literal
result3 = df.groupby('id').agg(weighted_mean_udf(df.v, df.w)).sort('id')
expected3 = df.groupby('id').agg(mean(df.v).alias('weighted_mean(v, w)')).sort('id')
assert_frame_equal(expected3.toPandas(), result3.toPandas())
# Groupby one expression and aggregate one UDF without literal
result4 = df.groupby((col('id') + 1).alias('id'))\
.agg(weighted_mean_udf(df.v, df.w))\
.sort('id')
expected4 = df.groupby((col('id') + 1).alias('id'))\
.agg(mean(df.v).alias('weighted_mean(v, w)'))\
.sort('id')
assert_frame_equal(expected4.toPandas(), result4.toPandas())
def test_unsupported_types(self):
with QuietTest(self.sc):
with self.assertRaisesRegexp(NotImplementedError, 'not supported'):
pandas_udf(
lambda x: x,
ArrayType(ArrayType(TimestampType())),
PandasUDFType.GROUPED_AGG)
with QuietTest(self.sc):
with self.assertRaisesRegexp(NotImplementedError, 'not supported'):
@pandas_udf('mean double, std double', PandasUDFType.GROUPED_AGG)
def mean_and_std_udf(v):
return v.mean(), v.std()
with QuietTest(self.sc):
with self.assertRaisesRegexp(NotImplementedError, 'not supported'):
@pandas_udf(MapType(DoubleType(), DoubleType()), PandasUDFType.GROUPED_AGG)
def mean_and_std_udf(v):
return {v.mean(): v.std()}
def test_alias(self):
df = self.data
mean_udf = self.pandas_agg_mean_udf
result1 = df.groupby('id').agg(mean_udf(df.v).alias('mean_alias'))
expected1 = df.groupby('id').agg(mean(df.v).alias('mean_alias'))
assert_frame_equal(expected1.toPandas(), result1.toPandas())
def test_mixed_sql(self):
"""
Test mixing group aggregate pandas UDF with sql expression.
"""
df = self.data
sum_udf = self.pandas_agg_sum_udf
# Mix group aggregate pandas UDF with sql expression
result1 = (df.groupby('id')
.agg(sum_udf(df.v) + 1)
.sort('id'))
expected1 = (df.groupby('id')
.agg(sum(df.v) + 1)
.sort('id'))
# Mix group aggregate pandas UDF with sql expression (order swapped)
result2 = (df.groupby('id')
.agg(sum_udf(df.v + 1))
.sort('id'))
expected2 = (df.groupby('id')
.agg(sum(df.v + 1))
.sort('id'))
# Wrap group aggregate pandas UDF with two sql expressions
result3 = (df.groupby('id')
.agg(sum_udf(df.v + 1) + 2)
.sort('id'))
expected3 = (df.groupby('id')
.agg(sum(df.v + 1) + 2)
.sort('id'))
assert_frame_equal(expected1.toPandas(), result1.toPandas())
assert_frame_equal(expected2.toPandas(), result2.toPandas())
assert_frame_equal(expected3.toPandas(), result3.toPandas())
def test_mixed_udfs(self):
"""
Test mixing group aggregate pandas UDF with python UDF and scalar pandas UDF.
"""
df = self.data
plus_one = self.python_plus_one
plus_two = self.pandas_scalar_plus_two
sum_udf = self.pandas_agg_sum_udf
# Mix group aggregate pandas UDF and python UDF
result1 = (df.groupby('id')
.agg(plus_one(sum_udf(df.v)))
.sort('id'))
expected1 = (df.groupby('id')
.agg(plus_one(sum(df.v)))
.sort('id'))
# Mix group aggregate pandas UDF and python UDF (order swapped)
result2 = (df.groupby('id')
.agg(sum_udf(plus_one(df.v)))
.sort('id'))
expected2 = (df.groupby('id')
.agg(sum(plus_one(df.v)))
.sort('id'))
# Mix group aggregate pandas UDF and scalar pandas UDF
result3 = (df.groupby('id')
.agg(sum_udf(plus_two(df.v)))
.sort('id'))
expected3 = (df.groupby('id')
.agg(sum(plus_two(df.v)))
.sort('id'))
# Mix group aggregate pandas UDF and scalar pandas UDF (order swapped)
result4 = (df.groupby('id')
.agg(plus_two(sum_udf(df.v)))
.sort('id'))
expected4 = (df.groupby('id')
.agg(plus_two(sum(df.v)))
.sort('id'))
# Wrap group aggregate pandas UDF with two python UDFs and use python UDF in groupby
result5 = (df.groupby(plus_one(df.id))
.agg(plus_one(sum_udf(plus_one(df.v))))
.sort('plus_one(id)'))
expected5 = (df.groupby(plus_one(df.id))
.agg(plus_one(sum(plus_one(df.v))))
.sort('plus_one(id)'))
# Wrap group aggregate pandas UDF with two scala pandas UDF and user scala pandas UDF in
# groupby
result6 = (df.groupby(plus_two(df.id))
.agg(plus_two(sum_udf(plus_two(df.v))))
.sort('plus_two(id)'))
expected6 = (df.groupby(plus_two(df.id))
.agg(plus_two(sum(plus_two(df.v))))
.sort('plus_two(id)'))
assert_frame_equal(expected1.toPandas(), result1.toPandas())
assert_frame_equal(expected2.toPandas(), result2.toPandas())
assert_frame_equal(expected3.toPandas(), result3.toPandas())
assert_frame_equal(expected4.toPandas(), result4.toPandas())
assert_frame_equal(expected5.toPandas(), result5.toPandas())
assert_frame_equal(expected6.toPandas(), result6.toPandas())
def test_multiple_udfs(self):
"""
Test multiple group aggregate pandas UDFs in one agg function.
"""
df = self.data
mean_udf = self.pandas_agg_mean_udf
sum_udf = self.pandas_agg_sum_udf
weighted_mean_udf = self.pandas_agg_weighted_mean_udf
result1 = (df.groupBy('id')
.agg(mean_udf(df.v),
sum_udf(df.v),
weighted_mean_udf(df.v, df.w))
.sort('id')
.toPandas())
expected1 = (df.groupBy('id')
.agg(mean(df.v),
sum(df.v),
mean(df.v).alias('weighted_mean(v, w)'))
.sort('id')
.toPandas())
assert_frame_equal(expected1, result1)
def test_complex_groupby(self):
df = self.data
sum_udf = self.pandas_agg_sum_udf
plus_one = self.python_plus_one
plus_two = self.pandas_scalar_plus_two
# groupby one expression
result1 = df.groupby(df.v % 2).agg(sum_udf(df.v))
expected1 = df.groupby(df.v % 2).agg(sum(df.v))
# empty groupby
result2 = df.groupby().agg(sum_udf(df.v))
expected2 = df.groupby().agg(sum(df.v))
# groupby one column and one sql expression
result3 = df.groupby(df.id, df.v % 2).agg(sum_udf(df.v)).orderBy(df.id, df.v % 2)
expected3 = df.groupby(df.id, df.v % 2).agg(sum(df.v)).orderBy(df.id, df.v % 2)
# groupby one python UDF
result4 = df.groupby(plus_one(df.id)).agg(sum_udf(df.v))
expected4 = df.groupby(plus_one(df.id)).agg(sum(df.v))
# groupby one scalar pandas UDF
result5 = df.groupby(plus_two(df.id)).agg(sum_udf(df.v)).sort('sum(v)')
expected5 = df.groupby(plus_two(df.id)).agg(sum(df.v)).sort('sum(v)')
# groupby one expression and one python UDF
result6 = df.groupby(df.v % 2, plus_one(df.id)).agg(sum_udf(df.v))
expected6 = df.groupby(df.v % 2, plus_one(df.id)).agg(sum(df.v))
# groupby one expression and one scalar pandas UDF
result7 = (df.groupby(df.v % 2, plus_two(df.id))
.agg(sum_udf(df.v)).sort(['sum(v)', 'plus_two(id)']))
expected7 = (df.groupby(df.v % 2, plus_two(df.id))
.agg(sum(df.v)).sort(['sum(v)', 'plus_two(id)']))
assert_frame_equal(expected1.toPandas(), result1.toPandas())
assert_frame_equal(expected2.toPandas(), result2.toPandas())
assert_frame_equal(expected3.toPandas(), result3.toPandas())
assert_frame_equal(expected4.toPandas(), result4.toPandas())
assert_frame_equal(expected5.toPandas(), result5.toPandas())
assert_frame_equal(expected6.toPandas(), result6.toPandas())
assert_frame_equal(expected7.toPandas(), result7.toPandas())
def test_complex_expressions(self):
df = self.data
plus_one = self.python_plus_one
plus_two = self.pandas_scalar_plus_two
sum_udf = self.pandas_agg_sum_udf
# Test complex expressions with sql expression, python UDF and
# group aggregate pandas UDF
result1 = (df.withColumn('v1', plus_one(df.v))
.withColumn('v2', df.v + 2)
.groupby(df.id, df.v % 2)
.agg(sum_udf(col('v')),
sum_udf(col('v1') + 3),
sum_udf(col('v2')) + 5,
plus_one(sum_udf(col('v1'))),
sum_udf(plus_one(col('v2'))))
.sort(['id', '(v % 2)'])
.toPandas().sort_values(by=['id', '(v % 2)']))
expected1 = (df.withColumn('v1', df.v + 1)
.withColumn('v2', df.v + 2)
.groupby(df.id, df.v % 2)
.agg(sum(col('v')),
sum(col('v1') + 3),
sum(col('v2')) + 5,
plus_one(sum(col('v1'))),
sum(plus_one(col('v2'))))
.sort(['id', '(v % 2)'])
.toPandas().sort_values(by=['id', '(v % 2)']))
# Test complex expressions with sql expression, scala pandas UDF and
# group aggregate pandas UDF
result2 = (df.withColumn('v1', plus_one(df.v))
.withColumn('v2', df.v + 2)
.groupby(df.id, df.v % 2)
.agg(sum_udf(col('v')),
sum_udf(col('v1') + 3),
sum_udf(col('v2')) + 5,
plus_two(sum_udf(col('v1'))),
sum_udf(plus_two(col('v2'))))
.sort(['id', '(v % 2)'])
.toPandas().sort_values(by=['id', '(v % 2)']))
expected2 = (df.withColumn('v1', df.v + 1)
.withColumn('v2', df.v + 2)
.groupby(df.id, df.v % 2)
.agg(sum(col('v')),
sum(col('v1') + 3),
sum(col('v2')) + 5,
plus_two(sum(col('v1'))),
sum(plus_two(col('v2'))))
.sort(['id', '(v % 2)'])
.toPandas().sort_values(by=['id', '(v % 2)']))
# Test sequential groupby aggregate
result3 = (df.groupby('id')
.agg(sum_udf(df.v).alias('v'))
.groupby('id')
.agg(sum_udf(col('v')))
.sort('id')
.toPandas())
expected3 = (df.groupby('id')
.agg(sum(df.v).alias('v'))
.groupby('id')
.agg(sum(col('v')))
.sort('id')
.toPandas())
assert_frame_equal(expected1, result1)
assert_frame_equal(expected2, result2)
assert_frame_equal(expected3, result3)
def test_retain_group_columns(self):
with self.sql_conf({"spark.sql.retainGroupColumns": False}):
df = self.data
sum_udf = self.pandas_agg_sum_udf
result1 = df.groupby(df.id).agg(sum_udf(df.v))
expected1 = df.groupby(df.id).agg(sum(df.v))
assert_frame_equal(expected1.toPandas(), result1.toPandas())
def test_array_type(self):
df = self.data
array_udf = pandas_udf(lambda x: [1.0, 2.0], 'array<double>', PandasUDFType.GROUPED_AGG)
result1 = df.groupby('id').agg(array_udf(df['v']).alias('v2'))
self.assertEquals(result1.first()['v2'], [1.0, 2.0])
def test_invalid_args(self):
df = self.data
plus_one = self.python_plus_one
mean_udf = self.pandas_agg_mean_udf
with QuietTest(self.sc):
with self.assertRaisesRegexp(
AnalysisException,
'nor.*aggregate function'):
df.groupby(df.id).agg(plus_one(df.v)).collect()
with QuietTest(self.sc):
with self.assertRaisesRegexp(
AnalysisException,
'aggregate function.*argument.*aggregate function'):
df.groupby(df.id).agg(mean_udf(mean_udf(df.v))).collect()
with QuietTest(self.sc):
with self.assertRaisesRegexp(
AnalysisException,
'mixture.*aggregate function.*group aggregate pandas UDF'):
df.groupby(df.id).agg(mean_udf(df.v), mean(df.v)).collect()
def test_register_vectorized_udf_basic(self):
sum_pandas_udf = pandas_udf(
lambda v: v.sum(), "integer", PythonEvalType.SQL_GROUPED_AGG_PANDAS_UDF)
self.assertEqual(sum_pandas_udf.evalType, PythonEvalType.SQL_GROUPED_AGG_PANDAS_UDF)
group_agg_pandas_udf = self.spark.udf.register("sum_pandas_udf", sum_pandas_udf)
self.assertEqual(group_agg_pandas_udf.evalType, PythonEvalType.SQL_GROUPED_AGG_PANDAS_UDF)
q = "SELECT sum_pandas_udf(v1) FROM VALUES (3, 0), (2, 0), (1, 1) tbl(v1, v2) GROUP BY v2"
actual = sorted(map(lambda r: r[0], self.spark.sql(q).collect()))
expected = [1, 5]
self.assertEqual(actual, expected)
def test_grouped_with_empty_partition(self):
data = [Row(id=1, x=2), Row(id=1, x=3), Row(id=2, x=4)]
expected = [Row(id=1, sum=5), Row(id=2, x=4)]
num_parts = len(data) + 1
df = self.spark.createDataFrame(self.sc.parallelize(data, numSlices=num_parts))
f = pandas_udf(lambda x: x.sum(),
'int', PandasUDFType.GROUPED_AGG)
result = df.groupBy('id').agg(f(df['x']).alias('sum')).collect()
self.assertEqual(result, expected)
def test_grouped_without_group_by_clause(self):
@pandas_udf('double', PandasUDFType.GROUPED_AGG)
def max_udf(v):
return v.max()
df = self.spark.range(0, 100)
self.spark.udf.register('max_udf', max_udf)
with self.tempView("table"):
df.createTempView('table')
agg1 = df.agg(max_udf(df['id']))
agg2 = self.spark.sql("select max_udf(id) from table")
assert_frame_equal(agg1.toPandas(), agg2.toPandas())
def test_no_predicate_pushdown_through(self):
# SPARK-30921: We should not pushdown predicates of PythonUDFs through Aggregate.
import numpy as np
@pandas_udf('float', PandasUDFType.GROUPED_AGG)
def mean(x):
return np.mean(x)
df = self.spark.createDataFrame([
Row(id=1, foo=42), Row(id=2, foo=1), Row(id=2, foo=2)
])
agg = df.groupBy('id').agg(mean('foo').alias("mean"))
filtered = agg.filter(agg['mean'] > 40.0)
assert(filtered.collect()[0]["mean"] == 42.0)
if __name__ == "__main__":
from pyspark.sql.tests.test_pandas_udf_grouped_agg import *
try:
import xmlrunner
testRunner = xmlrunner.XMLTestRunner(output='target/test-reports', verbosity=2)
except ImportError:
testRunner = None
unittest.main(testRunner=testRunner, verbosity=2)
| apache-2.0 |
wronk/mne-python | examples/preprocessing/plot_resample.py | 12 | 3439 | """
===============
Resampling data
===============
When performing experiments where timing is critical, a signal with a high
sampling rate is desired. However, having a signal with a much higher sampling
rate than is necessary needlessly consumes memory and slows down computations
operating on the data.
This example downsamples from 600 Hz to 100 Hz. This achieves a 6-fold
reduction in data size, at the cost of an equal loss of temporal resolution.
"""
# Authors: Marijn van Vliet <[email protected]>
#
# License: BSD (3-clause)
#
from __future__ import print_function
from matplotlib import pyplot as plt
import mne
from mne.datasets import sample
###############################################################################
# Setting up data paths and loading raw data (skip some data for speed)
data_path = sample.data_path()
raw_fname = data_path + '/MEG/sample/sample_audvis_raw.fif'
raw = mne.io.read_raw_fif(raw_fname).crop(120, 240).load_data()
###############################################################################
# Since downsampling reduces the timing precision of events, we recommend
# first extracting epochs and downsampling the Epochs object:
events = mne.find_events(raw)
epochs = mne.Epochs(raw, events, event_id=2, tmin=-0.1, tmax=0.8, preload=True)
# Downsample to 100 Hz
print('Original sampling rate:', epochs.info['sfreq'], 'Hz')
epochs_resampled = epochs.copy().resample(100, npad='auto')
print('New sampling rate:', epochs_resampled.info['sfreq'], 'Hz')
# Plot a piece of data to see the effects of downsampling
plt.figure(figsize=(7, 3))
n_samples_to_plot = int(0.5 * epochs.info['sfreq']) # plot 0.5 seconds of data
plt.plot(epochs.times[:n_samples_to_plot],
epochs.get_data()[0, 0, :n_samples_to_plot], color='black')
n_samples_to_plot = int(0.5 * epochs_resampled.info['sfreq'])
plt.plot(epochs_resampled.times[:n_samples_to_plot],
epochs_resampled.get_data()[0, 0, :n_samples_to_plot],
'-o', color='red')
plt.xlabel('time (s)')
plt.legend(['original', 'downsampled'], loc='best')
plt.title('Effect of downsampling')
mne.viz.tight_layout()
###############################################################################
# When resampling epochs is unwanted or impossible, for example when the data
# doesn't fit into memory or your analysis pipeline doesn't involve epochs at
# all, the alternative approach is to resample the continuous data. This
# can also be done on non-preloaded data.
# Resample to 300 Hz
raw_resampled = raw.copy().resample(300, npad='auto')
###############################################################################
# Because resampling also affects the stim channels, some trigger onsets might
# be lost in this case. While MNE attempts to downsample the stim channels in
# an intelligent manner to avoid this, the recommended approach is to find
# events on the original data before downsampling.
print('Number of events before resampling:', len(mne.find_events(raw)))
# Resample to 100 Hz (generates warning)
raw_resampled = raw.copy().resample(100, npad='auto')
print('Number of events after resampling:',
len(mne.find_events(raw_resampled)))
# To avoid losing events, jointly resample the data and event matrix
events = mne.find_events(raw)
raw_resampled, events_resampled = raw.copy().resample(
100, npad='auto', events=events)
print('Number of events after resampling:', len(events_resampled))
| bsd-3-clause |
MJuddBooth/pandas | pandas/tests/scalar/timedelta/test_formats.py | 9 | 1068 | # -*- coding: utf-8 -*-
import pytest
from pandas import Timedelta
@pytest.mark.parametrize('td, expected_repr', [
(Timedelta(10, unit='d'), "Timedelta('10 days 00:00:00')"),
(Timedelta(10, unit='s'), "Timedelta('0 days 00:00:10')"),
(Timedelta(10, unit='ms'), "Timedelta('0 days 00:00:00.010000')"),
(Timedelta(-10, unit='ms'), "Timedelta('-1 days +23:59:59.990000')")])
def test_repr(td, expected_repr):
assert repr(td) == expected_repr
@pytest.mark.parametrize('td, expected_iso', [
(Timedelta(days=6, minutes=50, seconds=3, milliseconds=10, microseconds=10,
nanoseconds=12), 'P6DT0H50M3.010010012S'),
(Timedelta(days=4, hours=12, minutes=30, seconds=5), 'P4DT12H30M5S'),
(Timedelta(nanoseconds=123), 'P0DT0H0M0.000000123S'),
# trim nano
(Timedelta(microseconds=10), 'P0DT0H0M0.00001S'),
# trim micro
(Timedelta(milliseconds=1), 'P0DT0H0M0.001S'),
# don't strip every 0
(Timedelta(minutes=1), 'P0DT0H1M0S')])
def test_isoformat(td, expected_iso):
assert td.isoformat() == expected_iso
| bsd-3-clause |
saketkc/statsmodels | statsmodels/regression/tests/test_regression.py | 18 | 38246 | """
Test functions for models.regression
"""
# TODO: Test for LM
from statsmodels.compat.python import long, lrange
import warnings
import pandas
import numpy as np
from numpy.testing import (assert_almost_equal, assert_approx_equal, assert_,
assert_raises, assert_equal, assert_allclose)
from scipy.linalg import toeplitz
from statsmodels.tools.tools import add_constant, categorical
from statsmodels.compat.numpy import np_matrix_rank
from statsmodels.regression.linear_model import OLS, WLS, GLS, yule_walker
from statsmodels.datasets import longley
from scipy.stats import t as student_t
DECIMAL_4 = 4
DECIMAL_3 = 3
DECIMAL_2 = 2
DECIMAL_1 = 1
DECIMAL_7 = 7
DECIMAL_0 = 0
class CheckRegressionResults(object):
"""
res2 contains results from Rmodelwrap or were obtained from a statistical
packages such as R, Stata, or SAS and were written to model_results
"""
decimal_params = DECIMAL_4
def test_params(self):
assert_almost_equal(self.res1.params, self.res2.params,
self.decimal_params)
decimal_standarderrors = DECIMAL_4
def test_standarderrors(self):
assert_almost_equal(self.res1.bse,self.res2.bse,
self.decimal_standarderrors)
decimal_confidenceintervals = DECIMAL_4
def test_confidenceintervals(self):
#NOTE: stata rounds residuals (at least) to sig digits so approx_equal
conf1 = self.res1.conf_int()
conf2 = self.res2.conf_int()
for i in range(len(conf1)):
assert_approx_equal(conf1[i][0], conf2[i][0],
self.decimal_confidenceintervals)
assert_approx_equal(conf1[i][1], conf2[i][1],
self.decimal_confidenceintervals)
decimal_conf_int_subset = DECIMAL_4
def test_conf_int_subset(self):
if len(self.res1.params) > 1:
ci1 = self.res1.conf_int(cols=(1,2))
ci2 = self.res1.conf_int()[1:3]
assert_almost_equal(ci1, ci2, self.decimal_conf_int_subset)
else:
pass
decimal_scale = DECIMAL_4
def test_scale(self):
assert_almost_equal(self.res1.scale, self.res2.scale,
self.decimal_scale)
decimal_rsquared = DECIMAL_4
def test_rsquared(self):
assert_almost_equal(self.res1.rsquared, self.res2.rsquared,
self.decimal_rsquared)
decimal_rsquared_adj = DECIMAL_4
def test_rsquared_adj(self):
assert_almost_equal(self.res1.rsquared_adj, self.res2.rsquared_adj,
self.decimal_rsquared_adj)
def test_degrees(self):
assert_equal(self.res1.model.df_model, self.res2.df_model)
assert_equal(self.res1.model.df_resid, self.res2.df_resid)
decimal_ess = DECIMAL_4
def test_ess(self):
#Explained Sum of Squares
assert_almost_equal(self.res1.ess, self.res2.ess,
self.decimal_ess)
decimal_ssr = DECIMAL_4
def test_sumof_squaredresids(self):
assert_almost_equal(self.res1.ssr, self.res2.ssr, self.decimal_ssr)
decimal_mse_resid = DECIMAL_4
def test_mse_resid(self):
#Mean squared error of residuals
assert_almost_equal(self.res1.mse_model, self.res2.mse_model,
self.decimal_mse_resid)
decimal_mse_model = DECIMAL_4
def test_mse_model(self):
assert_almost_equal(self.res1.mse_resid, self.res2.mse_resid,
self.decimal_mse_model)
decimal_mse_total = DECIMAL_4
def test_mse_total(self):
assert_almost_equal(self.res1.mse_total, self.res2.mse_total,
self.decimal_mse_total, err_msg="Test class %s" % self)
decimal_fvalue = DECIMAL_4
def test_fvalue(self):
#didn't change this, not sure it should complain -inf not equal -inf
#if not (np.isinf(self.res1.fvalue) and np.isinf(self.res2.fvalue)):
assert_almost_equal(self.res1.fvalue, self.res2.fvalue,
self.decimal_fvalue)
decimal_loglike = DECIMAL_4
def test_loglike(self):
assert_almost_equal(self.res1.llf, self.res2.llf, self.decimal_loglike)
decimal_aic = DECIMAL_4
def test_aic(self):
assert_almost_equal(self.res1.aic, self.res2.aic, self.decimal_aic)
decimal_bic = DECIMAL_4
def test_bic(self):
assert_almost_equal(self.res1.bic, self.res2.bic, self.decimal_bic)
decimal_pvalues = DECIMAL_4
def test_pvalues(self):
assert_almost_equal(self.res1.pvalues, self.res2.pvalues,
self.decimal_pvalues)
decimal_wresid = DECIMAL_4
def test_wresid(self):
assert_almost_equal(self.res1.wresid, self.res2.wresid,
self.decimal_wresid)
decimal_resids = DECIMAL_4
def test_resids(self):
assert_almost_equal(self.res1.resid, self.res2.resid,
self.decimal_resids)
decimal_norm_resids = DECIMAL_4
def test_norm_resids(self):
assert_almost_equal(self.res1.resid_pearson, self.res2.resid_pearson,
self.decimal_norm_resids)
#TODO: test fittedvalues and what else?
class TestOLS(CheckRegressionResults):
@classmethod
def setupClass(cls):
from .results.results_regression import Longley
data = longley.load()
data.exog = add_constant(data.exog, prepend=False)
res1 = OLS(data.endog, data.exog).fit()
res2 = Longley()
res2.wresid = res1.wresid # workaround hack
cls.res1 = res1
cls.res2 = res2
res_qr = OLS(data.endog, data.exog).fit(method="qr")
model_qr = OLS(data.endog, data.exog)
Q, R = np.linalg.qr(data.exog)
model_qr.exog_Q, model_qr.exog_R = Q, R
model_qr.normalized_cov_params = np.linalg.inv(np.dot(R.T, R))
model_qr.rank = np_matrix_rank(R)
res_qr2 = model_qr.fit(method="qr")
cls.res_qr = res_qr
cls.res_qr_manual = res_qr2
def test_eigenvalues(self):
eigenval_perc_diff = (self.res_qr.eigenvals - self.res_qr_manual.eigenvals)
eigenval_perc_diff /= self.res_qr.eigenvals
zeros = np.zeros_like(eigenval_perc_diff)
assert_almost_equal(eigenval_perc_diff, zeros, DECIMAL_7)
# Robust error tests. Compare values computed with SAS
def test_HC0_errors(self):
#They are split up because the copied results do not have any DECIMAL_4
#places for the last place.
assert_almost_equal(self.res1.HC0_se[:-1],
self.res2.HC0_se[:-1], DECIMAL_4)
assert_approx_equal(np.round(self.res1.HC0_se[-1]), self.res2.HC0_se[-1])
def test_HC1_errors(self):
assert_almost_equal(self.res1.HC1_se[:-1],
self.res2.HC1_se[:-1], DECIMAL_4)
assert_approx_equal(self.res1.HC1_se[-1], self.res2.HC1_se[-1])
def test_HC2_errors(self):
assert_almost_equal(self.res1.HC2_se[:-1],
self.res2.HC2_se[:-1], DECIMAL_4)
assert_approx_equal(self.res1.HC2_se[-1], self.res2.HC2_se[-1])
def test_HC3_errors(self):
assert_almost_equal(self.res1.HC3_se[:-1],
self.res2.HC3_se[:-1], DECIMAL_4)
assert_approx_equal(self.res1.HC3_se[-1], self.res2.HC3_se[-1])
def test_qr_params(self):
assert_almost_equal(self.res1.params,
self.res_qr.params, 6)
def test_qr_normalized_cov_params(self):
#todo: need assert_close
assert_almost_equal(np.ones_like(self.res1.normalized_cov_params),
self.res1.normalized_cov_params /
self.res_qr.normalized_cov_params, 5)
def test_missing(self):
data = longley.load()
data.exog = add_constant(data.exog, prepend=False)
data.endog[[3, 7, 14]] = np.nan
mod = OLS(data.endog, data.exog, missing='drop')
assert_equal(mod.endog.shape[0], 13)
assert_equal(mod.exog.shape[0], 13)
def test_rsquared_adj_overfit(self):
# Test that if df_resid = 0, rsquared_adj = 0.
# This is a regression test for user issue:
# https://github.com/statsmodels/statsmodels/issues/868
with warnings.catch_warnings(record=True):
x = np.random.randn(5)
y = np.random.randn(5, 6)
results = OLS(x, y).fit()
rsquared_adj = results.rsquared_adj
assert_equal(rsquared_adj, np.nan)
def test_qr_alternatives(self):
assert_allclose(self.res_qr.params, self.res_qr_manual.params,
rtol=5e-12)
def test_norm_resid(self):
resid = self.res1.wresid
norm_resid = resid / np.sqrt(np.sum(resid**2.0) / self.res1.df_resid)
model_norm_resid = self.res1.resid_pearson
assert_almost_equal(model_norm_resid, norm_resid, DECIMAL_7)
def test_norm_resid_zero_variance(self):
with warnings.catch_warnings(record=True):
y = self.res1.model.endog
res = OLS(y,y).fit()
assert_allclose(res.scale, 0, atol=1e-20)
assert_allclose(res.wresid, res.resid_pearson, atol=5e-11)
class TestRTO(CheckRegressionResults):
@classmethod
def setupClass(cls):
from .results.results_regression import LongleyRTO
data = longley.load()
res1 = OLS(data.endog, data.exog).fit()
res2 = LongleyRTO()
res2.wresid = res1.wresid # workaround hack
cls.res1 = res1
cls.res2 = res2
res_qr = OLS(data.endog, data.exog).fit(method="qr")
cls.res_qr = res_qr
class TestFtest(object):
"""
Tests f_test vs. RegressionResults
"""
@classmethod
def setupClass(cls):
data = longley.load()
data.exog = add_constant(data.exog, prepend=False)
cls.res1 = OLS(data.endog, data.exog).fit()
R = np.identity(7)[:-1,:]
cls.Ftest = cls.res1.f_test(R)
def test_F(self):
assert_almost_equal(self.Ftest.fvalue, self.res1.fvalue, DECIMAL_4)
def test_p(self):
assert_almost_equal(self.Ftest.pvalue, self.res1.f_pvalue, DECIMAL_4)
def test_Df_denom(self):
assert_equal(self.Ftest.df_denom, self.res1.model.df_resid)
def test_Df_num(self):
assert_equal(self.Ftest.df_num, 6)
class TestFTest2(object):
"""
A joint test that the coefficient on
GNP = the coefficient on UNEMP and that the coefficient on
POP = the coefficient on YEAR for the Longley dataset.
Ftest1 is from statsmodels. Results are from Rpy using R's car library.
"""
@classmethod
def setupClass(cls):
data = longley.load()
data.exog = add_constant(data.exog, prepend=False)
res1 = OLS(data.endog, data.exog).fit()
R2 = [[0,1,-1,0,0,0,0],[0, 0, 0, 0, 1, -1, 0]]
cls.Ftest1 = res1.f_test(R2)
hyp = 'x2 = x3, x5 = x6'
cls.NewFtest1 = res1.f_test(hyp)
def test_new_ftest(self):
assert_equal(self.NewFtest1.fvalue, self.Ftest1.fvalue)
def test_fvalue(self):
assert_almost_equal(self.Ftest1.fvalue, 9.7404618732968196, DECIMAL_4)
def test_pvalue(self):
assert_almost_equal(self.Ftest1.pvalue, 0.0056052885317493459,
DECIMAL_4)
def test_df_denom(self):
assert_equal(self.Ftest1.df_denom, 9)
def test_df_num(self):
assert_equal(self.Ftest1.df_num, 2)
class TestFtestQ(object):
"""
A joint hypothesis test that Rb = q. Coefficient tests are essentially
made up. Test values taken from Stata.
"""
@classmethod
def setupClass(cls):
data = longley.load()
data.exog = add_constant(data.exog, prepend=False)
res1 = OLS(data.endog, data.exog).fit()
R = np.array([[0,1,1,0,0,0,0],
[0,1,0,1,0,0,0],
[0,1,0,0,0,0,0],
[0,0,0,0,1,0,0],
[0,0,0,0,0,1,0]])
q = np.array([0,0,0,1,0])
cls.Ftest1 = res1.f_test((R,q))
def test_fvalue(self):
assert_almost_equal(self.Ftest1.fvalue, 70.115557, 5)
def test_pvalue(self):
assert_almost_equal(self.Ftest1.pvalue, 6.229e-07, 10)
def test_df_denom(self):
assert_equal(self.Ftest1.df_denom, 9)
def test_df_num(self):
assert_equal(self.Ftest1.df_num, 5)
class TestTtest(object):
"""
Test individual t-tests. Ie., are the coefficients significantly
different than zero.
"""
@classmethod
def setupClass(cls):
data = longley.load()
data.exog = add_constant(data.exog, prepend=False)
cls.res1 = OLS(data.endog, data.exog).fit()
R = np.identity(7)
cls.Ttest = cls.res1.t_test(R)
hyp = 'x1 = 0, x2 = 0, x3 = 0, x4 = 0, x5 = 0, x6 = 0, const = 0'
cls.NewTTest = cls.res1.t_test(hyp)
def test_new_tvalue(self):
assert_equal(self.NewTTest.tvalue, self.Ttest.tvalue)
def test_tvalue(self):
assert_almost_equal(self.Ttest.tvalue, self.res1.tvalues, DECIMAL_4)
def test_sd(self):
assert_almost_equal(self.Ttest.sd, self.res1.bse, DECIMAL_4)
def test_pvalue(self):
assert_almost_equal(self.Ttest.pvalue, student_t.sf(
np.abs(self.res1.tvalues), self.res1.model.df_resid)*2,
DECIMAL_4)
def test_df_denom(self):
assert_equal(self.Ttest.df_denom, self.res1.model.df_resid)
def test_effect(self):
assert_almost_equal(self.Ttest.effect, self.res1.params)
class TestTtest2(object):
"""
Tests the hypothesis that the coefficients on POP and YEAR
are equal.
Results from RPy using 'car' package.
"""
@classmethod
def setupClass(cls):
R = np.zeros(7)
R[4:6] = [1,-1]
data = longley.load()
data.exog = add_constant(data.exog, prepend=False)
res1 = OLS(data.endog, data.exog).fit()
cls.Ttest1 = res1.t_test(R)
def test_tvalue(self):
assert_almost_equal(self.Ttest1.tvalue, -4.0167754636397284,
DECIMAL_4)
def test_sd(self):
assert_almost_equal(self.Ttest1.sd, 455.39079425195314, DECIMAL_4)
def test_pvalue(self):
assert_almost_equal(self.Ttest1.pvalue, 2*0.0015163772380932246,
DECIMAL_4)
def test_df_denom(self):
assert_equal(self.Ttest1.df_denom, 9)
def test_effect(self):
assert_almost_equal(self.Ttest1.effect, -1829.2025687186533, DECIMAL_4)
class TestGLS(object):
"""
These test results were obtained by replication with R.
"""
@classmethod
def setupClass(cls):
from .results.results_regression import LongleyGls
data = longley.load()
exog = add_constant(np.column_stack((data.exog[:,1],
data.exog[:,4])), prepend=False)
tmp_results = OLS(data.endog, exog).fit()
rho = np.corrcoef(tmp_results.resid[1:],
tmp_results.resid[:-1])[0][1] # by assumption
order = toeplitz(np.arange(16))
sigma = rho**order
GLS_results = GLS(data.endog, exog, sigma=sigma).fit()
cls.res1 = GLS_results
cls.res2 = LongleyGls()
# attach for test_missing
cls.sigma = sigma
cls.exog = exog
cls.endog = data.endog
def test_aic(self):
assert_approx_equal(self.res1.aic+2, self.res2.aic, 3)
def test_bic(self):
assert_approx_equal(self.res1.bic, self.res2.bic, 2)
def test_loglike(self):
assert_almost_equal(self.res1.llf, self.res2.llf, DECIMAL_0)
def test_params(self):
assert_almost_equal(self.res1.params, self.res2.params, DECIMAL_1)
def test_resid(self):
assert_almost_equal(self.res1.resid, self.res2.resid, DECIMAL_4)
def test_scale(self):
assert_almost_equal(self.res1.scale, self.res2.scale, DECIMAL_4)
def test_tvalues(self):
assert_almost_equal(self.res1.tvalues, self.res2.tvalues, DECIMAL_4)
def test_standarderrors(self):
assert_almost_equal(self.res1.bse, self.res2.bse, DECIMAL_4)
def test_fittedvalues(self):
assert_almost_equal(self.res1.fittedvalues, self.res2.fittedvalues,
DECIMAL_4)
def test_pvalues(self):
assert_almost_equal(self.res1.pvalues, self.res2.pvalues, DECIMAL_4)
def test_missing(self):
endog = self.endog.copy() # copy or changes endog for other methods
endog[[4,7,14]] = np.nan
mod = GLS(endog, self.exog, sigma=self.sigma, missing='drop')
assert_equal(mod.endog.shape[0], 13)
assert_equal(mod.exog.shape[0], 13)
assert_equal(mod.sigma.shape, (13,13))
class TestGLS_alt_sigma(CheckRegressionResults):
"""
Test that GLS with no argument is equivalent to OLS.
"""
@classmethod
def setupClass(cls):
data = longley.load()
data.exog = add_constant(data.exog, prepend=False)
ols_res = OLS(data.endog, data.exog).fit()
gls_res = GLS(data.endog, data.exog).fit()
gls_res_scalar = GLS(data.endog, data.exog, sigma=1)
cls.endog = data.endog
cls.exog = data.exog
cls.res1 = gls_res
cls.res2 = ols_res
cls.res3 = gls_res_scalar
# self.res2.conf_int = self.res2.conf_int()
def test_wrong_size_sigma_1d(self):
n = len(self.endog)
assert_raises(ValueError, GLS, self.endog, self.exog, sigma=np.ones(n-1))
def test_wrong_size_sigma_2d(self):
n = len(self.endog)
assert_raises(ValueError, GLS, self.endog, self.exog, sigma=np.ones((n-1,n-1)))
# def check_confidenceintervals(self, conf1, conf2):
# assert_almost_equal(conf1, conf2, DECIMAL_4)
class TestLM(object):
@classmethod
def setupClass(cls):
# TODO: Test HAC method
X = np.random.randn(100,3)
b = np.ones((3,1))
e = np.random.randn(100,1)
y = np.dot(X,b) + e
# Cases?
# Homoskedastic
# HC0
cls.res1_full = OLS(y,X).fit()
cls.res1_restricted = OLS(y,X[:,0]).fit()
cls.res2_full = cls.res1_full.get_robustcov_results('HC0')
cls.res2_restricted = cls.res1_restricted.get_robustcov_results('HC0')
cls.X = X
cls.Y = y
def test_LM_homoskedastic(self):
resid = self.res1_restricted.wresid
n = resid.shape[0]
X = self.X
S = np.dot(resid,resid) / n * np.dot(X.T,X) / n
Sinv = np.linalg.inv(S)
s = np.mean(X * resid[:,None], 0)
LMstat = n * np.dot(np.dot(s,Sinv),s.T)
LMstat_OLS = self.res1_full.compare_lm_test(self.res1_restricted)
LMstat2 = LMstat_OLS[0]
assert_almost_equal(LMstat, LMstat2, DECIMAL_7)
def test_LM_heteroskedastic_nodemean(self):
resid = self.res1_restricted.wresid
n = resid.shape[0]
X = self.X
scores = X * resid[:,None]
S = np.dot(scores.T,scores) / n
Sinv = np.linalg.inv(S)
s = np.mean(scores, 0)
LMstat = n * np.dot(np.dot(s,Sinv),s.T)
LMstat_OLS = self.res2_full.compare_lm_test(self.res2_restricted, demean=False)
LMstat2 = LMstat_OLS[0]
assert_almost_equal(LMstat, LMstat2, DECIMAL_7)
def test_LM_heteroskedastic_demean(self):
resid = self.res1_restricted.wresid
n = resid.shape[0]
X = self.X
scores = X * resid[:,None]
scores_demean = scores - scores.mean(0)
S = np.dot(scores_demean.T,scores_demean) / n
Sinv = np.linalg.inv(S)
s = np.mean(scores, 0)
LMstat = n * np.dot(np.dot(s,Sinv),s.T)
LMstat_OLS = self.res2_full.compare_lm_test(self.res2_restricted)
LMstat2 = LMstat_OLS[0]
assert_almost_equal(LMstat, LMstat2, DECIMAL_7)
def test_LM_heteroskedastic_LRversion(self):
resid = self.res1_restricted.wresid
resid_full = self.res1_full.wresid
n = resid.shape[0]
X = self.X
scores = X * resid[:,None]
s = np.mean(scores, 0)
scores = X * resid_full[:,None]
S = np.dot(scores.T,scores) / n
Sinv = np.linalg.inv(S)
LMstat = n * np.dot(np.dot(s,Sinv),s.T)
LMstat_OLS = self.res2_full.compare_lm_test(self.res2_restricted, use_lr = True)
LMstat2 = LMstat_OLS[0]
assert_almost_equal(LMstat, LMstat2, DECIMAL_7)
def test_LM_nonnested(self):
assert_raises(ValueError, self.res2_restricted.compare_lm_test, self.res2_full)
class TestOLS_GLS_WLS_equivalence(object):
@classmethod
def setupClass(cls):
data = longley.load()
data.exog = add_constant(data.exog, prepend=False)
y = data.endog
X = data.exog
n = y.shape[0]
w = np.ones(n)
cls.results = []
cls.results.append(OLS(y, X).fit())
cls.results.append(WLS(y, X, w).fit())
cls.results.append(GLS(y, X, 100*w).fit())
cls.results.append(GLS(y, X, np.diag(0.1*w)).fit())
def test_ll(self):
llf = np.array([r.llf for r in self.results])
llf_1 = np.ones_like(llf) * self.results[0].llf
assert_almost_equal(llf, llf_1, DECIMAL_7)
ic = np.array([r.aic for r in self.results])
ic_1 = np.ones_like(ic) * self.results[0].aic
assert_almost_equal(ic, ic_1, DECIMAL_7)
ic = np.array([r.bic for r in self.results])
ic_1 = np.ones_like(ic) * self.results[0].bic
assert_almost_equal(ic, ic_1, DECIMAL_7)
def test_params(self):
params = np.array([r.params for r in self.results])
params_1 = np.array([self.results[0].params] * len(self.results))
assert_allclose(params, params_1)
def test_ss(self):
bse = np.array([r.bse for r in self.results])
bse_1 = np.array([self.results[0].bse] * len(self.results))
assert_allclose(bse, bse_1)
def test_rsquared(self):
rsquared = np.array([r.rsquared for r in self.results])
rsquared_1 = np.array([self.results[0].rsquared] * len(self.results))
assert_almost_equal(rsquared, rsquared_1, DECIMAL_7)
class TestGLS_WLS_equivalence(TestOLS_GLS_WLS_equivalence):
# reuse test methods
@classmethod
def setupClass(cls):
data = longley.load()
data.exog = add_constant(data.exog, prepend=False)
y = data.endog
X = data.exog
n = y.shape[0]
np.random.seed(5)
w = np.random.uniform(0.5, 1, n)
w_inv = 1. / w
cls.results = []
cls.results.append(WLS(y, X, w).fit())
cls.results.append(WLS(y, X, 0.01 * w).fit())
cls.results.append(GLS(y, X, 100 * w_inv).fit())
cls.results.append(GLS(y, X, np.diag(0.1 * w_inv)).fit())
def test_rsquared(self):
# TODO: WLS rsquared is ok, GLS might have wrong centered_tss
# We only check that WLS and GLS rsquared is invariant to scaling
# WLS and GLS have different rsquared
assert_almost_equal(self.results[1].rsquared, self.results[0].rsquared,
DECIMAL_7)
assert_almost_equal(self.results[3].rsquared, self.results[2].rsquared,
DECIMAL_7)
class TestNonFit(object):
@classmethod
def setupClass(cls):
data = longley.load()
data.exog = add_constant(data.exog, prepend=False)
cls.endog = data.endog
cls.exog = data.exog
cls.ols_model = OLS(data.endog, data.exog)
def test_df_resid(self):
df_resid = self.endog.shape[0] - self.exog.shape[1]
assert_equal(self.ols_model.df_resid, long(9))
class TestWLS_CornerCases(object):
@classmethod
def setupClass(cls):
cls.exog = np.ones((1,))
cls.endog = np.ones((1,))
weights = 1
cls.wls_res = WLS(cls.endog, cls.exog, weights=weights).fit()
def test_wrong_size_weights(self):
weights = np.ones((10,10))
assert_raises(ValueError, WLS, self.endog, self.exog, weights=weights)
class TestWLSExogWeights(CheckRegressionResults):
#Test WLS with Greene's credit card data
#reg avgexp age income incomesq ownrent [aw=1/incomesq]
def __init__(self):
from .results.results_regression import CCardWLS
from statsmodels.datasets.ccard import load
dta = load()
dta.exog = add_constant(dta.exog, prepend=False)
nobs = 72.
weights = 1/dta.exog[:,2]
# for comparison with stata analytic weights
scaled_weights = ((weights * nobs)/weights.sum())
self.res1 = WLS(dta.endog, dta.exog, weights=scaled_weights).fit()
self.res2 = CCardWLS()
self.res2.wresid = scaled_weights ** .5 * self.res2.resid
# correction because we use different definition for loglike/llf
corr_ic = 2 * (self.res1.llf - self.res2.llf)
self.res2.aic -= corr_ic
self.res2.bic -= corr_ic
self.res2.llf += 0.5 * np.sum(np.log(self.res1.model.weights))
def test_wls_example():
#example from the docstring, there was a note about a bug, should
#be fixed now
Y = [1,3,4,5,2,3,4]
X = lrange(1,8)
X = add_constant(X, prepend=False)
wls_model = WLS(Y,X, weights=lrange(1,8)).fit()
#taken from R lm.summary
assert_almost_equal(wls_model.fvalue, 0.127337843215, 6)
assert_almost_equal(wls_model.scale, 2.44608530786**2, 6)
def test_wls_tss():
y = np.array([22, 22, 22, 23, 23, 23])
X = [[1, 0], [1, 0], [1, 1], [0, 1], [0, 1], [0, 1]]
ols_mod = OLS(y, add_constant(X, prepend=False)).fit()
yw = np.array([22, 22, 23.])
Xw = [[1,0],[1,1],[0,1]]
w = np.array([2, 1, 3.])
wls_mod = WLS(yw, add_constant(Xw, prepend=False), weights=w).fit()
assert_equal(ols_mod.centered_tss, wls_mod.centered_tss)
class TestWLSScalarVsArray(CheckRegressionResults):
@classmethod
def setupClass(cls):
from statsmodels.datasets.longley import load
dta = load()
dta.exog = add_constant(dta.exog, prepend=True)
wls_scalar = WLS(dta.endog, dta.exog, weights=1./3).fit()
weights = [1/3.] * len(dta.endog)
wls_array = WLS(dta.endog, dta.exog, weights=weights).fit()
cls.res1 = wls_scalar
cls.res2 = wls_array
#class TestWLS_GLS(CheckRegressionResults):
# @classmethod
# def setupClass(cls):
# from statsmodels.datasets.ccard import load
# data = load()
# cls.res1 = WLS(data.endog, data.exog, weights = 1/data.exog[:,2]).fit()
# cls.res2 = GLS(data.endog, data.exog, sigma = data.exog[:,2]).fit()
#
# def check_confidenceintervals(self, conf1, conf2):
# assert_almost_equal(conf1, conf2(), DECIMAL_4)
def test_wls_missing():
from statsmodels.datasets.ccard import load
data = load()
endog = data.endog
endog[[10, 25]] = np.nan
mod = WLS(data.endog, data.exog, weights = 1/data.exog[:,2], missing='drop')
assert_equal(mod.endog.shape[0], 70)
assert_equal(mod.exog.shape[0], 70)
assert_equal(mod.weights.shape[0], 70)
class TestWLS_OLS(CheckRegressionResults):
@classmethod
def setupClass(cls):
data = longley.load()
data.exog = add_constant(data.exog, prepend=False)
cls.res1 = OLS(data.endog, data.exog).fit()
cls.res2 = WLS(data.endog, data.exog).fit()
def check_confidenceintervals(self, conf1, conf2):
assert_almost_equal(conf1, conf2(), DECIMAL_4)
class TestGLS_OLS(CheckRegressionResults):
@classmethod
def setupClass(cls):
data = longley.load()
data.exog = add_constant(data.exog, prepend=False)
cls.res1 = GLS(data.endog, data.exog).fit()
cls.res2 = OLS(data.endog, data.exog).fit()
def check_confidenceintervals(self, conf1, conf2):
assert_almost_equal(conf1, conf2(), DECIMAL_4)
#TODO: test AR
# why the two-stage in AR?
#class test_ar(object):
# from statsmodels.datasets.sunspots import load
# data = load()
# model = AR(data.endog, rho=4).fit()
# R_res = RModel(data.endog, aic="FALSE", order_max=4)
# def test_params(self):
# assert_almost_equal(self.model.rho,
# pass
# def test_order(self):
# In R this can be defined or chosen by minimizing the AIC if aic=True
# pass
class TestYuleWalker(object):
@classmethod
def setupClass(cls):
from statsmodels.datasets.sunspots import load
data = load()
cls.rho, cls.sigma = yule_walker(data.endog, order=4,
method="mle")
cls.R_params = [1.2831003105694765, -0.45240924374091945,
-0.20770298557575195, 0.047943648089542337]
def test_params(self):
assert_almost_equal(self.rho, self.R_params, DECIMAL_4)
class TestDataDimensions(CheckRegressionResults):
@classmethod
def setupClass(cls):
np.random.seed(54321)
cls.endog_n_ = np.random.uniform(0,20,size=30)
cls.endog_n_one = cls.endog_n_[:,None]
cls.exog_n_ = np.random.uniform(0,20,size=30)
cls.exog_n_one = cls.exog_n_[:,None]
cls.degen_exog = cls.exog_n_one[:-1]
cls.mod1 = OLS(cls.endog_n_one, cls.exog_n_one)
cls.mod1.df_model += 1
cls.res1 = cls.mod1.fit()
# Note that these are created for every subclass..
# A little extra overhead probably
cls.mod2 = OLS(cls.endog_n_one, cls.exog_n_one)
cls.mod2.df_model += 1
cls.res2 = cls.mod2.fit()
def check_confidenceintervals(self, conf1, conf2):
assert_almost_equal(conf1, conf2(), DECIMAL_4)
class TestGLS_large_data(TestDataDimensions):
@classmethod
def setupClass(cls):
nobs = 1000
y = np.random.randn(nobs,1)
X = np.random.randn(nobs,20)
sigma = np.ones_like(y)
cls.gls_res = GLS(y, X, sigma=sigma).fit()
cls.gls_res_scalar = GLS(y, X, sigma=1).fit()
cls.gls_res_none= GLS(y, X).fit()
cls.ols_res = OLS(y, X).fit()
def test_large_equal_params(self):
assert_almost_equal(self.ols_res.params, self.gls_res.params, DECIMAL_7)
def test_large_equal_loglike(self):
assert_almost_equal(self.ols_res.llf, self.gls_res.llf, DECIMAL_7)
def test_large_equal_params_none(self):
assert_almost_equal(self.gls_res.params, self.gls_res_none.params,
DECIMAL_7)
class TestNxNx(TestDataDimensions):
@classmethod
def setupClass(cls):
super(TestNxNx, cls).setupClass()
cls.mod2 = OLS(cls.endog_n_, cls.exog_n_)
cls.mod2.df_model += 1
cls.res2 = cls.mod2.fit()
class TestNxOneNx(TestDataDimensions):
@classmethod
def setupClass(cls):
super(TestNxOneNx, cls).setupClass()
cls.mod2 = OLS(cls.endog_n_one, cls.exog_n_)
cls.mod2.df_model += 1
cls.res2 = cls.mod2.fit()
class TestNxNxOne(TestDataDimensions):
@classmethod
def setupClass(cls):
super(TestNxNxOne, cls).setupClass()
cls.mod2 = OLS(cls.endog_n_, cls.exog_n_one)
cls.mod2.df_model += 1
cls.res2 = cls.mod2.fit()
def test_bad_size():
np.random.seed(54321)
data = np.random.uniform(0,20,31)
assert_raises(ValueError, OLS, data, data[1:])
def test_const_indicator():
np.random.seed(12345)
X = np.random.randint(0, 3, size=30)
X = categorical(X, drop=True)
y = np.dot(X, [1., 2., 3.]) + np.random.normal(size=30)
modc = OLS(y, add_constant(X[:,1:], prepend=True)).fit()
mod = OLS(y, X, hasconst=True).fit()
assert_almost_equal(modc.rsquared, mod.rsquared, 12)
def test_706():
# make sure one regressor pandas Series gets passed to DataFrame
# for conf_int.
y = pandas.Series(np.random.randn(10))
x = pandas.Series(np.ones(10))
res = OLS(y,x).fit()
conf_int = res.conf_int()
np.testing.assert_equal(conf_int.shape, (1, 2))
np.testing.assert_(isinstance(conf_int, pandas.DataFrame))
def test_summary():
# test 734
import re
dta = longley.load_pandas()
X = dta.exog
X["constant"] = 1
y = dta.endog
with warnings.catch_warnings(record=True):
res = OLS(y, X).fit()
table = res.summary().as_latex()
# replace the date and time
table = re.sub("(?<=\n\\\\textbf\{Date:\} &).+?&",
" Sun, 07 Apr 2013 &", table)
table = re.sub("(?<=\n\\\\textbf\{Time:\} &).+?&",
" 13:46:07 &", table)
expected = """\\begin{center}
\\begin{tabular}{lclc}
\\toprule
\\textbf{Dep. Variable:} & TOTEMP & \\textbf{ R-squared: } & 0.995 \\\\
\\textbf{Model:} & OLS & \\textbf{ Adj. R-squared: } & 0.992 \\\\
\\textbf{Method:} & Least Squares & \\textbf{ F-statistic: } & 330.3 \\\\
\\textbf{Date:} & Sun, 07 Apr 2013 & \\textbf{ Prob (F-statistic):} & 4.98e-10 \\\\
\\textbf{Time:} & 13:46:07 & \\textbf{ Log-Likelihood: } & -109.62 \\\\
\\textbf{No. Observations:} & 16 & \\textbf{ AIC: } & 233.2 \\\\
\\textbf{Df Residuals:} & 9 & \\textbf{ BIC: } & 238.6 \\\\
\\textbf{Df Model:} & 6 & \\textbf{ } & \\\\
\\bottomrule
\\end{tabular}
\\begin{tabular}{lcccccc}
& \\textbf{coef} & \\textbf{std err} & \\textbf{t} & \\textbf{P$>$$|$t$|$} & \\textbf{[0.025} & \\textbf{0.975]} \\\\
\\midrule
\\textbf{GNPDEFL} & 15.0619 & 84.915 & 0.177 & 0.863 & -177.029 & 207.153 \\\\
\\textbf{GNP} & -0.0358 & 0.033 & -1.070 & 0.313 & -0.112 & 0.040 \\\\
\\textbf{UNEMP} & -2.0202 & 0.488 & -4.136 & 0.003 & -3.125 & -0.915 \\\\
\\textbf{ARMED} & -1.0332 & 0.214 & -4.822 & 0.001 & -1.518 & -0.549 \\\\
\\textbf{POP} & -0.0511 & 0.226 & -0.226 & 0.826 & -0.563 & 0.460 \\\\
\\textbf{YEAR} & 1829.1515 & 455.478 & 4.016 & 0.003 & 798.788 & 2859.515 \\\\
\\textbf{constant} & -3.482e+06 & 8.9e+05 & -3.911 & 0.004 & -5.5e+06 & -1.47e+06 \\\\
\\bottomrule
\\end{tabular}
\\begin{tabular}{lclc}
\\textbf{Omnibus:} & 0.749 & \\textbf{ Durbin-Watson: } & 2.559 \\\\
\\textbf{Prob(Omnibus):} & 0.688 & \\textbf{ Jarque-Bera (JB): } & 0.684 \\\\
\\textbf{Skew:} & 0.420 & \\textbf{ Prob(JB): } & 0.710 \\\\
\\textbf{Kurtosis:} & 2.434 & \\textbf{ Cond. No. } & 4.86e+09 \\\\
\\bottomrule
\\end{tabular}
%\\caption{OLS Regression Results}
\\end{center}"""
assert_equal(table, expected)
class TestRegularizedFit(object):
# Make sure there are no issues when there are no selected
# variables.
def test_empty_model(self):
np.random.seed(742)
n = 100
endog = np.random.normal(size=n)
exog = np.random.normal(size=(n, 3))
model = OLS(endog, exog)
result = model.fit_regularized(alpha=1000)
assert_equal(result.params, 0.)
assert_equal(result.bse, 0.)
def test_regularized(self):
import os
from . import glmnet_r_results
cur_dir = os.path.dirname(os.path.abspath(__file__))
data = np.loadtxt(os.path.join(cur_dir, "results", "lasso_data.csv"),
delimiter=",")
tests = [x for x in dir(glmnet_r_results) if x.startswith("rslt_")]
for test in tests:
vec = getattr(glmnet_r_results, test)
n = vec[0]
p = vec[1]
L1_wt = float(vec[2])
lam = float(vec[3])
params = vec[4:].astype(np.float64)
endog = data[0:int(n), 0]
exog = data[0:int(n), 1:(int(p)+1)]
endog = endog - endog.mean()
endog /= endog.std(ddof=1)
exog = exog - exog.mean(0)
exog /= exog.std(0, ddof=1)
mod = OLS(endog, exog)
rslt = mod.fit_regularized(L1_wt=L1_wt, alpha=lam)
assert_almost_equal(rslt.params, params, decimal=3)
# Smoke test for summary
smry = rslt.summary()
def test_formula_missing_cat():
# gh-805
import statsmodels.api as sm
from statsmodels.formula.api import ols
from patsy import PatsyError
dta = sm.datasets.grunfeld.load_pandas().data
dta.ix[0, 'firm'] = np.nan
mod = ols(formula='value ~ invest + capital + firm + year',
data=dta.dropna())
res = mod.fit()
mod2 = ols(formula='value ~ invest + capital + firm + year',
data=dta)
res2 = mod2.fit()
assert_almost_equal(res.params.values, res2.params.values)
assert_raises(PatsyError, ols, 'value ~ invest + capital + firm + year',
data=dta, missing='raise')
def test_missing_formula_predict():
# see 2171
nsample = 30
data = pandas.DataFrame({'x': np.linspace(0, 10, nsample)})
null = pandas.DataFrame({'x': np.array([np.nan])})
data = pandas.concat([data, null])
beta = np.array([1, 0.1])
e = np.random.normal(size=nsample+1)
data['y'] = beta[0] + beta[1] * data['x'] + e
model = OLS.from_formula('y ~ x', data=data)
fit = model.fit()
pred = fit.predict(exog=data[:-1])
def test_fvalue_implicit_constant():
nobs = 100
np.random.seed(2)
x = np.random.randn(nobs, 1)
x = ((x > 0) == [True, False]).astype(int)
y = x.sum(1) + np.random.randn(nobs)
w = 1 + 0.25 * np.random.rand(nobs)
from statsmodels.regression.linear_model import OLS, WLS
res = OLS(y, x).fit(cov_type='HC1')
assert_(np.isnan(res.fvalue))
assert_(np.isnan(res.f_pvalue))
res.summary()
res = WLS(y, x).fit(cov_type='HC1')
assert_(np.isnan(res.fvalue))
assert_(np.isnan(res.f_pvalue))
res.summary()
if __name__=="__main__":
import nose
# run_module_suite()
nose.runmodule(argv=[__file__,'-vvs','-x','--pdb', '--pdb-failure'],
exit=False)
# nose.runmodule(argv=[__file__,'-vvs','-x'], exit=False) #, '--pdb'
| bsd-3-clause |
serendio-labs/diskoveror-ta | src/main/python/Topics/Seeds22/labeledCluster.py | 3 | 3944 | '''
Copyright 2015 Serendio Inc.
Author - Satish Palaniappan
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing,
software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and limitations under the License.
'''
__author__ = "Satish Palaniappan"
import numpy as np
from sklearn import cluster, datasets, preprocessing
import pickle
import gensim
import time
import re
import tokenize
from scipy import spatial
def save_obj(obj, name ):
with open( name + '.pkl', 'wb') as f:
pickle.dump(obj, f, protocol=2)
def load_obj(name ):
with open( name + '.pkl', 'rb') as f:
return pickle.load(f)
# 3M word google dataset of pretrained 300D vectors
model = gensim.models.Word2Vec.load_word2vec_format('vectors.bin', binary=True)
model.init_sims(replace=True)
#### getting all vecs from w2v using the inbuilt syn0 list see code
# X = model.syn0
# ### scaling feature vecs
# min_max_scaler = preprocessing.MinMaxScaler()
# X_Scaled_Feature_Vecs = min_max_scaler.fit_transform(X)
# X_Scaled_Feature_Vecs = X
# W2V = dict(zip(model.vocab, X_Scaled_Feature_Vecs))
#Cosine Distance
# from scipy import spatial
# dataSetI = model["travel"]
# dataSetII = model["travelling"]
# result = 1 - spatial.distance.cosine(dataSetI, dataSetII)
# print(result)
X_Scaled_Feature_Vecs=[]
for word in model.vocab:
X_Scaled_Feature_Vecs.append(model[word])
# ######## Interested Categories
cat = [
"advertising",
"beauty",
"business",
"celebrity",
"diy craft",
"entertainment",
"family",
"fashion",
"food",
"general",
"health",
"lifestyle",
"music",
"news",
"pop",
"culture",
"social",
"media",
"sports",
"technology",
"travel",
"video games"
]
nums = range(0,22)
num2cat = dict(zip(nums, cat))
catVec=[]
# load from C file output
for c in cat:
try:
catVec.append(model[c.lower()])
except:
words = c.split()
A = np.add(np.array(model[words[0].lower()]),np.array(model[words[1].lower()]))
M = np.multiply(A,A)
lent=0
for i in M:
lent+=i
V = np.divide(A,lent)
catVec.append(list(V))
# kmeans
##### better code
t0 = time.time()
# Assign Max_Iter to 1 (ONE) if u just want to fit vectors around seeds
kmeans = cluster.KMeans(n_clusters=22, init=np.array(catVec), max_iter=1).fit(X_Scaled_Feature_Vecs)
#kmeans = cluster.KMeans(n_clusters=22, init=np.array(catVec), max_iter=900).fit(X_Scaled_Feature_Vecs)
print(str(time.time()-t0))
print(kmeans.inertia_)
###### After Fiting the Cluster Centers are recomputed : update catVec (Order Preserved)
catVec = kmeans.cluster_centers_
# #test
# for c in catVec:
# print(num2cat[kmeans.predict(c)[0]])
##### save best for future use
save_obj(kmeans,"clusterSmall")
KM = load_obj("clusterSmall")
# Cluster_lookUP = dict(zip(model.vocab, KM.labels_))
Cluster_lookUP = dict()
for word in model.vocab:
Cluster_lookUP[word] = KM.predict(model[word])[0]
## Precomputing the cosine similarities
Cosine_Similarity = dict()
for k in Cluster_lookUP.keys():
Cosine_Similarity[k] = 1 - spatial.distance.cosine(model[k], catVec[Cluster_lookUP[k]])
#check
print(num2cat[Cluster_lookUP["flight"]] + " "+str(Cosine_Similarity["flight"]))
print(num2cat[Cluster_lookUP["gamecube"]] +" "+str(Cosine_Similarity["gamecube"]))
#Saving Models
save_obj(Cluster_lookUP,"Cluster_lookUP")
save_obj(Cosine_Similarity,"Cosine_Similarity")
save_obj(num2cat,"num2cat")
save_obj(catVec,"catVec")
| apache-2.0 |
Vimos/scikit-learn | sklearn/decomposition/tests/test_pca.py | 9 | 21107 | import numpy as np
import scipy as sp
from itertools import product
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_true
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_greater
from sklearn.utils.testing import assert_raises
from sklearn.utils.testing import assert_no_warnings
from sklearn.utils.testing import assert_warns_message
from sklearn.utils.testing import ignore_warnings
from sklearn.utils.testing import assert_less
from sklearn import datasets
from sklearn.decomposition import PCA
from sklearn.decomposition import RandomizedPCA
from sklearn.decomposition.pca import _assess_dimension_
from sklearn.decomposition.pca import _infer_dimension_
iris = datasets.load_iris()
solver_list = ['full', 'arpack', 'randomized', 'auto']
def test_pca():
# PCA on dense arrays
X = iris.data
for n_comp in np.arange(X.shape[1]):
pca = PCA(n_components=n_comp, svd_solver='full')
X_r = pca.fit(X).transform(X)
np.testing.assert_equal(X_r.shape[1], n_comp)
X_r2 = pca.fit_transform(X)
assert_array_almost_equal(X_r, X_r2)
X_r = pca.transform(X)
X_r2 = pca.fit_transform(X)
assert_array_almost_equal(X_r, X_r2)
# Test get_covariance and get_precision
cov = pca.get_covariance()
precision = pca.get_precision()
assert_array_almost_equal(np.dot(cov, precision),
np.eye(X.shape[1]), 12)
# test explained_variance_ratio_ == 1 with all components
pca = PCA(svd_solver='full')
pca.fit(X)
assert_almost_equal(pca.explained_variance_ratio_.sum(), 1.0, 3)
def test_pca_arpack_solver():
# PCA on dense arrays
X = iris.data
d = X.shape[1]
# Loop excluding the extremes, invalid inputs for arpack
for n_comp in np.arange(1, d):
pca = PCA(n_components=n_comp, svd_solver='arpack', random_state=0)
X_r = pca.fit(X).transform(X)
np.testing.assert_equal(X_r.shape[1], n_comp)
X_r2 = pca.fit_transform(X)
assert_array_almost_equal(X_r, X_r2)
X_r = pca.transform(X)
assert_array_almost_equal(X_r, X_r2)
# Test get_covariance and get_precision
cov = pca.get_covariance()
precision = pca.get_precision()
assert_array_almost_equal(np.dot(cov, precision),
np.eye(d), 12)
pca = PCA(n_components=0, svd_solver='arpack', random_state=0)
assert_raises(ValueError, pca.fit, X)
# Check internal state
assert_equal(pca.n_components,
PCA(n_components=0,
svd_solver='arpack', random_state=0).n_components)
assert_equal(pca.svd_solver,
PCA(n_components=0,
svd_solver='arpack', random_state=0).svd_solver)
pca = PCA(n_components=d, svd_solver='arpack', random_state=0)
assert_raises(ValueError, pca.fit, X)
assert_equal(pca.n_components,
PCA(n_components=d,
svd_solver='arpack', random_state=0).n_components)
assert_equal(pca.svd_solver,
PCA(n_components=0,
svd_solver='arpack', random_state=0).svd_solver)
def test_pca_randomized_solver():
# PCA on dense arrays
X = iris.data
# Loop excluding the 0, invalid for randomized
for n_comp in np.arange(1, X.shape[1]):
pca = PCA(n_components=n_comp, svd_solver='randomized', random_state=0)
X_r = pca.fit(X).transform(X)
np.testing.assert_equal(X_r.shape[1], n_comp)
X_r2 = pca.fit_transform(X)
assert_array_almost_equal(X_r, X_r2)
X_r = pca.transform(X)
assert_array_almost_equal(X_r, X_r2)
# Test get_covariance and get_precision
cov = pca.get_covariance()
precision = pca.get_precision()
assert_array_almost_equal(np.dot(cov, precision),
np.eye(X.shape[1]), 12)
pca = PCA(n_components=0, svd_solver='randomized', random_state=0)
assert_raises(ValueError, pca.fit, X)
pca = PCA(n_components=0, svd_solver='randomized', random_state=0)
assert_raises(ValueError, pca.fit, X)
# Check internal state
assert_equal(pca.n_components,
PCA(n_components=0,
svd_solver='randomized', random_state=0).n_components)
assert_equal(pca.svd_solver,
PCA(n_components=0,
svd_solver='randomized', random_state=0).svd_solver)
def test_no_empty_slice_warning():
# test if we avoid numpy warnings for computing over empty arrays
n_components = 10
n_features = n_components + 2 # anything > n_comps triggered it in 0.16
X = np.random.uniform(-1, 1, size=(n_components, n_features))
pca = PCA(n_components=n_components)
assert_no_warnings(pca.fit, X)
def test_whitening():
# Check that PCA output has unit-variance
rng = np.random.RandomState(0)
n_samples = 100
n_features = 80
n_components = 30
rank = 50
# some low rank data with correlated features
X = np.dot(rng.randn(n_samples, rank),
np.dot(np.diag(np.linspace(10.0, 1.0, rank)),
rng.randn(rank, n_features)))
# the component-wise variance of the first 50 features is 3 times the
# mean component-wise variance of the remaining 30 features
X[:, :50] *= 3
assert_equal(X.shape, (n_samples, n_features))
# the component-wise variance is thus highly varying:
assert_greater(X.std(axis=0).std(), 43.8)
for solver, copy in product(solver_list, (True, False)):
# whiten the data while projecting to the lower dim subspace
X_ = X.copy() # make sure we keep an original across iterations.
pca = PCA(n_components=n_components, whiten=True, copy=copy,
svd_solver=solver, random_state=0, iterated_power=7)
# test fit_transform
X_whitened = pca.fit_transform(X_.copy())
assert_equal(X_whitened.shape, (n_samples, n_components))
X_whitened2 = pca.transform(X_)
assert_array_almost_equal(X_whitened, X_whitened2)
assert_almost_equal(X_whitened.std(axis=0), np.ones(n_components),
decimal=6)
assert_almost_equal(X_whitened.mean(axis=0), np.zeros(n_components))
X_ = X.copy()
pca = PCA(n_components=n_components, whiten=False, copy=copy,
svd_solver=solver).fit(X_)
X_unwhitened = pca.transform(X_)
assert_equal(X_unwhitened.shape, (n_samples, n_components))
# in that case the output components still have varying variances
assert_almost_equal(X_unwhitened.std(axis=0).std(), 74.1, 1)
# we always center, so no test for non-centering.
# Ignore warnings from switching to more power iterations in randomized_svd
@ignore_warnings
def test_explained_variance():
# Check that PCA output has unit-variance
rng = np.random.RandomState(0)
n_samples = 100
n_features = 80
X = rng.randn(n_samples, n_features)
pca = PCA(n_components=2, svd_solver='full').fit(X)
apca = PCA(n_components=2, svd_solver='arpack', random_state=0).fit(X)
assert_array_almost_equal(pca.explained_variance_,
apca.explained_variance_, 1)
assert_array_almost_equal(pca.explained_variance_ratio_,
apca.explained_variance_ratio_, 3)
rpca = PCA(n_components=2, svd_solver='randomized', random_state=42).fit(X)
assert_array_almost_equal(pca.explained_variance_,
rpca.explained_variance_, 1)
assert_array_almost_equal(pca.explained_variance_ratio_,
rpca.explained_variance_ratio_, 1)
# compare to empirical variances
X_pca = pca.transform(X)
assert_array_almost_equal(pca.explained_variance_,
np.var(X_pca, axis=0))
X_pca = apca.transform(X)
assert_array_almost_equal(apca.explained_variance_,
np.var(X_pca, axis=0))
X_rpca = rpca.transform(X)
assert_array_almost_equal(rpca.explained_variance_, np.var(X_rpca, axis=0),
decimal=1)
# Same with correlated data
X = datasets.make_classification(n_samples, n_features,
n_informative=n_features-2,
random_state=rng)[0]
pca = PCA(n_components=2).fit(X)
rpca = PCA(n_components=2, svd_solver='randomized',
random_state=rng).fit(X)
assert_array_almost_equal(pca.explained_variance_ratio_,
rpca.explained_variance_ratio_, 5)
def test_singular_values():
# Check that the PCA output has the correct singular values
rng = np.random.RandomState(0)
n_samples = 100
n_features = 80
X = rng.randn(n_samples, n_features)
pca = PCA(n_components=2, svd_solver='full',
random_state=rng).fit(X)
apca = PCA(n_components=2, svd_solver='arpack',
random_state=rng).fit(X)
rpca = PCA(n_components=2, svd_solver='randomized',
random_state=rng).fit(X)
assert_array_almost_equal(pca.singular_values_, apca.singular_values_, 12)
assert_array_almost_equal(pca.singular_values_, rpca.singular_values_, 1)
assert_array_almost_equal(apca.singular_values_, rpca.singular_values_, 1)
# Compare to the Frobenius norm
X_pca = pca.transform(X)
X_apca = apca.transform(X)
X_rpca = rpca.transform(X)
assert_array_almost_equal(np.sum(pca.singular_values_**2.0),
np.linalg.norm(X_pca, "fro")**2.0, 12)
assert_array_almost_equal(np.sum(apca.singular_values_**2.0),
np.linalg.norm(X_apca, "fro")**2.0, 12)
assert_array_almost_equal(np.sum(rpca.singular_values_**2.0),
np.linalg.norm(X_rpca, "fro")**2.0, 0)
# Compare to the 2-norms of the score vectors
assert_array_almost_equal(pca.singular_values_,
np.sqrt(np.sum(X_pca**2.0, axis=0)), 12)
assert_array_almost_equal(apca.singular_values_,
np.sqrt(np.sum(X_apca**2.0, axis=0)), 12)
assert_array_almost_equal(rpca.singular_values_,
np.sqrt(np.sum(X_rpca**2.0, axis=0)), 2)
# Set the singular values and see what we get back
rng = np.random.RandomState(0)
n_samples = 100
n_features = 110
X = rng.randn(n_samples, n_features)
pca = PCA(n_components=3, svd_solver='full', random_state=rng)
apca = PCA(n_components=3, svd_solver='arpack', random_state=rng)
rpca = PCA(n_components=3, svd_solver='randomized', random_state=rng)
X_pca = pca.fit_transform(X)
X_pca /= np.sqrt(np.sum(X_pca**2.0, axis=0))
X_pca[:, 0] *= 3.142
X_pca[:, 1] *= 2.718
X_hat = np.dot(X_pca, pca.components_)
pca.fit(X_hat)
apca.fit(X_hat)
rpca.fit(X_hat)
assert_array_almost_equal(pca.singular_values_, [3.142, 2.718, 1.0], 14)
assert_array_almost_equal(apca.singular_values_, [3.142, 2.718, 1.0], 14)
assert_array_almost_equal(rpca.singular_values_, [3.142, 2.718, 1.0], 14)
def test_pca_check_projection():
# Test that the projection of data is correct
rng = np.random.RandomState(0)
n, p = 100, 3
X = rng.randn(n, p) * .1
X[:10] += np.array([3, 4, 5])
Xt = 0.1 * rng.randn(1, p) + np.array([3, 4, 5])
for solver in solver_list:
Yt = PCA(n_components=2, svd_solver=solver).fit(X).transform(Xt)
Yt /= np.sqrt((Yt ** 2).sum())
assert_almost_equal(np.abs(Yt[0][0]), 1., 1)
def test_pca_inverse():
# Test that the projection of data can be inverted
rng = np.random.RandomState(0)
n, p = 50, 3
X = rng.randn(n, p) # spherical data
X[:, 1] *= .00001 # make middle component relatively small
X += [5, 4, 3] # make a large mean
# same check that we can find the original data from the transformed
# signal (since the data is almost of rank n_components)
pca = PCA(n_components=2, svd_solver='full').fit(X)
Y = pca.transform(X)
Y_inverse = pca.inverse_transform(Y)
assert_almost_equal(X, Y_inverse, decimal=3)
# same as above with whitening (approximate reconstruction)
for solver in solver_list:
pca = PCA(n_components=2, whiten=True, svd_solver=solver)
pca.fit(X)
Y = pca.transform(X)
Y_inverse = pca.inverse_transform(Y)
assert_almost_equal(X, Y_inverse, decimal=3)
def test_pca_validation():
X = [[0, 1], [1, 0]]
for solver in solver_list:
for n_components in [-1, 3]:
assert_raises(ValueError,
PCA(n_components, svd_solver=solver).fit, X)
def test_randomized_pca_check_projection():
# Test that the projection by randomized PCA on dense data is correct
rng = np.random.RandomState(0)
n, p = 100, 3
X = rng.randn(n, p) * .1
X[:10] += np.array([3, 4, 5])
Xt = 0.1 * rng.randn(1, p) + np.array([3, 4, 5])
Yt = PCA(n_components=2, svd_solver='randomized',
random_state=0).fit(X).transform(Xt)
Yt /= np.sqrt((Yt ** 2).sum())
assert_almost_equal(np.abs(Yt[0][0]), 1., 1)
def test_randomized_pca_check_list():
# Test that the projection by randomized PCA on list data is correct
X = [[1.0, 0.0], [0.0, 1.0]]
X_transformed = PCA(n_components=1, svd_solver='randomized',
random_state=0).fit(X).transform(X)
assert_equal(X_transformed.shape, (2, 1))
assert_almost_equal(X_transformed.mean(), 0.00, 2)
assert_almost_equal(X_transformed.std(), 0.71, 2)
def test_randomized_pca_inverse():
# Test that randomized PCA is inversible on dense data
rng = np.random.RandomState(0)
n, p = 50, 3
X = rng.randn(n, p) # spherical data
X[:, 1] *= .00001 # make middle component relatively small
X += [5, 4, 3] # make a large mean
# same check that we can find the original data from the transformed signal
# (since the data is almost of rank n_components)
pca = PCA(n_components=2, svd_solver='randomized', random_state=0).fit(X)
Y = pca.transform(X)
Y_inverse = pca.inverse_transform(Y)
assert_almost_equal(X, Y_inverse, decimal=2)
# same as above with whitening (approximate reconstruction)
pca = PCA(n_components=2, whiten=True, svd_solver='randomized',
random_state=0).fit(X)
Y = pca.transform(X)
Y_inverse = pca.inverse_transform(Y)
relative_max_delta = (np.abs(X - Y_inverse) / np.abs(X).mean()).max()
assert_less(relative_max_delta, 1e-5)
def test_pca_dim():
# Check automated dimensionality setting
rng = np.random.RandomState(0)
n, p = 100, 5
X = rng.randn(n, p) * .1
X[:10] += np.array([3, 4, 5, 1, 2])
pca = PCA(n_components='mle', svd_solver='full').fit(X)
assert_equal(pca.n_components, 'mle')
assert_equal(pca.n_components_, 1)
def test_infer_dim_1():
# TODO: explain what this is testing
# Or at least use explicit variable names...
n, p = 1000, 5
rng = np.random.RandomState(0)
X = (rng.randn(n, p) * .1 + rng.randn(n, 1) * np.array([3, 4, 5, 1, 2]) +
np.array([1, 0, 7, 4, 6]))
pca = PCA(n_components=p, svd_solver='full')
pca.fit(X)
spect = pca.explained_variance_
ll = []
for k in range(p):
ll.append(_assess_dimension_(spect, k, n, p))
ll = np.array(ll)
assert_greater(ll[1], ll.max() - .01 * n)
def test_infer_dim_2():
# TODO: explain what this is testing
# Or at least use explicit variable names...
n, p = 1000, 5
rng = np.random.RandomState(0)
X = rng.randn(n, p) * .1
X[:10] += np.array([3, 4, 5, 1, 2])
X[10:20] += np.array([6, 0, 7, 2, -1])
pca = PCA(n_components=p, svd_solver='full')
pca.fit(X)
spect = pca.explained_variance_
assert_greater(_infer_dimension_(spect, n, p), 1)
def test_infer_dim_3():
n, p = 100, 5
rng = np.random.RandomState(0)
X = rng.randn(n, p) * .1
X[:10] += np.array([3, 4, 5, 1, 2])
X[10:20] += np.array([6, 0, 7, 2, -1])
X[30:40] += 2 * np.array([-1, 1, -1, 1, -1])
pca = PCA(n_components=p, svd_solver='full')
pca.fit(X)
spect = pca.explained_variance_
assert_greater(_infer_dimension_(spect, n, p), 2)
def test_infer_dim_by_explained_variance():
X = iris.data
pca = PCA(n_components=0.95, svd_solver='full')
pca.fit(X)
assert_equal(pca.n_components, 0.95)
assert_equal(pca.n_components_, 2)
pca = PCA(n_components=0.01, svd_solver='full')
pca.fit(X)
assert_equal(pca.n_components, 0.01)
assert_equal(pca.n_components_, 1)
rng = np.random.RandomState(0)
# more features than samples
X = rng.rand(5, 20)
pca = PCA(n_components=.5, svd_solver='full').fit(X)
assert_equal(pca.n_components, 0.5)
assert_equal(pca.n_components_, 2)
def test_pca_score():
# Test that probabilistic PCA scoring yields a reasonable score
n, p = 1000, 3
rng = np.random.RandomState(0)
X = rng.randn(n, p) * .1 + np.array([3, 4, 5])
for solver in solver_list:
pca = PCA(n_components=2, svd_solver=solver)
pca.fit(X)
ll1 = pca.score(X)
h = -0.5 * np.log(2 * np.pi * np.exp(1) * 0.1 ** 2) * p
np.testing.assert_almost_equal(ll1 / h, 1, 0)
def test_pca_score2():
# Test that probabilistic PCA correctly separated different datasets
n, p = 100, 3
rng = np.random.RandomState(0)
X = rng.randn(n, p) * .1 + np.array([3, 4, 5])
for solver in solver_list:
pca = PCA(n_components=2, svd_solver=solver)
pca.fit(X)
ll1 = pca.score(X)
ll2 = pca.score(rng.randn(n, p) * .2 + np.array([3, 4, 5]))
assert_greater(ll1, ll2)
# Test that it gives different scores if whiten=True
pca = PCA(n_components=2, whiten=True, svd_solver=solver)
pca.fit(X)
ll2 = pca.score(X)
assert_true(ll1 > ll2)
def test_pca_score3():
# Check that probabilistic PCA selects the right model
n, p = 200, 3
rng = np.random.RandomState(0)
Xl = (rng.randn(n, p) + rng.randn(n, 1) * np.array([3, 4, 5]) +
np.array([1, 0, 7]))
Xt = (rng.randn(n, p) + rng.randn(n, 1) * np.array([3, 4, 5]) +
np.array([1, 0, 7]))
ll = np.zeros(p)
for k in range(p):
pca = PCA(n_components=k, svd_solver='full')
pca.fit(Xl)
ll[k] = pca.score(Xt)
assert_true(ll.argmax() == 1)
def test_svd_solver_auto():
rng = np.random.RandomState(0)
X = rng.uniform(size=(1000, 50))
# case: n_components in (0,1) => 'full'
pca = PCA(n_components=.5)
pca.fit(X)
pca_test = PCA(n_components=.5, svd_solver='full')
pca_test.fit(X)
assert_array_almost_equal(pca.components_, pca_test.components_)
# case: max(X.shape) <= 500 => 'full'
pca = PCA(n_components=5, random_state=0)
Y = X[:10, :]
pca.fit(Y)
pca_test = PCA(n_components=5, svd_solver='full', random_state=0)
pca_test.fit(Y)
assert_array_almost_equal(pca.components_, pca_test.components_)
# case: n_components >= .8 * min(X.shape) => 'full'
pca = PCA(n_components=50)
pca.fit(X)
pca_test = PCA(n_components=50, svd_solver='full')
pca_test.fit(X)
assert_array_almost_equal(pca.components_, pca_test.components_)
# n_components >= 1 and n_components < .8 * min(X.shape) => 'randomized'
pca = PCA(n_components=10, random_state=0)
pca.fit(X)
pca_test = PCA(n_components=10, svd_solver='randomized', random_state=0)
pca_test.fit(X)
assert_array_almost_equal(pca.components_, pca_test.components_)
def test_deprecation_randomized_pca():
rng = np.random.RandomState(0)
X = rng.random_sample((5, 4))
depr_message = ("Class RandomizedPCA is deprecated; RandomizedPCA was "
"deprecated in 0.18 and will be "
"removed in 0.20. Use PCA(svd_solver='randomized') "
"instead. The new implementation DOES NOT store "
"whiten ``components_``. Apply transform to get them.")
def fit_deprecated(X):
global Y
rpca = RandomizedPCA(random_state=0)
Y = rpca.fit_transform(X)
assert_warns_message(DeprecationWarning, depr_message, fit_deprecated, X)
Y_pca = PCA(svd_solver='randomized', random_state=0).fit_transform(X)
assert_array_almost_equal(Y, Y_pca)
def test_pca_sparse_input():
X = np.random.RandomState(0).rand(5, 4)
X = sp.sparse.csr_matrix(X)
assert(sp.sparse.issparse(X))
for svd_solver in solver_list:
pca = PCA(n_components=3, svd_solver=svd_solver)
assert_raises(TypeError, pca.fit, X)
def test_pca_bad_solver():
X = np.random.RandomState(0).rand(5, 4)
pca = PCA(n_components=3, svd_solver='bad_argument')
assert_raises(ValueError, pca.fit, X)
| bsd-3-clause |
daniaki/pyPPI | pyppi/models/binary_relevance.py | 1 | 8033 | """
A classifier implementing the Binary Relevance approach to multi-label
learning.
"""
__all__ = [
"MixedBinaryRelevanceClassifier"
]
import logging
import numpy as np
from joblib import Parallel, delayed
from sklearn.base import clone, BaseEstimator
from sklearn.metrics import (
hamming_loss, label_ranking_loss, f1_score,
precision_score, recall_score
)
from pyppi.model_selection.scoring import fdr_score, specificity
logger = logging.getLogger("pyppi")
def _fit_label(estimator, X, y, label_idx, n_labels, verbose, **fit_params):
if verbose:
logger.info("Fitting label {}/{}.".format(label_idx+1, n_labels))
return estimator.fit(X, y, **fit_params)
def _predict_proba_label(estimator, X):
return estimator.predict_proba(X)
def _predict_label(estimator, X):
return estimator.predict(X)
class MixedBinaryRelevanceClassifier(object):
"""Mimics the `OneVsRest` classifier from Sklearn allowing
a different type of classifier for each label as opposed to one classifier
for all labels.
Parameters:
----------
estimators : `list`
List of `Scikit-Learn` estimators supporting `fit`, `predict` and
`predict_proba`.
n_jobs : int, optional, default: 1
Number of processes to use when fitting each label.
verbose : bool, optional, default: False
Logs messages regarding fitting progress.
"""
def __init__(self, estimators, n_jobs=1, verbose=False):
if not isinstance(estimators, list):
raise TypeError("estimators must be a list.")
self.estimators = estimators
self.n_jobs = n_jobs
self.verbose = verbose
def __repr__(self):
return (
"MixedBinaryRelevanceClassifier(estimators={}, n_jobs={})".format(
self.estimators, self.n_jobs
)
)
def _check_y_shape(self, y):
try:
if y.shape[1] <= 1:
raise ValueError(
"y must be in multi-label indicator matrix format. "
"For binary or multi-class classification use scikit-learn."
)
if y.shape[1] != len(self.estimators):
raise ValueError(
"Shape of y {} along dim 1 does not match {}.".format(
y.shape, len(self.estimators)
)
)
except IndexError:
raise ValueError(
"y must be in multi-label indicator matrix format. "
"For binary or multi-class classification use scikit-learn."
)
def clone(self, deep=True):
params = self.get_params(deep)
return self.__class__(**params)
def _check_fitted(self):
if not hasattr(self, 'estimators_'):
raise ValueError("This estimator has not yet been fit.")
if not hasattr(self, 'n_labels_'):
raise ValueError("This estimator has not yet been fit.")
def get_params(self, deep=True):
return {
"estimators": [clone(e) for e in self.estimators],
"n_jobs": self.n_jobs,
"verbose": self.verbose
}
def set_params(self, **params):
for key, value in params.items():
if key not in self.get_params().keys():
raise ValueError(
"'{}' is not a valid param for {}.".format(
key, self.__class__.__name__
)
)
elif key == 'estimators':
if not isinstance(value, list):
raise TypeError("'estimators' must be a list.")
self.estimators = [clone(e) for e in value]
if hasattr(self, 'n_labels_'):
delattr(self, 'n_labels_')
if hasattr(self, 'estimators_'):
delattr(self, 'estimators_')
else:
setattr(self, key, value)
return self
def fit(self, X, y, **fit_params):
"""
Fit the model according to the given training data.
Parameters
----------
X : {array-like, sparse matrix}, shape (n_samples, n_features)
Training vector, where n_samples is the number of samples and
n_features is the number of features.
y : array-like, shape (n_samples, n_labels)
Target vector relative to X.
Returns
-------
self : object
Returns self.
"""
self._check_y_shape(y)
n_labels = len(self.estimators)
self.estimators_ = Parallel(n_jobs=self.n_jobs)(
delayed(_fit_label)(
estimator=clone(estimator),
X=X, y=y[:, i], label_idx=i,
n_labels=n_labels, verbose=self.verbose,
**fit_params
)
for i, estimator in enumerate(self.estimators)
)
self.n_labels_ = len(self.estimators_)
return self
def predict(self, X):
"""
Predict class labels for samples in X.
Parameters
----------
X : {array-like, sparse matrix}, shape = (n_samples, n_features)
Samples.
Returns
-------
C : array, shape = (n_samples, n_labels)
Predicted class labels per sample.
"""
self._check_fitted()
predictions = np.vstack(Parallel(n_jobs=self.n_jobs)(
delayed(_predict_label)(
estimator=estimator, X=X
)
for estimator in self.estimators_
)).T
return predictions
def predict_proba(self, X):
"""
Probability estimates for each label.
Parameters
----------
X : array-like, shape = (n_samples, n_features)
Returns
-------
T : array-like, shape = (n_samples, n_labels)
Returns the probability of the sample for each label in the model,
where labels are ordered as the indices of 'y' used during fit.
"""
self._check_fitted()
probas = Parallel(n_jobs=self.n_jobs)(
delayed(_predict_proba_label)(
estimator=estimator, X=X
)
for estimator in self.estimators_
)
probas = np.vstack([x[:, 1] for x in probas]).T
return probas
def score(self, X, y, sample_weight=None, use_proba=False,
scorer=hamming_loss, **score_params):
"""
Returns the score as determined by `scoring` on the given
test data and labels.
Parameters
----------
X : array-like, shape = (n_samples, n_features)
Test samples.
y : array-like, shape = (n_samples, n_labels)
True labels for X.
sample_weight : array-like, shape = [n_samples], optional
Sample weights.
use_proba : boolean, default: False
If True, apply scoring function to probability estimates.
scorer : function, optional
The scoring method to apply to predictions.
score_params : dict, optional
Keyword arguments for scorer.
Returns
-------
`float` or array-like (n_labels, ) if scoring uses binary.
Mean score of self.predict(X) wrt. y.
"""
self._check_y_shape(y)
self._check_fitted()
if use_proba:
y_pred = self.predict_proba(X)
else:
y_pred = self.predict(X)
average = score_params.get("average", None)
if average == "binary":
return np.asarray([
scorer(
y[:, i], y_pred[:, i],
sample_weight=sample_weight,
**score_params
)
for i in range(self.n_labels_)
])
else:
return scorer(
y, y_pred, sample_weight=sample_weight,
**score_params
)
| mit |
hitszxp/scikit-learn | sklearn/tree/export.py | 30 | 4529 | """
This module defines export functions for decision trees.
"""
# Authors: Gilles Louppe <[email protected]>
# Peter Prettenhofer <[email protected]>
# Brian Holt <[email protected]>
# Noel Dawe <[email protected]>
# Satrajit Gosh <[email protected]>
# Licence: BSD 3 clause
from ..externals import six
from . import _tree
def export_graphviz(decision_tree, out_file="tree.dot", feature_names=None,
max_depth=None):
"""Export a decision tree in DOT format.
This function generates a GraphViz representation of the decision tree,
which is then written into `out_file`. Once exported, graphical renderings
can be generated using, for example::
$ dot -Tps tree.dot -o tree.ps (PostScript format)
$ dot -Tpng tree.dot -o tree.png (PNG format)
The sample counts that are shown are weighted with any sample_weights that
might be present.
Parameters
----------
decision_tree : decision tree classifier
The decision tree to be exported to GraphViz.
out_file : file object or string, optional (default="tree.dot")
Handle or name of the output file.
feature_names : list of strings, optional (default=None)
Names of each of the features.
max_depth : int, optional (default=None)
The maximum depth of the representation. If None, the tree is fully
generated.
Examples
--------
>>> from sklearn.datasets import load_iris
>>> from sklearn import tree
>>> clf = tree.DecisionTreeClassifier()
>>> iris = load_iris()
>>> clf = clf.fit(iris.data, iris.target)
>>> tree.export_graphviz(clf,
... out_file='tree.dot') # doctest: +SKIP
"""
def node_to_str(tree, node_id, criterion):
if not isinstance(criterion, six.string_types):
criterion = "impurity"
value = tree.value[node_id]
if tree.n_outputs == 1:
value = value[0, :]
if tree.children_left[node_id] == _tree.TREE_LEAF:
return "%s = %.4f\\nsamples = %s\\nvalue = %s" \
% (criterion,
tree.impurity[node_id],
tree.n_node_samples[node_id],
value)
else:
if feature_names is not None:
feature = feature_names[tree.feature[node_id]]
else:
feature = "X[%s]" % tree.feature[node_id]
return "%s <= %.4f\\n%s = %s\\nsamples = %s" \
% (feature,
tree.threshold[node_id],
criterion,
tree.impurity[node_id],
tree.n_node_samples[node_id])
def recurse(tree, node_id, criterion, parent=None, depth=0):
if node_id == _tree.TREE_LEAF:
raise ValueError("Invalid node_id %s" % _tree.TREE_LEAF)
left_child = tree.children_left[node_id]
right_child = tree.children_right[node_id]
# Add node with description
if max_depth is None or depth <= max_depth:
out_file.write('%d [label="%s", shape="box"] ;\n' %
(node_id, node_to_str(tree, node_id, criterion)))
if parent is not None:
# Add edge to parent
out_file.write('%d -> %d ;\n' % (parent, node_id))
if left_child != _tree.TREE_LEAF:
recurse(tree, left_child, criterion=criterion, parent=node_id,
depth=depth + 1)
recurse(tree, right_child, criterion=criterion, parent=node_id,
depth=depth + 1)
else:
out_file.write('%d [label="(...)", shape="box"] ;\n' % node_id)
if parent is not None:
# Add edge to parent
out_file.write('%d -> %d ;\n' % (parent, node_id))
own_file = False
try:
if isinstance(out_file, six.string_types):
if six.PY3:
out_file = open(out_file, "w", encoding="utf-8")
else:
out_file = open(out_file, "wb")
own_file = True
out_file.write("digraph Tree {\n")
if isinstance(decision_tree, _tree.Tree):
recurse(decision_tree, 0, criterion="impurity")
else:
recurse(decision_tree.tree_, 0, criterion=decision_tree.criterion)
out_file.write("}")
finally:
if own_file:
out_file.close()
| bsd-3-clause |
flumotion-mirror/flumotion | tools/theora-bench.py | 3 | 6965 | #!/usr/bin/env python
# -*- Mode: Python -*-
# vi:si:et:sw=4:sts=4:ts=4
# Flumotion - a streaming media server
# Copyright (C) 2004,2005,2006,2007,2008,2009 Fluendo, S.L.
# Copyright (C) 2010,2011 Flumotion Services, S.A.
# All rights reserved.
#
# This file may be distributed and/or modified under the terms of
# the GNU Lesser General Public License version 2.1 as published by
# the Free Software Foundation.
# This file is distributed without any warranty; without even the implied
# warranty of merchantability or fitness for a particular purpose.
# See "LICENSE.LGPL" in the source distribution for more information.
#
# Headers in this file shall remain intact.
import gobject
gobject.threads_init()
import pygst
pygst.require('0.10')
import gst
import time
import sys
class SlidingWindow:
def __init__(self, size):
self._window = [0.0] * size
self._windowPtr = 0
self._first = True
# Maintain the current average, and the max of our current average
self.max = 0.0
self.average = 0.0
self.windowSize = size
def addValue(self, val):
self._window[self._windowPtr] = val
self._windowPtr = (self._windowPtr + 1) % self.windowSize
if self._first:
if self._windowPtr == 0:
self._first = False
return
self.average = sum(self._window) / self.windowSize
if self.average > self.max:
self.max = self.average
class TheoraBench:
def __init__(self, filename, outTemplate, width=None, height=None,
framerate=None):
self.framerate = None
self.width = None
self.height = None
self.outfileTemplate = outTemplate
# TODO: What's a reasonable windowSize to use?
windowSize = 20
self.window = SlidingWindow(windowSize)
self.samples = 0
self.data = ([], [], [])
self.pipeline = pipeline = gst.Pipeline()
self.bus = pipeline.get_bus()
filesrc = gst.element_factory_make("filesrc")
decodebin = gst.element_factory_make("decodebin")
self.ffmpegcolorspace = gst.element_factory_make("ffmpegcolorspace")
videorate = gst.element_factory_make("videorate")
videoscale = gst.element_factory_make("videoscale")
self.theoraenc = gst.element_factory_make("theoraenc")
fakesink = gst.element_factory_make("fakesink")
filesrc.set_property("location", filename)
pipeline.add(filesrc, decodebin, self.ffmpegcolorspace, videorate,
videoscale, self.theoraenc, fakesink)
filesrc.link(decodebin)
gst.element_link_many(self.ffmpegcolorspace, videorate, videoscale)
structure = gst.Structure("video/x-raw-yuv")
if height:
structure['height'] = height
if width:
structure['width'] = width
if framerate:
structure['framerate'] = framerate
caps = gst.Caps(structure)
videoscale.link(self.theoraenc, caps)
self.theoraenc.link(fakesink)
decodebin.connect("new-decoded-pad", self._pad_added_cb)
def _eos_cb(self, bus, msg):
print "Done"
fn = self.outfileTemplate % (self.width, self.height,
float(self.framerate))
print "Writing file: ", fn
self.writeGraph(fn, self.data,
"Frame number",
"CPU Percentage required",
("Frame)",
"Sliding Average (%d frames)" % self.window.windowSize,
"Sliding Average Peak"))
self.mainloop.quit()
def writeGraph(self, filename, data, xlabel, ylabel, dataNames):
# data is ([time], [average], [average_peak]) as percentages (floats)
#out = open(filename, "w")
#out.close()
import matplotlib
matplotlib.use('Agg')
from matplotlib import pylab
length = len(data[0])
pylab.plot(xrange(length), data[1])
pylab.plot(xrange(length), data[2])
pylab.axis([0, length-1, 0, 110])
pylab.savefig(filename, dpi=72)
pass
def _error_cb(self, bus, msg):
error = msg.parse_error()
print "Error: ", error[1]
self.mainloop.quit()
def run(self):
self.mainloop = gobject.MainLoop()
self.bus.add_signal_watch()
self.bus.connect("message::eos", self._eos_cb)
self.bus.connect("message::error", self._eos_cb)
self.pipeline.set_state(gst.STATE_PLAYING)
self.mainloop.run()
def _pad_added_cb(self, decodebin, pad, last):
structure = pad.get_caps()[0]
name = structure.get_name()
if name.startswith('video/x-raw-'):
sinkpad = self.ffmpegcolorspace.get_pad("sink")
pad.link(sinkpad)
#self.framerate = structure['framerate']
sinkpad = self.theoraenc.get_pad("sink")
srcpad = self.theoraenc.get_pad("src")
sinkpad.add_buffer_probe(self._buffer_probe_sink_cb)
srcpad.add_buffer_probe(self._buffer_probe_src_cb)
def _buffer_probe_sink_cb(self, pad, buf):
if not self.framerate:
self.framerate = buf.get_caps()[0]['framerate']
self.width = buf.get_caps()[0]['width']
self.height = buf.get_caps()[0]['height']
self._last_ts = time.time()
return True
def _buffer_probe_src_cb(self, pad, buf):
processing_time = time.time() - self._last_ts
self.window.addValue(processing_time)
self.samples += 1
if self.samples <= self.window.windowSize:
return True # Ignore these, our sliding window isn't very smart
self.data[0].append(processing_time * float(self.framerate) * 100.0)
self.data[1].append(self.window.average * float(
self.framerate) * 100.0)
self.data[2].append(self.window.max * float(self.framerate) * 100.0)
print "This frame: %.2f: %.2f%%. Average: %.2f%%. Peak: %.2f%%" % (
processing_time,
processing_time * float(self.framerate) * 100.0,
self.window.average * float(self.framerate) * 100.0,
self.window.max * float(self.framerate) * 100.0)
return True
if len(sys.argv) == 2:
framerates = [(30, 1),
(25, 1),
(25, 2), (None, None)]
sizes = [(800, 600),
(400, 300),
(None, None)] # Other useful sizes here
for framerate in framerates:
for size in sizes:
if framerate[1]:
fr = gst.Fraction(framerate[0], framerate[1])
else:
fr = None
infile = sys.argv[1]
outfileTemplate = sys.argv[1] + ".%dx%d@%.2f.png"
bench = TheoraBench(sys.argv[1], outfileTemplate, size[0],
size[1], fr)
bench.run()
else:
print "Usage: %s filename.ogg" % sys.argv[0]
| lgpl-2.1 |
yavalvas/yav_com | build/matplotlib/lib/mpl_examples/user_interfaces/embedding_in_wx3.py | 9 | 4849 | #!/usr/bin/env python
"""
Copyright (C) 2003-2004 Andrew Straw, Jeremy O'Donoghue and others
License: This work is licensed under the PSF. A copy should be included
with this source code, and is also available at
http://www.python.org/psf/license.html
This is yet another example of using matplotlib with wx. Hopefully
this is pretty full-featured:
- both matplotlib toolbar and WX buttons manipulate plot
- full wxApp framework, including widget interaction
- XRC (XML wxWidgets resource) file to create GUI (made with XRCed)
This was derived from embedding_in_wx and dynamic_image_wxagg.
Thanks to matplotlib and wx teams for creating such great software!
"""
from __future__ import print_function
# Used to guarantee to use at least Wx2.8
import wxversion
wxversion.ensureMinimal('2.8')
import sys, time, os, gc
import matplotlib
matplotlib.use('WXAgg')
import matplotlib.cm as cm
import matplotlib.cbook as cbook
from matplotlib.backends.backend_wxagg import Toolbar, FigureCanvasWxAgg
from matplotlib.figure import Figure
import numpy as np
import wx
import wx.xrc as xrc
ERR_TOL = 1e-5 # floating point slop for peak-detection
matplotlib.rc('image', origin='lower')
class PlotPanel(wx.Panel):
def __init__(self, parent):
wx.Panel.__init__(self, parent, -1)
self.fig = Figure((5,4), 75)
self.canvas = FigureCanvasWxAgg(self, -1, self.fig)
self.toolbar = Toolbar(self.canvas) #matplotlib toolbar
self.toolbar.Realize()
#self.toolbar.set_active([0,1])
# Now put all into a sizer
sizer = wx.BoxSizer(wx.VERTICAL)
# This way of adding to sizer allows resizing
sizer.Add(self.canvas, 1, wx.LEFT|wx.TOP|wx.GROW)
# Best to allow the toolbar to resize!
sizer.Add(self.toolbar, 0, wx.GROW)
self.SetSizer(sizer)
self.Fit()
def init_plot_data(self):
a = self.fig.add_subplot(111)
x = np.arange(120.0)*2*np.pi/60.0
y = np.arange(100.0)*2*np.pi/50.0
self.x, self.y = np.meshgrid(x, y)
z = np.sin(self.x) + np.cos(self.y)
self.im = a.imshow( z, cmap=cm.jet)#, interpolation='nearest')
zmax = np.amax(z) - ERR_TOL
ymax_i, xmax_i = np.nonzero(z >= zmax)
if self.im.origin == 'upper':
ymax_i = z.shape[0]-ymax_i
self.lines = a.plot(xmax_i,ymax_i,'ko')
self.toolbar.update() # Not sure why this is needed - ADS
def GetToolBar(self):
# You will need to override GetToolBar if you are using an
# unmanaged toolbar in your frame
return self.toolbar
def OnWhiz(self,evt):
self.x += np.pi/15
self.y += np.pi/20
z = np.sin(self.x) + np.cos(self.y)
self.im.set_array(z)
zmax = np.amax(z) - ERR_TOL
ymax_i, xmax_i = np.nonzero(z >= zmax)
if self.im.origin == 'upper':
ymax_i = z.shape[0]-ymax_i
self.lines[0].set_data(xmax_i,ymax_i)
self.canvas.draw()
def onEraseBackground(self, evt):
# this is supposed to prevent redraw flicker on some X servers...
pass
class MyApp(wx.App):
def OnInit(self):
xrcfile = cbook.get_sample_data('embedding_in_wx3.xrc', asfileobj=False)
print('loading', xrcfile)
self.res = xrc.XmlResource(xrcfile)
# main frame and panel ---------
self.frame = self.res.LoadFrame(None,"MainFrame")
self.panel = xrc.XRCCTRL(self.frame,"MainPanel")
# matplotlib panel -------------
# container for matplotlib panel (I like to make a container
# panel for our panel so I know where it'll go when in XRCed.)
plot_container = xrc.XRCCTRL(self.frame,"plot_container_panel")
sizer = wx.BoxSizer(wx.VERTICAL)
# matplotlib panel itself
self.plotpanel = PlotPanel(plot_container)
self.plotpanel.init_plot_data()
# wx boilerplate
sizer.Add(self.plotpanel, 1, wx.EXPAND)
plot_container.SetSizer(sizer)
# whiz button ------------------
whiz_button = xrc.XRCCTRL(self.frame,"whiz_button")
wx.EVT_BUTTON(whiz_button, whiz_button.GetId(),
self.plotpanel.OnWhiz)
# bang button ------------------
bang_button = xrc.XRCCTRL(self.frame,"bang_button")
wx.EVT_BUTTON(bang_button, bang_button.GetId(),
self.OnBang)
# final setup ------------------
sizer = self.panel.GetSizer()
self.frame.Show(1)
self.SetTopWindow(self.frame)
return True
def OnBang(self,event):
bang_count = xrc.XRCCTRL(self.frame,"bang_count")
bangs = bang_count.GetValue()
bangs = int(bangs)+1
bang_count.SetValue(str(bangs))
if __name__ == '__main__':
app = MyApp(0)
app.MainLoop()
| mit |
LACMTA/folium | folium/folium.py | 1 | 49682 | # -*- coding: utf-8 -*-
"""
Folium
-------
Make beautiful, interactive maps with Python and Leaflet.js
"""
from __future__ import absolute_import
from __future__ import print_function
from __future__ import division
import codecs
import functools
import json
from uuid import uuid4
from jinja2 import Environment, PackageLoader
from pkg_resources import resource_string
from folium import utilities
from folium.six import text_type, binary_type, iteritems
import sys
import base64
ENV = Environment(loader=PackageLoader('folium', 'templates'))
def initialize_notebook():
"""Initialize the IPython notebook display elements."""
try:
from IPython.core.display import display, HTML
except ImportError:
print("IPython Notebook could not be loaded.")
lib_css = ENV.get_template('ipynb_init_css.html')
lib_js = ENV.get_template('ipynb_init_js.html')
leaflet_dvf = ENV.get_template('leaflet-dvf.markers.min.js')
display(HTML(lib_css.render()))
display(HTML(lib_js.render({'leaflet_dvf': leaflet_dvf.render()})))
def iter_obj(type):
"""Decorator to keep count of different map object types in self.mk_cnt."""
def decorator(func):
@functools.wraps(func)
def wrapper(self, *args, **kwargs):
self.mark_cnt[type] = self.mark_cnt.get(type, 0) + 1
func_result = func(self, *args, **kwargs)
return func_result
return wrapper
return decorator
class Map(object):
"""Create a Map with Folium."""
def __init__(self, location=None, width='100%', height='100%',
tiles='OpenStreetMap', API_key=None, max_zoom=18, min_zoom=1,
zoom_start=10, attr=None, min_lat=-90, max_lat=90,
min_lon=-180, max_lon=180):
"""Create a Map with Folium and Leaflet.js
Generate a base map of given width and height with either default
tilesets or a custom tileset URL. The following tilesets are built-in
to Folium. Pass any of the following to the "tiles" keyword:
- "OpenStreetMap"
- "MapQuest Open"
- "MapQuest Open Aerial"
- "Mapbox Bright" (Limited levels of zoom for free tiles)
- "Mapbox Control Room" (Limited levels of zoom for free tiles)
- "Stamen" (Terrain, Toner, and Watercolor)
- "Cloudmade" (Must pass API key)
- "Mapbox" (Must pass API key)
- "CartoDB" (positron and dark_matter)
You can pass a custom tileset to Folium by passing a Leaflet-style
URL to the tiles parameter:
http://{s}.yourtiles.com/{z}/{x}/{y}.png
Parameters
----------
location: tuple or list, default None
Latitude and Longitude of Map (Northing, Easting).
width: pixel int or percentage string (default: '100%')
Width of the map.
height: pixel int or percentage string (default: '100%')
Height of the map.
tiles: str, default 'OpenStreetMap'
Map tileset to use. Can use defaults or pass a custom URL.
API_key: str, default None
API key for Cloudmade or Mapbox tiles.
max_zoom: int, default 18
Maximum zoom depth for the map.
zoom_start: int, default 10
Initial zoom level for the map.
attr: string, default None
Map tile attribution; only required if passing custom tile URL.
Returns
-------
Folium Map Object
Examples
--------
>>>map = folium.Map(location=[45.523, -122.675], width=750, height=500)
>>>map = folium.Map(location=[45.523, -122.675],
tiles='Mapbox Control Room')
>>>map = folium.Map(location=(45.523, -122.675), max_zoom=20,
tiles='Cloudmade', API_key='YourKey')
>>>map = folium.Map(location=[45.523, -122.675], zoom_start=2,
tiles=('http://{s}.tiles.mapbox.com/v3/'
'mapbox.control-room/{z}/{x}/{y}.png'),
attr='Mapbox attribution')
"""
# Inits.
self.map_path = None
self.render_iframe = False
self.map_type = 'base'
self.map_id = '_'.join(['folium', uuid4().hex])
# Mark counter, JSON, Plugins.
self.mark_cnt = {}
self.json_data = {}
self.plugins = {}
# No location means we will use automatic bounds and ignore zoom
self.location = location
# If location is not passed, we center the map at 0,0
if not location:
location = [0, 0]
zoom_start = min_zoom
# Map Size Parameters.
try:
if isinstance(width, int):
width_type = 'px'
assert width > 0
else:
width_type = '%'
width = int(width.strip('%'))
assert 0 <= width <= 100
except:
msg = "Cannot parse width {!r} as {!r}".format
raise ValueError(msg(width, width_type))
self.width = width
try:
if isinstance(height, int):
height_type = 'px'
assert height > 0
else:
height_type = '%'
height = int(height.strip('%'))
assert 0 <= height <= 100
except:
msg = "Cannot parse height {!r} as {!r}".format
raise ValueError(msg(height, height_type))
self.height = height
self.map_size = {'width': width, 'height': height}
self._size = ('style="width: {0}{1}; height: {2}{3}"'
.format(width, width_type, height, height_type))
# Templates.
self.env = ENV
self.template_vars = dict(lat=location[0],
lon=location[1],
size=self._size,
max_zoom=max_zoom,
zoom_level=zoom_start,
map_id=self.map_id,
min_zoom=min_zoom,
min_lat=min_lat,
max_lat=max_lat,
min_lon=min_lon,
max_lon=max_lon)
# Tiles.
self.tiles = ''.join(tiles.lower().strip().split())
if self.tiles in ('cloudmade', 'mapbox') and not API_key:
raise ValueError('You must pass an API key if using Cloudmade'
' or non-default Mapbox tiles.')
self.default_tiles = ['openstreetmap', 'mapboxcontrolroom',
'mapquestopen', 'mapquestopenaerial',
'mapboxbright', 'mapbox', 'cloudmade',
'stamenterrain', 'stamentoner',
'stamenwatercolor',
'cartodbpositron', 'cartodbdark_matter']
self.tile_types = {}
for tile in self.default_tiles:
tile_path = 'tiles/%s' % tile
self.tile_types[tile] = {
'templ': self.env.get_template('%s/%s' % (tile_path,
'tiles.txt')),
'attr': self.env.get_template('%s/%s' % (tile_path,
'attr.txt')),
}
if self.tiles in self.tile_types:
self.template_vars['Tiles'] = (self.tile_types[self.tiles]['templ']
.render(API_key=API_key))
self.template_vars['attr'] = (self.tile_types[self.tiles]['attr']
.render())
else:
self.template_vars['Tiles'] = tiles
if not attr:
raise ValueError('Custom tiles must'
' also be passed an attribution')
if isinstance(attr, binary_type):
attr = text_type(attr, 'utf8')
self.template_vars['attr'] = attr
self.tile_types.update({'Custom': {'template': tiles,
'attr': attr}})
self.added_layers = []
self.template_vars.setdefault('wms_layers', [])
self.template_vars.setdefault('tile_layers', [])
self.template_vars.setdefault('image_layers', [])
@iter_obj('simple')
def add_tile_layer(self, tile_name=None, tile_url=None, active=False):
"""Adds a simple tile layer.
Parameters
----------
tile_name: string
name of the tile layer
tile_url: string
url location of the tile layer
active: boolean
should the layer be active when added
"""
if tile_name not in self.added_layers:
tile_name = tile_name.replace(" ", "_")
tile_temp = self.env.get_template('tile_layer.js')
tile = tile_temp.render({'tile_name': tile_name,
'tile_url': tile_url})
self.template_vars.setdefault('tile_layers', []).append((tile))
self.added_layers.append({tile_name: tile_url})
@iter_obj('simple')
def add_wms_layer(self, wms_name=None, wms_url=None, wms_format=None,
wms_layers=None, wms_transparent=True):
"""Adds a simple tile layer.
Parameters
----------
wms_name: string
name of wms layer
wms_url : string
url of wms layer
"""
if wms_name not in self.added_layers:
wms_name = wms_name.replace(" ", "_")
wms_temp = self.env.get_template('wms_layer.js')
wms = wms_temp.render({
'wms_name': wms_name,
'wms_url': wms_url,
'wms_format': wms_format,
'wms_layer_names': wms_layers,
'wms_transparent': str(wms_transparent).lower()})
self.template_vars.setdefault('wms_layers', []).append((wms))
self.added_layers.append({wms_name: wms_url})
@iter_obj('simple')
def add_layers_to_map(self):
"""
Required function to actually add the layers to the HTML packet.
"""
layers_temp = self.env.get_template('add_layers.js')
data_string = ''
for i, layer in enumerate(self.added_layers):
name = list(layer.keys())[0]
if i < len(self.added_layers)-1:
term_string = ",\n"
else:
term_string += "\n"
data_string += '\"{}\": {}'.format(name, name, term_string)
data_layers = layers_temp.render({'layers': data_string})
self.template_vars.setdefault('data_layers', []).append((data_layers))
@iter_obj('simple')
def simple_marker(self, location=None, popup=None,
marker_color='blue', marker_icon='info-sign',
clustered_marker=False, icon_angle=0, popup_width=300):
"""Create a simple stock Leaflet marker on the map, with optional
popup text or Vincent visualization.
Parameters
----------
location: tuple or list, default None
Latitude and Longitude of Marker (Northing, Easting)
popup: string or tuple, default 'Pop Text'
Input text or visualization for object. Can pass either text,
or a tuple of the form (Vincent object, 'vis_path.json')
It is possible to adjust the width of text/HTML popups
using the optional keywords `popup_width` (default is 300px).
marker_color
color of marker you want
marker_icon
icon from (http://getbootstrap.com/components/) you want on the
marker
clustered_marker
boolean of whether or not you want the marker clustered with
other markers
Returns
-------
Marker names and HTML in obj.template_vars
Example
-------
>>>map.simple_marker(location=[45.5, -122.3], popup='Portland, OR')
>>>map.simple_marker(location=[45.5, -122.3], popup=(vis, 'vis.json'))
"""
count = self.mark_cnt['simple']
mark_temp = self.env.get_template('simple_marker.js')
marker_num = 'marker_{0}'.format(count)
add_line = "{'icon':"+marker_num+"_icon}"
icon_temp = self.env.get_template('simple_icon.js')
icon = icon_temp.render({'icon': marker_icon,
'icon_name': marker_num+"_icon",
'markerColor': marker_color,
'icon_angle': icon_angle})
# Get marker and popup.
marker = mark_temp.render({'marker': 'marker_' + str(count),
'lat': location[0],
'lon': location[1],
'icon': add_line
})
popup_out = self._popup_render(popup=popup, mk_name='marker_',
count=count, width=popup_width)
if clustered_marker:
add_mark = 'clusteredmarkers.addLayer(marker_{0})'.format(count)
name = 'cluster_markers'
else:
add_mark = 'map.addLayer(marker_{0})'.format(count)
name = 'custom_markers'
append = (icon, marker, popup_out, add_mark)
self.template_vars.setdefault(name, []).append(append)
@iter_obj('div_mark')
def div_markers(self, locations=None, popups=None, marker_size=10, popup_width=300):
"""Create a simple div marker on the map, with optional
popup text or Vincent visualization. Useful for marking points along a
line.
Parameters
----------
locations: list of locations, where each location is an array
Latitude and Longitude of Marker (Northing, Easting)
popup: list of popups, each popup should be a string or tuple, default 'Pop Text'
Input text or visualization for object. Can pass either text,
or a tuple of the form (Vincent object, 'vis_path.json')
It is possible to adjust the width of text/HTML popups
using the optional keywords `popup_width`. (Leaflet default is 300px.)
marker_size
default is 5
Returns
-------
Marker names and HTML in obj.template_vars
Example
-------
>>>map.div_markers(locations=[[37.421114, -122.128314], [37.391637, -122.085416], [37.388832, -122.087709]], popups=['1437494575531', '1437492135937', '1437493590434'])
"""
call_cnt = self.mark_cnt['div_mark']
if locations is None or popups is None:
raise RuntimeError("Both locations and popups are mandatory")
for (point_cnt, (location, popup)) in enumerate(zip(locations, popups)):
marker_num = 'div_marker_{0}_{1}'.format(call_cnt, point_cnt)
icon_temp = self.env.get_template('static_div_icon.js')
icon_name = marker_num+"_icon"
icon = icon_temp.render({'icon_name': icon_name,
'size': marker_size})
mark_temp = self.env.get_template('simple_marker.js')
# Get marker and popup.
marker = mark_temp.render({'marker': marker_num,
'lat': location[0],
'lon': location[1],
'icon': "{'icon':"+icon_name+"}"
})
popup_out = self._popup_render(popup=popup, mk_name='div_marker_{0}_'.format(call_cnt),
count=point_cnt, width=popup_width)
add_mark = 'map.addLayer(div_marker_{0}_{1})'.format(call_cnt, point_cnt)
append = (icon, marker, popup_out, add_mark)
self.template_vars.setdefault('div_markers', []).append(append)
@iter_obj('line')
def line(self, locations,
line_color=None, line_opacity=None, line_weight=None,
popup=None, popup_width=300):
"""Add a line to the map with optional styles.
Parameters
----------
locations: list of points (latitude, longitude)
Latitude and Longitude of line (Northing, Easting)
line_color: string, default Leaflet's default ('#03f')
line_opacity: float, default Leaflet's default (0.5)
line_weight: float, default Leaflet's default (5)
popup: string or tuple, default 'Pop Text'
Input text or visualization for object. Can pass either text,
or a tuple of the form (Vincent object, 'vis_path.json')
It is possible to adjust the width of text/HTML popups
using the optional keywords `popup_width` (default is 300px).
Note: If the optional styles are omitted, they will not be included
in the HTML output and will obtain the Leaflet defaults listed above.
Example
-------
>>>map.line(locations=[(45.5, -122.3), (42.3, -71.0)])
>>>map.line(locations=[(45.5, -122.3), (42.3, -71.0)],
line_color='red', line_opacity=1.0)
"""
count = self.mark_cnt['line']
line_temp = self.env.get_template('polyline.js')
polyline_opts = {'color': line_color, 'weight': line_weight,
'opacity': line_opacity}
varname = 'line_{}'.format(count)
line_rendered = line_temp.render({'line': varname,
'locations': locations,
'options': polyline_opts})
popup_out = self._popup_render(popup=popup, mk_name='line_',
count=count, width=popup_width)
add_line = 'map.addLayer({});'.format(varname)
append = (line_rendered, popup_out, add_line)
self.template_vars.setdefault('lines', []).append((append))
@iter_obj('multiline')
def multiline(self, locations, line_color=None, line_opacity=None,
line_weight=None):
"""Add a multiPolyline to the map with optional styles.
A multiPolyline is single layer that consists of several polylines that
share styling/popup.
Parameters
----------
locations: list of lists of points (latitude, longitude)
Latitude and Longitude of line (Northing, Easting)
line_color: string, default Leaflet's default ('#03f')
line_opacity: float, default Leaflet's default (0.5)
line_weight: float, default Leaflet's default (5)
Note: If the optional styles are omitted, they will not be included
in the HTML output and will obtain the Leaflet defaults listed above.
Example
-------
# FIXME: Add another example.
>>> m.multiline(locations=[[(45.5236, -122.675), (45.5236, -122.675)],
[(45.5237, -122.675), (45.5237, -122.675)],
[(45.5238, -122.675), (45.5238, -122.675)]])
>>> m.multiline(locations=[[(45.5236, -122.675), (45.5236, -122.675)],
[(45.5237, -122.675), (45.5237, -122.675)],
[(45.5238, -122.675), (45.5238, -122.675)]],
line_color='red', line_weight=2,
line_opacity=1.0)
"""
count = self.mark_cnt['multiline']
multiline_temp = self.env.get_template('multi_polyline.js')
multiline_opts = {'color': line_color, 'weight': line_weight,
'opacity': line_opacity}
varname = 'multiline_{}'.format(count)
multiline_rendered = multiline_temp.render({'multiline': varname,
'locations': locations,
'options': multiline_opts})
add_multiline = 'map.addLayer({});'.format(varname)
append = (multiline_rendered, add_multiline)
self.template_vars.setdefault('multilines', []).append(append)
@iter_obj('circle')
def circle_marker(self, location=None, radius=500, popup=None,
line_color='black', fill_color='black',
fill_opacity=0.6, popup_width=300):
"""Create a simple circle marker on the map, with optional popup text
or Vincent visualization.
Parameters
----------
location: tuple or list, default None
Latitude and Longitude of Marker (Northing, Easting)
radius: int, default 500
Circle radius, in pixels
popup: string or tuple, default 'Pop Text'
Input text or visualization for object. Can pass either text,
or a tuple of the form (Vincent object, 'vis_path.json')
It is possible to adjust the width of text/HTML popups
using the optional keywords `popup_width` (default is 300px).
line_color: string, default black
Line color. Can pass hex value here as well.
fill_color: string, default black
Fill color. Can pass hex value here as well.
fill_opacity: float, default 0.6
Circle fill opacity
Returns
-------
Circle names and HTML in obj.template_vars
Example
-------
>>>map.circle_marker(location=[45.5, -122.3],
radius=1000, popup='Portland, OR')
>>>map.circle_marker(location=[45.5, -122.3],
radius=1000, popup=(bar_chart, 'bar_data.json'))
"""
count = self.mark_cnt['circle']
circle_temp = self.env.get_template('circle_marker.js')
circle = circle_temp.render({'circle': 'circle_' + str(count),
'radius': radius,
'lat': location[0], 'lon': location[1],
'line_color': line_color,
'fill_color': fill_color,
'fill_opacity': fill_opacity})
popup_out = self._popup_render(popup=popup, mk_name='circle_',
count=count, width=popup_width)
add_mark = 'map.addLayer(circle_{0})'.format(count)
self.template_vars.setdefault('markers', []).append((circle,
popup_out,
add_mark))
@iter_obj('polygon')
def polygon_marker(self, location=None, line_color='black', line_opacity=1,
line_weight=2, fill_color='blue', fill_opacity=1,
num_sides=4, rotation=0, radius=15, popup=None,
popup_width=300):
"""Custom markers using the Leaflet Data Vis Framework.
Parameters
----------
location: tuple or list, default None
Latitude and Longitude of Marker (Northing, Easting)
line_color: string, default 'black'
Marker line color
line_opacity: float, default 1
Line opacity, scale 0-1
line_weight: int, default 2
Stroke weight in pixels
fill_color: string, default 'blue'
Marker fill color
fill_opacity: float, default 1
Marker fill opacity
num_sides: int, default 4
Number of polygon sides
rotation: int, default 0
Rotation angle in degrees
radius: int, default 15
Marker radius, in pixels
popup: string or tuple, default 'Pop Text'
Input text or visualization for object. Can pass either text,
or a tuple of the form (Vincent object, 'vis_path.json')
It is possible to adjust the width of text/HTML popups
using the optional keywords `popup_width` (default is 300px).
Returns
-------
Polygon marker names and HTML in obj.template_vars
"""
count = self.mark_cnt['polygon']
poly_temp = self.env.get_template('poly_marker.js')
polygon = poly_temp.render({'marker': 'polygon_' + str(count),
'lat': location[0],
'lon': location[1],
'line_color': line_color,
'line_opacity': line_opacity,
'line_weight': line_weight,
'fill_color': fill_color,
'fill_opacity': fill_opacity,
'num_sides': num_sides,
'rotation': rotation,
'radius': radius})
popup_out = self._popup_render(popup=popup, mk_name='polygon_',
count=count, width=popup_width)
add_mark = 'map.addLayer(polygon_{0})'.format(count)
self.template_vars.setdefault('markers', []).append((polygon,
popup_out,
add_mark))
# Update JS/CSS and other Plugin files.
js_temp = self.env.get_template('dvf_js_ref.txt').render()
self.template_vars.update({'dvf_js': js_temp})
polygon_js = resource_string('folium',
'plugins/leaflet-dvf.markers.min.js')
self.plugins.update({'leaflet-dvf.markers.min.js': polygon_js})
def lat_lng_popover(self):
"""Enable popovers to display Lat and Lon on each click."""
latlng_temp = self.env.get_template('lat_lng_popover.js')
self.template_vars.update({'lat_lng_pop': latlng_temp.render()})
def click_for_marker(self, popup=None):
"""Enable the addition of markers via clicking on the map. The marker
popup defaults to Lat/Lon, but custom text can be passed via the
popup parameter. Double click markers to remove them.
Parameters
----------
popup:
Custom popup text
Example
-------
>>>map.click_for_marker(popup='Your Custom Text')
"""
latlng = '"Latitude: " + lat + "<br>Longitude: " + lng '
click_temp = self.env.get_template('click_for_marker.js')
if popup:
popup_txt = ''.join(['"', popup, '"'])
else:
popup_txt = latlng
click_str = click_temp.render({'popup': popup_txt})
self.template_vars.update({'click_pop': click_str})
def fit_bounds(self, bounds, padding_top_left=None,
padding_bottom_right=None, padding=None, max_zoom=None):
"""Fit the map to contain a bounding box with the maximum zoom level possible.
Parameters
----------
bounds: list of (latitude, longitude) points
Bounding box specified as two points [southwest, northeast]
padding_top_left: (x, y) point, default None
Padding in the top left corner. Useful if some elements in
the corner, such as controls, might obscure objects you're zooming
to.
padding_bottom_right: (x, y) point, default None
Padding in the bottom right corner.
padding: (x, y) point, default None
Equivalent to setting both top left and bottom right padding to
the same value.
max_zoom: int, default None
Maximum zoom to be used.
Example
-------
>>> map.fit_bounds([[52.193636, -2.221575], [52.636878, -1.139759]])
"""
options = {
'paddingTopLeft': padding_top_left,
'paddingBottomRight': padding_bottom_right,
'padding': padding,
'maxZoom': max_zoom,
}
fit_bounds_options = {}
for key, opt in options.items():
if opt:
fit_bounds_options[key] = opt
fit_bounds = self.env.get_template('fit_bounds.js')
fit_bounds_str = fit_bounds.render({
'bounds': json.dumps(bounds),
'fit_bounds_options': json.dumps(fit_bounds_options,
sort_keys=True),
})
self.template_vars.update({'fit_bounds': fit_bounds_str})
def add_plugin(self, plugin):
"""Adds a plugin to the map.
Parameters
----------
plugin: folium.plugins object
A plugin to be added to the map. It has to implement the methods
`render_html`, `render_css` and `render_js`.
"""
plugin.add_to_map(self)
def _auto_bounds(self):
if 'fit_bounds' in self.template_vars:
return
# Get count for each feature type
ft_names = ["marker", "line", "circle", "polygon", "multiline"]
ft_names = [i for i in ft_names if i in self.mark_cnt]
# Make a comprehensive list of all the features we want to fit
feat_str = ["{name}_{count}".format(name=ft_name,
count=self.mark_cnt[ft_name])
for ft_name in ft_names for
count in range(1, self.mark_cnt[ft_name]+1)]
feat_str = "[" + ', '.join(feat_str) + "]"
fit_bounds = self.env.get_template('fit_bounds.js')
fit_bounds_str = fit_bounds.render({
'autobounds': not self.location,
'features': feat_str,
'fit_bounds_options': json.dumps({'padding': [30, 30]}),
})
self.template_vars.update({'fit_bounds': fit_bounds_str.strip()})
def _popup_render(self, popup=None, mk_name=None, count=None,
width=300):
"""Popup renderer: either text or Vincent/Vega.
Parameters
----------
popup: str or Vincent tuple, default None
String for text popup, or tuple of (Vincent object, json_path)
mk_name: str, default None
Type of marker. Simple, Circle, etc.
count: int, default None
Count of marker
"""
if not popup:
return ''
else:
if sys.version_info >= (3, 0):
utype, stype = str, bytes
else:
utype, stype = unicode, str
if isinstance(popup, (utype, stype)):
popup_temp = self.env.get_template('simple_popup.js')
if isinstance(popup, utype):
popup_txt = popup.encode('ascii', 'xmlcharrefreplace')
else:
popup_txt = popup
if sys.version_info >= (3, 0):
popup_txt = popup_txt.decode()
pop_txt = json.dumps(str(popup_txt))
return popup_temp.render({'pop_name': mk_name + str(count),
'pop_txt': pop_txt, 'width': width})
elif isinstance(popup, tuple):
# Update template with JS libs.
vega_temp = self.env.get_template('vega_ref.txt').render()
jquery_temp = self.env.get_template('jquery_ref.txt').render()
d3_temp = self.env.get_template('d3_ref.txt').render()
vega_parse = self.env.get_template('vega_parse.js').render()
self.template_vars.update({'vega': vega_temp,
'd3': d3_temp,
'jquery': jquery_temp,
'vega_parse': vega_parse})
# Parameters for Vega template.
vega = popup[0]
mark = ''.join([mk_name, str(count)])
json_out = popup[1]
div_id = popup[1].split('.')[0]
width = vega.width
height = vega.height
if isinstance(vega.padding, dict):
width += vega.padding['left']+vega.padding['right']
height += vega.padding['top']+vega.padding['bottom']
else:
width += 75
height += 50
max_width = self.map_size['width']
vega_id = '#' + div_id
popup_temp = self.env.get_template('vega_marker.js')
return popup_temp.render({'mark': mark, 'div_id': div_id,
'width': width, 'height': height,
'max_width': max_width,
'json_out': json_out,
'vega_id': vega_id})
else:
raise TypeError("Unrecognized popup type: {!r}".format(popup))
@iter_obj('geojson')
def geo_json(self, geo_path=None, geo_str=None, data_out='data.json',
data=None, columns=None, key_on=None, threshold_scale=None,
fill_color='blue', fill_opacity=0.6, line_color='black',
line_weight=1, line_opacity=1, legend_name=None,
topojson=None, reset=False):
"""Apply a GeoJSON overlay to the map.
Plot a GeoJSON overlay on the base map. There is no requirement
to bind data (passing just a GeoJSON plots a single-color overlay),
but there is a data binding option to map your columnar data to
different feature objects with a color scale.
If data is passed as a Pandas dataframe, the "columns" and "key-on"
keywords must be included, the first to indicate which DataFrame
columns to use, the second to indicate the layer in the GeoJSON
on which to key the data. The 'columns' keyword does not need to be
passed for a Pandas series.
Colors are generated from color brewer (http://colorbrewer2.org/)
sequential palettes on a D3 threshold scale. The scale defaults to the
following quantiles: [0, 0.5, 0.75, 0.85, 0.9]. A custom scale can be
passed to `threshold_scale` of length <=6, in order to match the
color brewer range.
TopoJSONs can be passed as "geo_path", but the "topojson" keyword must
also be passed with the reference to the topojson objects to convert.
See the topojson.feature method in the TopoJSON API reference:
https://github.com/mbostock/topojson/wiki/API-Reference
Parameters
----------
geo_path: string, default None
URL or File path to your GeoJSON data
geo_str: string, default None
String of GeoJSON, alternative to geo_path
data_out: string, default 'data.json'
Path to write Pandas DataFrame/Series to JSON if binding data
data: Pandas DataFrame or Series, default None
Data to bind to the GeoJSON.
columns: dict or tuple, default None
If the data is a Pandas DataFrame, the columns of data to be bound.
Must pass column 1 as the key, and column 2 the values.
key_on: string, default None
Variable in the GeoJSON file to bind the data to. Must always
start with 'feature' and be in JavaScript objection notation.
Ex: 'feature.id' or 'feature.properties.statename'.
threshold_scale: list, default None
Data range for D3 threshold scale. Defaults to the following range
of quantiles: [0, 0.5, 0.75, 0.85, 0.9], rounded to the nearest
order-of-magnitude integer. Ex: 270 rounds to 200, 5600 to 6000.
fill_color: string, default 'blue'
Area fill color. Can pass a hex code, color name, or if you are
binding data, one of the following color brewer palettes:
'BuGn', 'BuPu', 'GnBu', 'OrRd', 'PuBu', 'PuBuGn', 'PuRd', 'RdPu',
'YlGn', 'YlGnBu', 'YlOrBr', and 'YlOrRd'.
fill_opacity: float, default 0.6
Area fill opacity, range 0-1.
line_color: string, default 'black'
GeoJSON geopath line color.
line_weight: int, default 1
GeoJSON geopath line weight.
line_opacity: float, default 1
GeoJSON geopath line opacity, range 0-1.
legend_name: string, default None
Title for data legend. If not passed, defaults to columns[1].
topojson: string, default None
If using a TopoJSON, passing "objects.yourfeature" to the topojson
keyword argument will enable conversion to GeoJSON.
reset: boolean, default False
Remove all current geoJSON layers, start with new layer
Output
------
GeoJSON data layer in obj.template_vars
Example
-------
>>> m.geo_json(geo_path='us-states.json', line_color='blue',
line_weight=3)
>>> m.geo_json(geo_path='geo.json', data=df,
columns=['Data 1', 'Data 2'],
key_on='feature.properties.myvalue', fill_color='PuBu',
threshold_scale=[0, 20, 30, 40, 50, 60])
>>> m.geo_json(geo_path='countries.json', topojson='objects.countries')
"""
if reset:
reset_vars = ['json_paths', 'func_vars', 'color_scales',
'geo_styles', 'gjson_layers', 'map_legends',
'topo_convert']
for var in reset_vars:
self.template_vars.update({var: []})
self.mark_cnt['geojson'] = 1
def json_style(style_cnt, line_color, line_weight, line_opacity,
fill_color, fill_opacity, quant_fill):
"""Generate JSON styling function from template"""
style_temp = self.env.get_template('geojson_style.js')
style = style_temp.render({'style': style_cnt,
'line_color': line_color,
'line_weight': line_weight,
'line_opacity': line_opacity,
'fill_color': fill_color,
'fill_opacity': fill_opacity,
'quantize_fill': quant_fill})
return style
# Set map type to geojson.
self.map_type = 'geojson'
# Get JSON map layer template pieces, convert TopoJSON if necessary.
# geo_str is really a hack.
if geo_path:
geo_path = ".defer(d3.json, '{0}')".format(geo_path)
elif geo_str:
fmt = (".defer(function(callback)"
"{{callback(null, JSON.parse('{}'))}})").format
geo_path = fmt(geo_str)
if topojson is None:
map_var = '_'.join(['gjson', str(self.mark_cnt['geojson'])])
layer_var = map_var
else:
map_var = '_'.join(['tjson', str(self.mark_cnt['geojson'])])
topo_obj = '.'.join([map_var, topojson])
layer_var = '_'.join(['topo', str(self.mark_cnt['geojson'])])
topo_templ = self.env.get_template('topo_func.js')
topo_func = topo_templ.render({'map_var': layer_var,
't_var': map_var,
't_var_obj': topo_obj})
topo_lib = self.env.get_template('topojson_ref.txt').render()
self.template_vars.update({'topojson': topo_lib})
self.template_vars.setdefault('topo_convert',
[]).append(topo_func)
style_count = '_'.join(['style', str(self.mark_cnt['geojson'])])
# Get Data binding pieces if available.
if data is not None:
import pandas as pd
# Create DataFrame with only the relevant columns.
if isinstance(data, pd.DataFrame):
data = pd.concat([data[columns[0]], data[columns[1]]], axis=1)
# Save data to JSON.
self.json_data[data_out] = utilities.transform_data(data)
# Add data to queue.
d_path = ".defer(d3.json, '{0}')".format(data_out)
self.template_vars.setdefault('json_paths', []).append(d_path)
# Add data variable to makeMap function.
data_var = '_'.join(['data', str(self.mark_cnt['geojson'])])
self.template_vars.setdefault('func_vars', []).append(data_var)
# D3 Color scale.
series = data[columns[1]]
if threshold_scale and len(threshold_scale) > 6:
raise ValueError
domain = threshold_scale or utilities.split_six(series=series)
if len(domain) > 253:
raise ValueError('The threshold scale must be length <= 253')
if not utilities.color_brewer(fill_color):
raise ValueError('Please pass a valid color brewer code to '
'fill_local. See docstring for valid codes.')
palette = utilities.color_brewer(fill_color, len(domain))
d3range = palette[0: len(domain) + 1]
tick_labels = utilities.legend_scaler(domain)
color_temp = self.env.get_template('d3_threshold.js')
d3scale = color_temp.render({'domain': domain,
'range': d3range})
self.template_vars.setdefault('color_scales', []).append(d3scale)
# Create legend.
name = legend_name or columns[1]
leg_templ = self.env.get_template('d3_map_legend.js')
legend = leg_templ.render({'lin_max': int(domain[-1]*1.1),
'tick_labels': tick_labels,
'caption': name})
self.template_vars.setdefault('map_legends', []).append(legend)
# Style with color brewer colors.
matchColor = 'color(matchKey({0}, {1}))'.format(key_on, data_var)
style = json_style(style_count, line_color, line_weight,
line_opacity, None, fill_opacity, matchColor)
else:
style = json_style(style_count, line_color, line_weight,
line_opacity, fill_color, fill_opacity, None)
layer = ('gJson_layer_{0} = L.geoJson({1}, {{style: {2},'
'onEachFeature: onEachFeature}}).addTo(map)'
.format(self.mark_cnt['geojson'], layer_var, style_count))
self.template_vars.setdefault('json_paths', []).append(geo_path)
self.template_vars.setdefault('func_vars', []).append(map_var)
self.template_vars.setdefault('geo_styles', []).append(style)
self.template_vars.setdefault('gjson_layers', []).append(layer)
@iter_obj('image_overlay')
def image_overlay(self, data, opacity=0.25, min_lat=-90.0, max_lat=90.0,
min_lon=-180.0, max_lon=180.0, image_name=None, filename=None):
"""Simple image overlay of raster data from a numpy array. This is a lightweight
way to overlay geospatial data on top of a map. If your data is high res, consider
implementing a WMS server and adding a WMS layer.
This function works by generating a PNG file from a numpy array. If you do not
specifiy a filename, it will embed the image inline. Otherwise, it saves the file in the
current directory, and then adds it as an image overlay layer in leaflet.js.
By default, the image is placed and stretched using bounds that cover the
entire globe.
Parameters
----------
data: numpy array OR url string, required.
if numpy array, must be a image format, i.e., NxM (mono), NxMx3 (rgb), or NxMx4 (rgba)
if url, must be a valid url to a image (local or external)
opacity: float, default 0.25
Image layer opacity in range 0 (completely transparent) to 1 (opaque)
min_lat: float, default -90.0
max_lat: float, default 90.0
min_lon: float, default -180.0
max_lon: float, default 180.0
image_name: string, default None
The name of the layer object in leaflet.js
filename: string, default None
Optional file name of output.png for image overlay. If None, we use a
inline PNG.
Output
------
Image overlay data layer in obj.template_vars
Examples
-------
# assumes a map object `m` has been created
>>> import numpy as np
>>> data = np.random.random((100,100))
# to make a rgba from a specific matplotlib colormap:
>>> import matplotlib.cm as cm
>>> cmapper = cm.cm.ColorMapper('jet')
>>> data2 = cmapper.to_rgba(np.random.random((100,100)))
# place the data over all of the globe (will be pretty pixelated!)
>>> m.image_overlay(data)
# put it only over a single city (Paris)
>>> m.image_overlay(data, min_lat=48.80418, max_lat=48.90970, min_lon=2.25214, max_lon=2.44731)
"""
if isinstance(data, str):
filename = data
else:
try:
png_str = utilities.write_png(data)
except Exception as e:
raise e
if filename is not None:
with open(filename, 'wb') as fd:
fd.write(png_str)
else:
filename = "data:image/png;base64,"+base64.b64encode(png_str).decode('utf-8')
if image_name not in self.added_layers:
if image_name is None:
image_name = "Image_Overlay"
else:
image_name = image_name.replace(" ", "_")
image_url = filename
image_bounds = [[min_lat, min_lon], [max_lat, max_lon]]
image_opacity = opacity
image_temp = self.env.get_template('image_layer.js')
image = image_temp.render({'image_name': image_name,
'image_url': image_url,
'image_bounds': image_bounds,
'image_opacity': image_opacity})
self.template_vars['image_layers'].append(image)
self.added_layers.append(image_name)
def _build_map(self, html_templ=None, templ_type='string'):
self._auto_bounds()
"""Build HTML/JS/CSS from Templates given current map type."""
if html_templ is None:
map_types = {'base': 'fol_template.html',
'geojson': 'geojson_template.html'}
# Check current map type.
type_temp = map_types[self.map_type]
html_templ = self.env.get_template(type_temp)
else:
if templ_type == 'string':
html_templ = self.env.from_string(html_templ)
self.HTML = html_templ.render(self.template_vars, plugins=self.plugins)
def create_map(self, path='map.html', plugin_data_out=True, template=None):
"""Write Map output to HTML and data output to JSON if available.
Parameters:
-----------
path: string, default 'map.html'
Path for HTML output for map
plugin_data_out: boolean, default True
If using plugins such as awesome markers, write all plugin
data such as JS/CSS/images to path
template: string, default None
Custom template to render
"""
self.map_path = path
self._build_map(template)
with codecs.open(path, 'w', 'utf8') as f:
f.write(self.HTML)
if self.json_data:
for path, data in iteritems(self.json_data):
with open(path, 'w') as g:
json.dump(data, g)
if self.plugins and plugin_data_out:
for name, plugin in iteritems(self.plugins):
with open(name, 'w') as f:
if isinstance(plugin, binary_type):
plugin = text_type(plugin, 'utf8')
f.write(plugin)
def _repr_html_(self):
"""Build the HTML representation for IPython."""
map_types = {'base': 'ipynb_repr.html',
'geojson': 'ipynb_iframe.html'}
# Check current map type.
type_temp = map_types[self.map_type]
if self.render_iframe:
type_temp = 'ipynb_iframe.html'
templ = self.env.get_template(type_temp)
self._build_map(html_templ=templ, templ_type='temp')
if self.map_type == 'geojson' or self.render_iframe:
if not self.map_path:
raise ValueError('Use create_map to set the path!')
return templ.render(path=self.map_path, width=self.width,
height=self.height)
return self.HTML
def display(self):
"""Display the visualization inline in the IPython notebook.
This is deprecated, use the following instead::
from IPython.display import display
display(viz)
"""
from IPython.core.display import display, HTML
display(HTML(self._repr_html_()))
| mit |
sumspr/scikit-learn | sklearn/cluster/tests/test_bicluster.py | 226 | 9457 | """Testing for Spectral Biclustering methods"""
import numpy as np
from scipy.sparse import csr_matrix, issparse
from sklearn.grid_search import ParameterGrid
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_raises
from sklearn.utils.testing import assert_true
from sklearn.utils.testing import SkipTest
from sklearn.base import BaseEstimator, BiclusterMixin
from sklearn.cluster.bicluster import SpectralCoclustering
from sklearn.cluster.bicluster import SpectralBiclustering
from sklearn.cluster.bicluster import _scale_normalize
from sklearn.cluster.bicluster import _bistochastic_normalize
from sklearn.cluster.bicluster import _log_normalize
from sklearn.metrics import consensus_score
from sklearn.datasets import make_biclusters, make_checkerboard
class MockBiclustering(BaseEstimator, BiclusterMixin):
# Mock object for testing get_submatrix.
def __init__(self):
pass
def get_indices(self, i):
# Overridden to reproduce old get_submatrix test.
return (np.where([True, True, False, False, True])[0],
np.where([False, False, True, True])[0])
def test_get_submatrix():
data = np.arange(20).reshape(5, 4)
model = MockBiclustering()
for X in (data, csr_matrix(data), data.tolist()):
submatrix = model.get_submatrix(0, X)
if issparse(submatrix):
submatrix = submatrix.toarray()
assert_array_equal(submatrix, [[2, 3],
[6, 7],
[18, 19]])
submatrix[:] = -1
if issparse(X):
X = X.toarray()
assert_true(np.all(X != -1))
def _test_shape_indices(model):
# Test get_shape and get_indices on fitted model.
for i in range(model.n_clusters):
m, n = model.get_shape(i)
i_ind, j_ind = model.get_indices(i)
assert_equal(len(i_ind), m)
assert_equal(len(j_ind), n)
def test_spectral_coclustering():
# Test Dhillon's Spectral CoClustering on a simple problem.
param_grid = {'svd_method': ['randomized', 'arpack'],
'n_svd_vecs': [None, 20],
'mini_batch': [False, True],
'init': ['k-means++'],
'n_init': [10],
'n_jobs': [1]}
random_state = 0
S, rows, cols = make_biclusters((30, 30), 3, noise=0.5,
random_state=random_state)
S -= S.min() # needs to be nonnegative before making it sparse
S = np.where(S < 1, 0, S) # threshold some values
for mat in (S, csr_matrix(S)):
for kwargs in ParameterGrid(param_grid):
model = SpectralCoclustering(n_clusters=3,
random_state=random_state,
**kwargs)
model.fit(mat)
assert_equal(model.rows_.shape, (3, 30))
assert_array_equal(model.rows_.sum(axis=0), np.ones(30))
assert_array_equal(model.columns_.sum(axis=0), np.ones(30))
assert_equal(consensus_score(model.biclusters_,
(rows, cols)), 1)
_test_shape_indices(model)
def test_spectral_biclustering():
# Test Kluger methods on a checkerboard dataset.
S, rows, cols = make_checkerboard((30, 30), 3, noise=0.5,
random_state=0)
non_default_params = {'method': ['scale', 'log'],
'svd_method': ['arpack'],
'n_svd_vecs': [20],
'mini_batch': [True]}
for mat in (S, csr_matrix(S)):
for param_name, param_values in non_default_params.items():
for param_value in param_values:
model = SpectralBiclustering(
n_clusters=3,
n_init=3,
init='k-means++',
random_state=0,
)
model.set_params(**dict([(param_name, param_value)]))
if issparse(mat) and model.get_params().get('method') == 'log':
# cannot take log of sparse matrix
assert_raises(ValueError, model.fit, mat)
continue
else:
model.fit(mat)
assert_equal(model.rows_.shape, (9, 30))
assert_equal(model.columns_.shape, (9, 30))
assert_array_equal(model.rows_.sum(axis=0),
np.repeat(3, 30))
assert_array_equal(model.columns_.sum(axis=0),
np.repeat(3, 30))
assert_equal(consensus_score(model.biclusters_,
(rows, cols)), 1)
_test_shape_indices(model)
def _do_scale_test(scaled):
"""Check that rows sum to one constant, and columns to another."""
row_sum = scaled.sum(axis=1)
col_sum = scaled.sum(axis=0)
if issparse(scaled):
row_sum = np.asarray(row_sum).squeeze()
col_sum = np.asarray(col_sum).squeeze()
assert_array_almost_equal(row_sum, np.tile(row_sum.mean(), 100),
decimal=1)
assert_array_almost_equal(col_sum, np.tile(col_sum.mean(), 100),
decimal=1)
def _do_bistochastic_test(scaled):
"""Check that rows and columns sum to the same constant."""
_do_scale_test(scaled)
assert_almost_equal(scaled.sum(axis=0).mean(),
scaled.sum(axis=1).mean(),
decimal=1)
def test_scale_normalize():
generator = np.random.RandomState(0)
X = generator.rand(100, 100)
for mat in (X, csr_matrix(X)):
scaled, _, _ = _scale_normalize(mat)
_do_scale_test(scaled)
if issparse(mat):
assert issparse(scaled)
def test_bistochastic_normalize():
generator = np.random.RandomState(0)
X = generator.rand(100, 100)
for mat in (X, csr_matrix(X)):
scaled = _bistochastic_normalize(mat)
_do_bistochastic_test(scaled)
if issparse(mat):
assert issparse(scaled)
def test_log_normalize():
# adding any constant to a log-scaled matrix should make it
# bistochastic
generator = np.random.RandomState(0)
mat = generator.rand(100, 100)
scaled = _log_normalize(mat) + 1
_do_bistochastic_test(scaled)
def test_fit_best_piecewise():
model = SpectralBiclustering(random_state=0)
vectors = np.array([[0, 0, 0, 1, 1, 1],
[2, 2, 2, 3, 3, 3],
[0, 1, 2, 3, 4, 5]])
best = model._fit_best_piecewise(vectors, n_best=2, n_clusters=2)
assert_array_equal(best, vectors[:2])
def test_project_and_cluster():
model = SpectralBiclustering(random_state=0)
data = np.array([[1, 1, 1],
[1, 1, 1],
[3, 6, 3],
[3, 6, 3]])
vectors = np.array([[1, 0],
[0, 1],
[0, 0]])
for mat in (data, csr_matrix(data)):
labels = model._project_and_cluster(data, vectors,
n_clusters=2)
assert_array_equal(labels, [0, 0, 1, 1])
def test_perfect_checkerboard():
raise SkipTest("This test is failing on the buildbot, but cannot"
" reproduce. Temporarily disabling it until it can be"
" reproduced and fixed.")
model = SpectralBiclustering(3, svd_method="arpack", random_state=0)
S, rows, cols = make_checkerboard((30, 30), 3, noise=0,
random_state=0)
model.fit(S)
assert_equal(consensus_score(model.biclusters_,
(rows, cols)), 1)
S, rows, cols = make_checkerboard((40, 30), 3, noise=0,
random_state=0)
model.fit(S)
assert_equal(consensus_score(model.biclusters_,
(rows, cols)), 1)
S, rows, cols = make_checkerboard((30, 40), 3, noise=0,
random_state=0)
model.fit(S)
assert_equal(consensus_score(model.biclusters_,
(rows, cols)), 1)
def test_errors():
data = np.arange(25).reshape((5, 5))
model = SpectralBiclustering(n_clusters=(3, 3, 3))
assert_raises(ValueError, model.fit, data)
model = SpectralBiclustering(n_clusters='abc')
assert_raises(ValueError, model.fit, data)
model = SpectralBiclustering(n_clusters=(3, 'abc'))
assert_raises(ValueError, model.fit, data)
model = SpectralBiclustering(method='unknown')
assert_raises(ValueError, model.fit, data)
model = SpectralBiclustering(svd_method='unknown')
assert_raises(ValueError, model.fit, data)
model = SpectralBiclustering(n_components=0)
assert_raises(ValueError, model.fit, data)
model = SpectralBiclustering(n_best=0)
assert_raises(ValueError, model.fit, data)
model = SpectralBiclustering(n_components=3, n_best=4)
assert_raises(ValueError, model.fit, data)
model = SpectralBiclustering()
data = np.arange(27).reshape((3, 3, 3))
assert_raises(ValueError, model.fit, data)
| bsd-3-clause |
wesm/arrow | python/pyarrow/feather.py | 1 | 9058 | # Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
import os
from pyarrow.pandas_compat import _pandas_api # noqa
from pyarrow.lib import (Codec, FeatherError, Table, # noqa
concat_tables, schema)
import pyarrow.lib as ext
from pyarrow.vendored.version import Version
def _check_pandas_version():
if _pandas_api.loose_version < Version('0.17.0'):
raise ImportError("feather requires pandas >= 0.17.0")
class FeatherDataset:
"""
Encapsulates details of reading a list of Feather files.
Parameters
----------
path_or_paths : List[str]
A list of file names
validate_schema : bool, default True
Check that individual file schemas are all the same / compatible
"""
def __init__(self, path_or_paths, validate_schema=True):
self.paths = path_or_paths
self.validate_schema = validate_schema
def read_table(self, columns=None):
"""
Read multiple feather files as a single pyarrow.Table
Parameters
----------
columns : List[str]
Names of columns to read from the file
Returns
-------
pyarrow.Table
Content of the file as a table (of columns)
"""
_fil = read_table(self.paths[0], columns=columns)
self._tables = [_fil]
self.schema = _fil.schema
for path in self.paths[1:]:
table = read_table(path, columns=columns)
if self.validate_schema:
self.validate_schemas(path, table)
self._tables.append(table)
return concat_tables(self._tables)
def validate_schemas(self, piece, table):
if not self.schema.equals(table.schema):
raise ValueError('Schema in {!s} was different. \n'
'{!s}\n\nvs\n\n{!s}'
.format(piece, self.schema,
table.schema))
def read_pandas(self, columns=None, use_threads=True):
"""
Read multiple Parquet files as a single pandas DataFrame
Parameters
----------
columns : List[str]
Names of columns to read from the file
use_threads : bool, default True
Use multiple threads when converting to pandas
Returns
-------
pandas.DataFrame
Content of the file as a pandas DataFrame (of columns)
"""
_check_pandas_version()
return self.read_table(columns=columns).to_pandas(
use_threads=use_threads)
def check_chunked_overflow(name, col):
if col.num_chunks == 1:
return
if col.type in (ext.binary(), ext.string()):
raise ValueError("Column '{}' exceeds 2GB maximum capacity of "
"a Feather binary column. This restriction may be "
"lifted in the future".format(name))
else:
# TODO(wesm): Not sure when else this might be reached
raise ValueError("Column '{}' of type {} was chunked on conversion "
"to Arrow and cannot be currently written to "
"Feather format".format(name, str(col.type)))
_FEATHER_SUPPORTED_CODECS = {'lz4', 'zstd', 'uncompressed'}
def write_feather(df, dest, compression=None, compression_level=None,
chunksize=None, version=2):
"""
Write a pandas.DataFrame to Feather format.
Parameters
----------
df : pandas.DataFrame or pyarrow.Table
Data to write out as Feather format.
dest : str
Local destination path.
compression : string, default None
Can be one of {"zstd", "lz4", "uncompressed"}. The default of None uses
LZ4 for V2 files if it is available, otherwise uncompressed.
compression_level : int, default None
Use a compression level particular to the chosen compressor. If None
use the default compression level
chunksize : int, default None
For V2 files, the internal maximum size of Arrow RecordBatch chunks
when writing the Arrow IPC file format. None means use the default,
which is currently 64K
version : int, default 2
Feather file version. Version 2 is the current. Version 1 is the more
limited legacy format
"""
if _pandas_api.have_pandas:
_check_pandas_version()
if (_pandas_api.has_sparse and
isinstance(df, _pandas_api.pd.SparseDataFrame)):
df = df.to_dense()
if _pandas_api.is_data_frame(df):
table = Table.from_pandas(df, preserve_index=False)
if version == 1:
# Version 1 does not chunking
for i, name in enumerate(table.schema.names):
col = table[i]
check_chunked_overflow(name, col)
else:
table = df
if version == 1:
if len(table.column_names) > len(set(table.column_names)):
raise ValueError("cannot serialize duplicate column names")
if compression is not None:
raise ValueError("Feather V1 files do not support compression "
"option")
if chunksize is not None:
raise ValueError("Feather V1 files do not support chunksize "
"option")
else:
if compression is None and Codec.is_available('lz4_frame'):
compression = 'lz4'
elif (compression is not None and
compression not in _FEATHER_SUPPORTED_CODECS):
raise ValueError('compression="{}" not supported, must be '
'one of {}'.format(compression,
_FEATHER_SUPPORTED_CODECS))
try:
ext.write_feather(table, dest, compression=compression,
compression_level=compression_level,
chunksize=chunksize, version=version)
except Exception:
if isinstance(dest, str):
try:
os.remove(dest)
except os.error:
pass
raise
def read_feather(source, columns=None, use_threads=True, memory_map=True):
"""
Read a pandas.DataFrame from Feather format. To read as pyarrow.Table use
feather.read_table.
Parameters
----------
source : str file path, or file-like object
columns : sequence, optional
Only read a specific set of columns. If not provided, all columns are
read.
use_threads: bool, default True
Whether to parallelize reading using multiple threads.
memory_map : boolean, default True
Use memory mapping when opening file on disk
Returns
-------
df : pandas.DataFrame
"""
_check_pandas_version()
return (read_table(source, columns=columns, memory_map=memory_map)
.to_pandas(use_threads=use_threads))
def read_table(source, columns=None, memory_map=True):
"""
Read a pyarrow.Table from Feather format
Parameters
----------
source : str file path, or file-like object
columns : sequence, optional
Only read a specific set of columns. If not provided, all columns are
read.
memory_map : boolean, default True
Use memory mapping when opening file on disk
Returns
-------
table : pyarrow.Table
"""
reader = ext.FeatherReader()
reader.open(source, use_memory_map=memory_map)
if columns is None:
return reader.read()
column_types = [type(column) for column in columns]
if all(map(lambda t: t == int, column_types)):
table = reader.read_indices(columns)
elif all(map(lambda t: t == str, column_types)):
table = reader.read_names(columns)
else:
column_type_names = [t.__name__ for t in column_types]
raise TypeError("Columns must be indices or names. "
"Got columns {} of types {}"
.format(columns, column_type_names))
# Feather v1 already respects the column selection
if reader.version < 3:
return table
# Feather v2 reads with sorted / deduplicated selection
elif sorted(set(columns)) == columns:
return table
else:
# follow exact order / selection of names
return table.select(columns)
| apache-2.0 |
kmiernik/Pyspectr | other/spectrum_profiler.py | 2 | 2296 | #!/usr/bin/env python3
"""
K. Miernik 2012
[email protected]
Performes fit to the decay part for all channels in E vs time spectrum
"""
import sys
import argparse
import math
import numpy
from lmfit import minimize, Parameters, report_errors
import matplotlib.pyplot as plt
import Pyspectr.hisfile as hisfile
class GeneralError(Exception):
"""General error class
"""
def __init__(self, msg = ''):
self.msg = msg
def __str__(self):
return repr(self.msg)
def decay(params, data_x):
T1 = params['T1'].value
A = params['A'].value
tau = params['tau'].value
return A * numpy.exp(-(data_x - T1) / tau)
def residual(params, data_x, data_y, data_dy):
model = fitfunc(params, data_x)
return (data_y - model) / data_dy
if __name__ == "__main__":
parser = argparse.ArgumentParser(description='')
parser.add_argument('in_file', help='Input file')
args = parser.parse_args()
hisId = 2681
T1 = 200
T2 = 300
his = hisfile.HisFile(args.in_file)
dim, xaxis, yaxis, data = his.load_histogram(hisId)
#data = data.transpose()
fitfunc = decay
params = Parameters()
params.add('T1', value=T1, vary=False)
params.add('A', value=100.0, min=0.0)
params.add('tau', value=100.0, min=0.0)
sys.stderr.write('.')
symbol = 0
for E in range(2, data.shape[0] - 1, 3):
symbol += 1
data_slice = sum(data[E-1:E+2])[T1:T2]
dy = numpy.sqrt(numpy.abs(data_slice))
for i, v in enumerate(dy):
if dy[i] == 0:
dy[i] = 1.0
data_sum_err = math.sqrt(dy.sum())
if data_slice.sum() - data_sum_err <= 0:
continue
params['A'].value = 100.0
params['tau'].value = 100.0
result = minimize(residual, params,
args=(yaxis[T1:T2], data_slice, dy))
scale = 0.01
print(E, result.params['tau'].value * scale * math.log(2),
result.params['tau'].stderr * scale * math.log(2))
sys.stderr.write('\r')
if symbol % 3 == 0:
sys.stderr.write('.')
elif symbol % 3 == 1:
sys.stderr.write('o')
else:
sys.stderr.write('*')
sys.stderr.write('\n')
| gpl-3.0 |
nkmk/python-snippets | notebook/sklearn_precision_score_average.py | 1 | 1571 | from sklearn.metrics import precision_score
from sklearn.metrics import confusion_matrix
y_true = [0, 0, 0, 0, 0, 1, 1, 1, 1, 1]
y_pred = [0, 1, 1, 1, 1, 0, 0, 0, 1, 1]
print(precision_score(y_true, y_pred))
# 0.3333333333333333
print(precision_score(y_true, y_pred, pos_label=0))
# 0.25
print(precision_score(y_true, y_pred, average=None))
# [0.25 0.33333333]
print(precision_score(y_true, y_pred, average='macro'))
# 0.29166666666666663
print(precision_score(y_true, y_pred, average='micro'))
# 0.3
print(confusion_matrix(y_true, y_pred))
# [[1 4]
# [3 2]]
print(confusion_matrix(y_true, y_pred, labels=[1, 0]))
# [[2 3]
# [4 1]]
print(precision_score(y_true, y_pred, average='weighted'))
# 0.29166666666666663
y_true_2 = [0, 1, 1, 1, 1]
y_pred_2 = [0, 0, 0, 0, 1]
print(confusion_matrix(y_true_2, y_pred_2))
# [[1 0]
# [3 1]]
print(confusion_matrix(y_true_2, y_pred_2, labels=[1, 0]))
# [[1 3]
# [0 1]]
print(precision_score(y_true_2, y_pred_2))
# 1.0
print(precision_score(y_true_2, y_pred_2, pos_label=0))
# 0.25
print(precision_score(y_true_2, y_pred_2, average='macro'))
# 0.625
print(precision_score(y_true_2, y_pred_2, average='micro'))
# 0.4
print(precision_score(y_true_2, y_pred_2, average='weighted'))
# 0.85
y_true_ab = ['A', 'A', 'A', 'A', 'A', 'B', 'B', 'B', 'B', 'B']
y_pred_ab = ['A', 'B', 'B', 'B', 'B', 'A', 'A', 'A', 'B', 'B']
# print(precision_score(y_true_ab, y_pred_ab))
# ValueError: pos_label=1 is not a valid label: array(['A', 'B'], dtype='<U1')
print(precision_score(y_true_ab, y_pred_ab, pos_label='A'))
# 0.25
| mit |
Lawrence-Liu/scikit-learn | sklearn/tree/tests/test_export.py | 130 | 9950 | """
Testing for export functions of decision trees (sklearn.tree.export).
"""
from re import finditer
from numpy.testing import assert_equal
from nose.tools import assert_raises
from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.tree import export_graphviz
from sklearn.externals.six import StringIO
from sklearn.utils.testing import assert_in
# toy sample
X = [[-2, -1], [-1, -1], [-1, -2], [1, 1], [1, 2], [2, 1]]
y = [-1, -1, -1, 1, 1, 1]
y2 = [[-1, 1], [-1, 2], [-1, 3], [1, 1], [1, 2], [1, 3]]
w = [1, 1, 1, .5, .5, .5]
def test_graphviz_toy():
# Check correctness of export_graphviz
clf = DecisionTreeClassifier(max_depth=3,
min_samples_split=1,
criterion="gini",
random_state=2)
clf.fit(X, y)
# Test export code
out = StringIO()
export_graphviz(clf, out_file=out)
contents1 = out.getvalue()
contents2 = 'digraph Tree {\n' \
'node [shape=box] ;\n' \
'0 [label="X[0] <= 0.0\\ngini = 0.5\\nsamples = 6\\n' \
'value = [3, 3]"] ;\n' \
'1 [label="gini = 0.0\\nsamples = 3\\nvalue = [3, 0]"] ;\n' \
'0 -> 1 [labeldistance=2.5, labelangle=45, ' \
'headlabel="True"] ;\n' \
'2 [label="gini = 0.0\\nsamples = 3\\nvalue = [0, 3]"] ;\n' \
'0 -> 2 [labeldistance=2.5, labelangle=-45, ' \
'headlabel="False"] ;\n' \
'}'
assert_equal(contents1, contents2)
# Test with feature_names
out = StringIO()
export_graphviz(clf, out_file=out, feature_names=["feature0", "feature1"])
contents1 = out.getvalue()
contents2 = 'digraph Tree {\n' \
'node [shape=box] ;\n' \
'0 [label="feature0 <= 0.0\\ngini = 0.5\\nsamples = 6\\n' \
'value = [3, 3]"] ;\n' \
'1 [label="gini = 0.0\\nsamples = 3\\nvalue = [3, 0]"] ;\n' \
'0 -> 1 [labeldistance=2.5, labelangle=45, ' \
'headlabel="True"] ;\n' \
'2 [label="gini = 0.0\\nsamples = 3\\nvalue = [0, 3]"] ;\n' \
'0 -> 2 [labeldistance=2.5, labelangle=-45, ' \
'headlabel="False"] ;\n' \
'}'
assert_equal(contents1, contents2)
# Test with class_names
out = StringIO()
export_graphviz(clf, out_file=out, class_names=["yes", "no"])
contents1 = out.getvalue()
contents2 = 'digraph Tree {\n' \
'node [shape=box] ;\n' \
'0 [label="X[0] <= 0.0\\ngini = 0.5\\nsamples = 6\\n' \
'value = [3, 3]\\nclass = yes"] ;\n' \
'1 [label="gini = 0.0\\nsamples = 3\\nvalue = [3, 0]\\n' \
'class = yes"] ;\n' \
'0 -> 1 [labeldistance=2.5, labelangle=45, ' \
'headlabel="True"] ;\n' \
'2 [label="gini = 0.0\\nsamples = 3\\nvalue = [0, 3]\\n' \
'class = no"] ;\n' \
'0 -> 2 [labeldistance=2.5, labelangle=-45, ' \
'headlabel="False"] ;\n' \
'}'
assert_equal(contents1, contents2)
# Test plot_options
out = StringIO()
export_graphviz(clf, out_file=out, filled=True, impurity=False,
proportion=True, special_characters=True, rounded=True)
contents1 = out.getvalue()
contents2 = 'digraph Tree {\n' \
'node [shape=box, style="filled, rounded", color="black", ' \
'fontname=helvetica] ;\n' \
'edge [fontname=helvetica] ;\n' \
'0 [label=<X<SUB>0</SUB> ≤ 0.0<br/>samples = 100.0%<br/>' \
'value = [0.5, 0.5]>, fillcolor="#e5813900"] ;\n' \
'1 [label=<samples = 50.0%<br/>value = [1.0, 0.0]>, ' \
'fillcolor="#e58139ff"] ;\n' \
'0 -> 1 [labeldistance=2.5, labelangle=45, ' \
'headlabel="True"] ;\n' \
'2 [label=<samples = 50.0%<br/>value = [0.0, 1.0]>, ' \
'fillcolor="#399de5ff"] ;\n' \
'0 -> 2 [labeldistance=2.5, labelangle=-45, ' \
'headlabel="False"] ;\n' \
'}'
assert_equal(contents1, contents2)
# Test max_depth
out = StringIO()
export_graphviz(clf, out_file=out, max_depth=0, class_names=True)
contents1 = out.getvalue()
contents2 = 'digraph Tree {\n' \
'node [shape=box] ;\n' \
'0 [label="X[0] <= 0.0\\ngini = 0.5\\nsamples = 6\\n' \
'value = [3, 3]\\nclass = y[0]"] ;\n' \
'1 [label="(...)"] ;\n' \
'0 -> 1 ;\n' \
'2 [label="(...)"] ;\n' \
'0 -> 2 ;\n' \
'}'
assert_equal(contents1, contents2)
# Test max_depth with plot_options
out = StringIO()
export_graphviz(clf, out_file=out, max_depth=0, filled=True,
node_ids=True)
contents1 = out.getvalue()
contents2 = 'digraph Tree {\n' \
'node [shape=box, style="filled", color="black"] ;\n' \
'0 [label="node #0\\nX[0] <= 0.0\\ngini = 0.5\\n' \
'samples = 6\\nvalue = [3, 3]", fillcolor="#e5813900"] ;\n' \
'1 [label="(...)", fillcolor="#C0C0C0"] ;\n' \
'0 -> 1 ;\n' \
'2 [label="(...)", fillcolor="#C0C0C0"] ;\n' \
'0 -> 2 ;\n' \
'}'
assert_equal(contents1, contents2)
# Test multi-output with weighted samples
clf = DecisionTreeClassifier(max_depth=2,
min_samples_split=1,
criterion="gini",
random_state=2)
clf = clf.fit(X, y2, sample_weight=w)
out = StringIO()
export_graphviz(clf, out_file=out, filled=True, impurity=False)
contents1 = out.getvalue()
contents2 = 'digraph Tree {\n' \
'node [shape=box, style="filled", color="black"] ;\n' \
'0 [label="X[0] <= 0.0\\nsamples = 6\\n' \
'value = [[3.0, 1.5, 0.0]\\n' \
'[1.5, 1.5, 1.5]]", fillcolor="#e5813900"] ;\n' \
'1 [label="X[1] <= -1.5\\nsamples = 3\\n' \
'value = [[3, 0, 0]\\n[1, 1, 1]]", ' \
'fillcolor="#e5813965"] ;\n' \
'0 -> 1 [labeldistance=2.5, labelangle=45, ' \
'headlabel="True"] ;\n' \
'2 [label="samples = 1\\nvalue = [[1, 0, 0]\\n' \
'[0, 0, 1]]", fillcolor="#e58139ff"] ;\n' \
'1 -> 2 ;\n' \
'3 [label="samples = 2\\nvalue = [[2, 0, 0]\\n' \
'[1, 1, 0]]", fillcolor="#e581398c"] ;\n' \
'1 -> 3 ;\n' \
'4 [label="X[0] <= 1.5\\nsamples = 3\\n' \
'value = [[0.0, 1.5, 0.0]\\n[0.5, 0.5, 0.5]]", ' \
'fillcolor="#e5813965"] ;\n' \
'0 -> 4 [labeldistance=2.5, labelangle=-45, ' \
'headlabel="False"] ;\n' \
'5 [label="samples = 2\\nvalue = [[0.0, 1.0, 0.0]\\n' \
'[0.5, 0.5, 0.0]]", fillcolor="#e581398c"] ;\n' \
'4 -> 5 ;\n' \
'6 [label="samples = 1\\nvalue = [[0.0, 0.5, 0.0]\\n' \
'[0.0, 0.0, 0.5]]", fillcolor="#e58139ff"] ;\n' \
'4 -> 6 ;\n' \
'}'
assert_equal(contents1, contents2)
# Test regression output with plot_options
clf = DecisionTreeRegressor(max_depth=3,
min_samples_split=1,
criterion="mse",
random_state=2)
clf.fit(X, y)
out = StringIO()
export_graphviz(clf, out_file=out, filled=True, leaves_parallel=True,
rotate=True, rounded=True)
contents1 = out.getvalue()
contents2 = 'digraph Tree {\n' \
'node [shape=box, style="filled, rounded", color="black", ' \
'fontname=helvetica] ;\n' \
'graph [ranksep=equally, splines=polyline] ;\n' \
'edge [fontname=helvetica] ;\n' \
'rankdir=LR ;\n' \
'0 [label="X[0] <= 0.0\\nmse = 1.0\\nsamples = 6\\n' \
'value = 0.0", fillcolor="#e581397f"] ;\n' \
'1 [label="mse = 0.0\\nsamples = 3\\nvalue = -1.0", ' \
'fillcolor="#e5813900"] ;\n' \
'0 -> 1 [labeldistance=2.5, labelangle=-45, ' \
'headlabel="True"] ;\n' \
'2 [label="mse = 0.0\\nsamples = 3\\nvalue = 1.0", ' \
'fillcolor="#e58139ff"] ;\n' \
'0 -> 2 [labeldistance=2.5, labelangle=45, ' \
'headlabel="False"] ;\n' \
'{rank=same ; 0} ;\n' \
'{rank=same ; 1; 2} ;\n' \
'}'
assert_equal(contents1, contents2)
def test_graphviz_errors():
# Check for errors of export_graphviz
clf = DecisionTreeClassifier(max_depth=3, min_samples_split=1)
clf.fit(X, y)
# Check feature_names error
out = StringIO()
assert_raises(IndexError, export_graphviz, clf, out, feature_names=[])
# Check class_names error
out = StringIO()
assert_raises(IndexError, export_graphviz, clf, out, class_names=[])
def test_friedman_mse_in_graphviz():
clf = DecisionTreeRegressor(criterion="friedman_mse", random_state=0)
clf.fit(X, y)
dot_data = StringIO()
export_graphviz(clf, out_file=dot_data)
clf = GradientBoostingClassifier(n_estimators=2, random_state=0)
clf.fit(X, y)
for estimator in clf.estimators_:
export_graphviz(estimator[0], out_file=dot_data)
for finding in finditer("\[.*?samples.*?\]", dot_data.getvalue()):
assert_in("friedman_mse", finding.group())
| bsd-3-clause |
madjelan/scikit-learn | sklearn/gaussian_process/tests/test_gaussian_process.py | 267 | 6813 | """
Testing for Gaussian Process module (sklearn.gaussian_process)
"""
# Author: Vincent Dubourg <[email protected]>
# Licence: BSD 3 clause
from nose.tools import raises
from nose.tools import assert_true
import numpy as np
from sklearn.gaussian_process import GaussianProcess
from sklearn.gaussian_process import regression_models as regression
from sklearn.gaussian_process import correlation_models as correlation
from sklearn.datasets import make_regression
from sklearn.utils.testing import assert_greater
f = lambda x: x * np.sin(x)
X = np.atleast_2d([1., 3., 5., 6., 7., 8.]).T
X2 = np.atleast_2d([2., 4., 5.5, 6.5, 7.5]).T
y = f(X).ravel()
def test_1d(regr=regression.constant, corr=correlation.squared_exponential,
random_start=10, beta0=None):
# MLE estimation of a one-dimensional Gaussian Process model.
# Check random start optimization.
# Test the interpolating property.
gp = GaussianProcess(regr=regr, corr=corr, beta0=beta0,
theta0=1e-2, thetaL=1e-4, thetaU=1e-1,
random_start=random_start, verbose=False).fit(X, y)
y_pred, MSE = gp.predict(X, eval_MSE=True)
y2_pred, MSE2 = gp.predict(X2, eval_MSE=True)
assert_true(np.allclose(y_pred, y) and np.allclose(MSE, 0.)
and np.allclose(MSE2, 0., atol=10))
def test_2d(regr=regression.constant, corr=correlation.squared_exponential,
random_start=10, beta0=None):
# MLE estimation of a two-dimensional Gaussian Process model accounting for
# anisotropy. Check random start optimization.
# Test the interpolating property.
b, kappa, e = 5., .5, .1
g = lambda x: b - x[:, 1] - kappa * (x[:, 0] - e) ** 2.
X = np.array([[-4.61611719, -6.00099547],
[4.10469096, 5.32782448],
[0.00000000, -0.50000000],
[-6.17289014, -4.6984743],
[1.3109306, -6.93271427],
[-5.03823144, 3.10584743],
[-2.87600388, 6.74310541],
[5.21301203, 4.26386883]])
y = g(X).ravel()
thetaL = [1e-4] * 2
thetaU = [1e-1] * 2
gp = GaussianProcess(regr=regr, corr=corr, beta0=beta0,
theta0=[1e-2] * 2, thetaL=thetaL,
thetaU=thetaU,
random_start=random_start, verbose=False)
gp.fit(X, y)
y_pred, MSE = gp.predict(X, eval_MSE=True)
assert_true(np.allclose(y_pred, y) and np.allclose(MSE, 0.))
eps = np.finfo(gp.theta_.dtype).eps
assert_true(np.all(gp.theta_ >= thetaL - eps)) # Lower bounds of hyperparameters
assert_true(np.all(gp.theta_ <= thetaU + eps)) # Upper bounds of hyperparameters
def test_2d_2d(regr=regression.constant, corr=correlation.squared_exponential,
random_start=10, beta0=None):
# MLE estimation of a two-dimensional Gaussian Process model accounting for
# anisotropy. Check random start optimization.
# Test the GP interpolation for 2D output
b, kappa, e = 5., .5, .1
g = lambda x: b - x[:, 1] - kappa * (x[:, 0] - e) ** 2.
f = lambda x: np.vstack((g(x), g(x))).T
X = np.array([[-4.61611719, -6.00099547],
[4.10469096, 5.32782448],
[0.00000000, -0.50000000],
[-6.17289014, -4.6984743],
[1.3109306, -6.93271427],
[-5.03823144, 3.10584743],
[-2.87600388, 6.74310541],
[5.21301203, 4.26386883]])
y = f(X)
gp = GaussianProcess(regr=regr, corr=corr, beta0=beta0,
theta0=[1e-2] * 2, thetaL=[1e-4] * 2,
thetaU=[1e-1] * 2,
random_start=random_start, verbose=False)
gp.fit(X, y)
y_pred, MSE = gp.predict(X, eval_MSE=True)
assert_true(np.allclose(y_pred, y) and np.allclose(MSE, 0.))
@raises(ValueError)
def test_wrong_number_of_outputs():
gp = GaussianProcess()
gp.fit([[1, 2, 3], [4, 5, 6]], [1, 2, 3])
def test_more_builtin_correlation_models(random_start=1):
# Repeat test_1d and test_2d for several built-in correlation
# models specified as strings.
all_corr = ['absolute_exponential', 'squared_exponential', 'cubic',
'linear']
for corr in all_corr:
test_1d(regr='constant', corr=corr, random_start=random_start)
test_2d(regr='constant', corr=corr, random_start=random_start)
test_2d_2d(regr='constant', corr=corr, random_start=random_start)
def test_ordinary_kriging():
# Repeat test_1d and test_2d with given regression weights (beta0) for
# different regression models (Ordinary Kriging).
test_1d(regr='linear', beta0=[0., 0.5])
test_1d(regr='quadratic', beta0=[0., 0.5, 0.5])
test_2d(regr='linear', beta0=[0., 0.5, 0.5])
test_2d(regr='quadratic', beta0=[0., 0.5, 0.5, 0.5, 0.5, 0.5])
test_2d_2d(regr='linear', beta0=[0., 0.5, 0.5])
test_2d_2d(regr='quadratic', beta0=[0., 0.5, 0.5, 0.5, 0.5, 0.5])
def test_no_normalize():
gp = GaussianProcess(normalize=False).fit(X, y)
y_pred = gp.predict(X)
assert_true(np.allclose(y_pred, y))
def test_random_starts():
# Test that an increasing number of random-starts of GP fitting only
# increases the reduced likelihood function of the optimal theta.
n_samples, n_features = 50, 3
np.random.seed(0)
rng = np.random.RandomState(0)
X = rng.randn(n_samples, n_features) * 2 - 1
y = np.sin(X).sum(axis=1) + np.sin(3 * X).sum(axis=1)
best_likelihood = -np.inf
for random_start in range(1, 5):
gp = GaussianProcess(regr="constant", corr="squared_exponential",
theta0=[1e-0] * n_features,
thetaL=[1e-4] * n_features,
thetaU=[1e+1] * n_features,
random_start=random_start, random_state=0,
verbose=False).fit(X, y)
rlf = gp.reduced_likelihood_function()[0]
assert_greater(rlf, best_likelihood - np.finfo(np.float32).eps)
best_likelihood = rlf
def test_mse_solving():
# test the MSE estimate to be sane.
# non-regression test for ignoring off-diagonals of feature covariance,
# testing with nugget that renders covariance useless, only
# using the mean function, with low effective rank of data
gp = GaussianProcess(corr='absolute_exponential', theta0=1e-4,
thetaL=1e-12, thetaU=1e-2, nugget=1e-2,
optimizer='Welch', regr="linear", random_state=0)
X, y = make_regression(n_informative=3, n_features=60, noise=50,
random_state=0, effective_rank=1)
gp.fit(X, y)
assert_greater(1000, gp.predict(X, eval_MSE=True)[1].mean())
| bsd-3-clause |
kgullikson88/HET-Scripts | Search2.py | 1 | 15434 | from scipy.interpolate import InterpolatedUnivariateSpline as interp
import os
import sys
import numpy as np
import DataStructures
import matplotlib.pyplot as plt
import Correlate
import FitsUtils
import FindContinuum
# import Units
from astropy import units, constants
import FittingUtilities
homedir = os.environ["HOME"]
modeldir = homedir + "/School/Research/Models/Sorted/Stellar/Vband/"
#Define regions contaminated by telluric residuals or other defects. We will not use those regions in the cross-correlation
badregions = [[588.8, 589.9],
[627.1, 635.4]]
badregions = [[0, 466],
#badregions = [[0, 540],
[567.5, 575.5],
[587.5, 593],
[627, 634.5],
[686, 706],
[716, 742],
[759, 9e9]]
#Set up model list
model_list = [modeldir + "lte30-4.00-0.0.AGS.Cond.PHOENIX-ACES-2009.HighRes.7.sorted",
modeldir + "lte32-4.00-0.0.AGS.Cond.PHOENIX-ACES-2009.HighRes.7.sorted",
modeldir + "lte34-4.00-0.0.AGS.Cond.PHOENIX-ACES-2009.HighRes.7.sorted",
modeldir + "lte35-4.00-0.0.AGS.Cond.PHOENIX-ACES-2009.HighRes.7.sorted",
modeldir + "lte36-4.00-0.0.AGS.Cond.PHOENIX-ACES-2009.HighRes.7.sorted",
modeldir + "lte37-4.00-0.0.AGS.Cond.PHOENIX-ACES-2009.HighRes.7.sorted",
modeldir + "lte38-4.00-0.0.AGS.Cond.PHOENIX-ACES-2009.HighRes.7.sorted",
modeldir + "lte39-4.00-0.0.AGS.Cond.PHOENIX-ACES-2009.HighRes.7.sorted",
modeldir + "lte40-4.00-0.0.AGS.Cond.PHOENIX-ACES-2009.HighRes.7.sorted",
modeldir + "lte42-4.00-0.0.AGS.Cond.PHOENIX-ACES-2009.HighRes.7.sorted",
modeldir + "lte44-4.00-0.0.AGS.Cond.PHOENIX-ACES-2009.HighRes.7.sorted",
modeldir + "lte46-4.00-0.0.AGS.Cond.PHOENIX-ACES-2009.HighRes.7.sorted",
modeldir + "lte48-4.00-0.0.AGS.Cond.PHOENIX-ACES-2009.HighRes.7.sorted",
modeldir + "lte50-4.00-0.0.AGS.Cond.PHOENIX-ACES-2009.HighRes.7.sorted",
modeldir + "lte51-4.00-0.0.AGS.Cond.PHOENIX-ACES-2009.HighRes.7.sorted",
modeldir + "lte52-4.50-0.0.AGS.Cond.PHOENIX-ACES-2009.HighRes.7.sorted",
modeldir + "lte53-4.50-0.0.AGS.Cond.PHOENIX-ACES-2009.HighRes.7.sorted",
modeldir + "lte54-4.50-0.0.AGS.Cond.PHOENIX-ACES-2009.HighRes.7.sorted",
modeldir + "lte55-4.50-0.0.AGS.Cond.PHOENIX-ACES-2009.HighRes.7.sorted",
modeldir + "lte56-4.50-0.0.AGS.Cond.PHOENIX-ACES-2009.HighRes.7.sorted",
modeldir + "lte57-4.00-0.0.AGS.Cond.PHOENIX-ACES-2009.HighRes.7.sorted",
modeldir + "lte58-4.50-0.0.AGS.Cond.PHOENIX-ACES-2009.HighRes.7.sorted",
modeldir + "lte59-4.50-0.0.AGS.Cond.PHOENIX-ACES-2009.HighRes.7.sorted",
modeldir + "lte60-4.50-0.0.AGS.Cond.PHOENIX-ACES-2009.HighRes.7.sorted",
modeldir + "lte61-4.50-0.0.AGS.Cond.PHOENIX-ACES-2009.HighRes.7.sorted",
modeldir + "lte62-4.50-0.0.AGS.Cond.PHOENIX-ACES-2009.HighRes.7.sorted",
modeldir + "lte63-4.50-0.0.AGS.Cond.PHOENIX-ACES-2009.HighRes.7.sorted",
modeldir + "lte64-4.50-0.0.AGS.Cond.PHOENIX-ACES-2009.HighRes.7.sorted",
modeldir + "lte65-4.50-0.0.AGS.Cond.PHOENIX-ACES-2009.HighRes.7.sorted",
modeldir + "lte66-4.50-0.0.AGS.Cond.PHOENIX-ACES-2009.HighRes.7.sorted",
modeldir + "lte67-4.50-0.0.AGS.Cond.PHOENIX-ACES-2009.HighRes.7.sorted",
modeldir + "lte68-4.50-0.0.AGS.Cond.PHOENIX-ACES-2009.HighRes.7.sorted",
modeldir + "lte69-4.00-0.0.AGS.Cond.PHOENIX-ACES-2009.HighRes.7.sorted",
modeldir + "lte69-4.50-0.0.AGS.Cond.PHOENIX-ACES-2009.HighRes.7.sorted",
modeldir + "lte70-4.00-0.0.AGS.Cond.PHOENIX-ACES-2009.HighRes.7.sorted",
modeldir + "lte70-4.50-0.0.AGS.Cond.PHOENIX-ACES-2009.HighRes.7.sorted",
modeldir + "lte72-4.50-0.0.AGS.Cond.PHOENIX-ACES-2009.HighRes.7.sorted",
modeldir + "lte74-4.00-0.0.AGS.Cond.PHOENIX-ACES-2009.HighRes.7.sorted",
modeldir + "lte74-4.50-0.0.AGS.Cond.PHOENIX-ACES-2009.HighRes.7.sorted",
modeldir + "lte76-4.50-0.0.AGS.Cond.PHOENIX-ACES-2009.HighRes.7.sorted",
modeldir + "lte78-4.50-0.0.AGS.Cond.PHOENIX-ACES-2009.HighRes.7.sorted",
modeldir + "lte30-4.0-0.5.Cond.PHOENIX2004.tab.7.sorted",
modeldir + "lte30-4.0+0.5.Cond.PHOENIX2004.tab.7.sorted",
modeldir + "lte31-4.0-0.5.Cond.PHOENIX2004.tab.7.sorted",
modeldir + "lte31-4.0+0.5.Cond.PHOENIX2004.tab.7.sorted",
modeldir + "lte32-4.0-0.5.Cond.PHOENIX2004.tab.7.sorted",
modeldir + "lte32-4.0+0.5.Cond.PHOENIX2004.tab.7.sorted",
modeldir + "lte33-4.0-0.5.Cond.PHOENIX2004.tab.7.sorted",
modeldir + "lte33-4.0+0.5.Cond.PHOENIX2004.tab.7.sorted",
modeldir + "lte34-4.0-0.5.Cond.PHOENIX2004.tab.7.sorted",
modeldir + "lte34-4.0+0.5.Cond.PHOENIX2004.tab.7.sorted",
modeldir + "lte35-4.0-0.5.Cond.PHOENIX2004.tab.7.sorted",
modeldir + "lte35-4.0+0.5.Cond.PHOENIX2004.tab.7.sorted",
modeldir + "lte36-4.0-0.5.Cond.PHOENIX2004.tab.7.sorted",
modeldir + "lte36-4.0+0.5.Cond.PHOENIX2004.tab.7.sorted",
modeldir + "lte37-4.0-0.5.Cond.PHOENIX2004.tab.7.sorted",
modeldir + "lte37-4.0+0.5.Cond.PHOENIX2004.tab.7.sorted",
modeldir + "lte38-4.0-0.5.Cond.PHOENIX2004.tab.7.sorted",
modeldir + "lte38-4.0+0.5.Cond.PHOENIX2004.tab.7.sorted",
modeldir + "lte39-4.0-0.5.Cond.PHOENIX2004.tab.7.sorted",
modeldir + "lte39-4.0+0.5.Cond.PHOENIX2004.tab.7.sorted",
modeldir + "lte40-4.0-0.5.Cond.PHOENIX2004.tab.7.sorted",
modeldir + "lte40-4.0+0.5.Cond.PHOENIX2004.tab.7.sorted",
modeldir + "lte41-4.0-0.5.Cond.PHOENIX2004.tab.7.sorted",
modeldir + "lte41-4.0+0.5.Cond.PHOENIX2004.tab.7.sorted",
modeldir + "lte42-4.0-0.5.Cond.PHOENIX2004.tab.7.sorted",
modeldir + "lte42-4.0+0.5.Cond.PHOENIX2004.tab.7.sorted",
modeldir + "lte43-4.0-0.5.Cond.PHOENIX2004.tab.7.sorted",
modeldir + "lte43-4.0+0.5.Cond.PHOENIX2004.tab.7.sorted",
modeldir + "lte44-4.0-0.5.Cond.PHOENIX2004.tab.7.sorted",
modeldir + "lte44-4.0+0.5.Cond.PHOENIX2004.tab.7.sorted",
modeldir + "lte45-4.0-0.5.Cond.PHOENIX2004.tab.7.sorted",
modeldir + "lte45-4.0+0.5.Cond.PHOENIX2004.tab.7.sorted",
modeldir + "lte46-4.0-0.5.Cond.PHOENIX2004.tab.7.sorted",
modeldir + "lte46-4.0+0.5.Cond.PHOENIX2004.tab.7.sorted",
modeldir + "lte47-4.0-0.5.Cond.PHOENIX2004.tab.7.sorted",
modeldir + "lte47-4.0+0.5.Cond.PHOENIX2004.tab.7.sorted",
modeldir + "lte48-4.0-0.5.Cond.PHOENIX2004.tab.7.sorted",
modeldir + "lte48-4.0+0.5.Cond.PHOENIX2004.tab.7.sorted",
modeldir + "lte49-4.0-0.5.Cond.PHOENIX2004.tab.7.sorted",
modeldir + "lte49-4.0+0.5.Cond.PHOENIX2004.tab.7.sorted",
modeldir + "lte50-3.5-0.5.Cond.PHOENIX2004.direct.7.sorted",
modeldir + "lte50-4.0+0.5.Cond.PHOENIX2004.direct.7.sorted",
modeldir + "lte51-4.0-0.5.Cond.PHOENIX2004.direct.7.sorted",
modeldir + "lte51-4.0+0.5.Cond.PHOENIX2004.direct.7.sorted",
modeldir + "lte52-4.0-0.5.Cond.PHOENIX2004.direct.7.sorted",
modeldir + "lte52-4.0+0.5.Cond.PHOENIX2004.direct.7.sorted",
modeldir + "lte53-4.0+0.5.Cond.PHOENIX2004.direct.7.sorted",
modeldir + "lte54-4.0-0.5.Cond.PHOENIX2004.direct.7.sorted",
modeldir + "lte54-4.0+0.5.Cond.PHOENIX2004.direct.7.sorted",
modeldir + "lte55-4.0-0.5.Cond.PHOENIX2004.direct.7.sorted",
modeldir + "lte55-4.0+0.5.Cond.PHOENIX2004.direct.7.sorted",
modeldir + "lte56-3.5-0.5.Cond.PHOENIX2004.direct.7.sorted",
modeldir + "lte56-4.0+0.5.Cond.PHOENIX2004.direct.7.sorted",
modeldir + "lte57-4.0-0.5.Cond.PHOENIX2004.direct.7.sorted",
modeldir + "lte57-4.0+0.5.Cond.PHOENIX2004.direct.7.sorted",
modeldir + "lte58-4.0-0.5.Cond.PHOENIX2004.direct.7.sorted",
modeldir + "lte58-4.0+0.5.Cond.PHOENIX2004.direct.7.sorted",
modeldir + "lte59-4.0+0.5.Cond.PHOENIX2004.direct.7.sorted",
modeldir + "lte60-4.0+0.5.Cond.PHOENIX2004.direct.7.sorted",
modeldir + "lte61-4.0-0.5.Cond.PHOENIX2004.direct.7.sorted",
modeldir + "lte61-4.0+0.5.Cond.PHOENIX2004.direct.7.sorted",
modeldir + "lte62-4.0+0.5.Cond.PHOENIX2004.direct.7.sorted",
modeldir + "lte63-4.0-0.5.Cond.PHOENIX2004.direct.7.sorted",
modeldir + "lte63-4.0+0.5.Cond.PHOENIX2004.direct.7.sorted",
modeldir + "lte64-4.0-0.5.Cond.PHOENIX2004.direct.7.sorted",
modeldir + "lte64-4.0+0.5.Cond.PHOENIX2004.direct.7.sorted",
modeldir + "lte65-4.0+0.5.Cond.PHOENIX2004.direct.7.sorted",
modeldir + "lte66-4.0-0.5.Cond.PHOENIX2004.direct.7.sorted",
modeldir + "lte66-4.0+0.5.Cond.PHOENIX2004.direct.7.sorted",
modeldir + "lte67-4.0+0.5.Cond.PHOENIX2004.direct.7.sorted",
modeldir + "lte68-4.0-0.5.Cond.PHOENIX2004.direct.7.sorted",
modeldir + "lte68-4.0+0.5.Cond.PHOENIX2004.direct.7.sorted",
modeldir + "lte69-4.0-0.5.Cond.PHOENIX2004.direct.7.sorted",
modeldir + "lte69-4.0+0.5.Cond.PHOENIX2004.direct.7.sorted",
modeldir + "lte70-3.5-0.5.Cond.PHOENIX2004.direct.7.sorted",
modeldir + "lte70-4.0+0.5.Cond.PHOENIX2004.direct.7.sorted",
modeldir + "lte72-3.5-0.5.Cond.PHOENIX2004.direct.7.sorted",
modeldir + "lte72-4.0+0.5.Cond.PHOENIX2004.direct.7.sorted",
modeldir + "lte74-4.0+0.5.Cond.PHOENIX2004.direct.7.sorted",
modeldir + "lte76-4.0+0.5.Cond.PHOENIX2004.direct.7.sorted",
modeldir + "lte78-3.5-0.5.Cond.PHOENIX2004.direct.7.sorted",
modeldir + "lte78-4.0+0.5.Cond.PHOENIX2004.direct.7.sorted"]
star_list = []
temp_list = []
gravity_list = []
metal_list = []
model_data = []
for fname in model_list:
if "PHOENIX2004" in fname:
temp = int(fname.split("lte")[-1][:2]) * 100
gravity = float(fname.split("lte")[-1][3:6])
metallicity = float(fname.split("lte")[-1][6:10])
elif "PHOENIX-ACES" in fname:
temp = int(fname.split("lte")[-1][:2]) * 100
gravity = float(fname.split("lte")[-1][3:7])
metallicity = float(fname.split("lte")[-1][7:11])
print "Reading in file %s" % fname
x, y = np.loadtxt(fname, usecols=(0, 1), unpack=True)
model_data.append(DataStructures.xypoint(x=x * units.angstrom.to(units.nm) / 1.00026, y=10 ** y))
star_list.append(str(temp))
temp_list.append(temp)
gravity_list.append(gravity)
metal_list.append(metallicity)
if __name__ == "__main__":
#Parse command line arguments:
fileList = []
extensions = True
tellurics = False
trimsize = 100
smooth = False
for arg in sys.argv[1:]:
if "-e" in arg:
extensions = False
if "-t" in arg:
tellurics = True #telluric lines modeled but not removed
if "-s" in arg:
smooth = True
else:
fileList.append(arg)
for fname in fileList:
if extensions:
orders = FitsUtils.MakeXYpoints(fname, extensions=extensions, x="wavelength", y="flux", errors="error")
if tellurics:
model_orders = FitsUtils.MakeXYpoints(fname, extensions=extensions, x="wavelength", y="model")
for i, order in enumerate(orders):
orders[i].cont = FindContinuum.Continuum(order.x, order.y, lowreject=2, highreject=2)
orders[i].y /= model_orders[i].y
else:
orders = FitsUtils.MakeXYpoints(fname, errors=2)
numorders = len(orders)
for i, order in enumerate(orders[::-1]):
#Only use the middle half of each order (lots of noise on the edges)
DATA = interp(order.x, order.y)
CONT = interp(order.x, order.cont)
ERROR = interp(order.x, order.err)
left = int(order.size() / 4.0)
right = int(order.size() * 3.0 / 4.0 + 0.5)
order.x = np.linspace(order.x[left], order.x[right], right - left + 1)
order.y = DATA(order.x)
order.cont = CONT(order.x)
order.err = ERROR(order.x)
#Remove bad regions from the data
for region in badregions:
left = np.searchsorted(order.x, region[0])
right = np.searchsorted(order.x, region[1])
if left == 0 or right == order.size():
order.x = np.delete(order.x, np.arange(left, right))
order.y = np.delete(order.y, np.arange(left, right))
order.cont = np.delete(order.cont, np.arange(left, right))
order.err = np.delete(order.err, np.arange(left, right))
else:
print "Warning! Bad region covers the middle of order %i" % i
print "Interpolating rather than removing"
order.y[left:right] = order.cont[left:right]
order.err[left:right] = 9e9
#Remove whole order if it is too small
remove = False
if order.x.size <= 1:
remove = True
else:
velrange = 3e5 * (np.median(order.x) - order.x[0]) / np.median(order.x)
if velrange <= 1050.0:
remove = True
if remove:
print "Removing order %i" % (numorders - 1 - i)
orders.pop(numorders - 1 - i)
else:
order.cont = FittingUtilities.Continuum(order.x, order.y, lowreject=3, highreject=3)
orders[numorders - 1 - i] = order.copy()
"""
for i, order in enumerate(orders):
plt.plot(order.x, order.y/order.cont+i)
plt.show()
sys.exit()
"""
#Smooth data
if smooth:
for order in orders:
order.y /= FittingUtilities.savitzky_golay(order.y, 91, 5)
output_dir = "Cross_correlations/"
outfilebase = fname.split(".fits")[0]
if "/" in fname:
dirs = fname.split("/")
output_dir = ""
outfilebase = dirs[-1].split(".fits")[0]
for directory in dirs[:-1]:
output_dir = output_dir + directory + "/"
output_dir = output_dir + "Cross_correlations/"
#Do the cross-correlation
for vsini in [10, 20, 30, 40]:
Correlate.PyCorr2(orders, resolution=60000, outdir=output_dir, models=model_data, stars=star_list,
temps=temp_list, gravities=gravity_list, metallicities=metal_list,
vsini=vsini * units.km.to(units.cm), debug=False, outfilebase=outfilebase)
| gpl-3.0 |
yipenggao/moose | python/peacock/tests/postprocessor_tab/test_LineGroupWidgetPostprocessor.py | 4 | 5988 | #!/usr/bin/env python
import sys
import os
import unittest
import shutil
from PyQt5 import QtCore, QtWidgets
from peacock.PostprocessorViewer.PostprocessorDataWidget import PostprocessorDataWidget
from peacock.PostprocessorViewer.plugins.LineGroupWidget import main
from peacock.utils import Testing
import mooseutils
class TestLineGroupWidgetPostprocessor(Testing.PeacockImageTestCase):
"""
Test class for the ArtistToggleWidget which toggles postprocessor lines.
"""
#: QApplication: The main App for QT, this must be static to work correctly.
qapp = QtWidgets.QApplication(sys.argv)
def copyfiles(self):
"""
Copy the data file to a local temporary.
"""
src = os.path.abspath(os.path.join(__file__, '../../input/white_elephant_jan_2016.csv'))
shutil.copyfile(src, self._filename)
def create(self, timer=False):
"""
Creates the widgets for testing.
This is done here rather than in setUp to allow for testing of delayed loading.
"""
self._reader = mooseutils.PostprocessorReader(self._filename)
self._data = PostprocessorDataWidget(self._reader, timer=timer)
# Build the widgets
self._control, self._widget, self._window = main(self._data)
self._widget.currentWidget().FigurePlugin.setFixedSize(QtCore.QSize(625, 625))
def setUp(self):
"""
Creates the GUI containing the ArtistGroupWidget and the matplotlib figure axes.
"""
self._filename = '{}_{}'.format(self.__class__.__name__, 'test.csv')
def tearDown(self):
"""
Clean up.
"""
if os.path.exists(self._filename):
os.remove(self._filename)
def testEmpty(self):
"""
Test that an empty plot is possible.
"""
self.copyfiles()
self.create()
self.assertImage('testEmpty.png')
# Test that controls are initialized and disabled correctly
self.assertEqual(self._control.AxisVariable.currentText(), "time")
self.assertFalse(self._control._toggles['time'].isEnabled(), "Time toggle should be disabled.")
def testSelect(self):
"""
Test that selecting variables works.
"""
self.copyfiles()
self.create()
vars = ['air_temp_set_1', 'precip_accum_set_1']
for var in vars:
self._control._toggles[var].CheckBox.setCheckState(QtCore.Qt.Checked)
self._control._toggles[var].clicked.emit()
self.assertImage('testSelect.png')
self.assertEqual('; '.join(vars), self._window.axes()[0].get_yaxis().get_label().get_text())
self.assertEqual('time', self._window.axes()[0].get_xaxis().get_label().get_text())
# Switch axis
self._control._toggles[vars[0]].PlotAxis.setCurrentIndex(1)
self._control._toggles[vars[0]].clicked.emit()
self.assertImage('testSelect2.png')
self.assertEqual(vars[0], self._window.axes()[1].get_yaxis().get_label().get_text())
self.assertEqual(vars[1], self._window.axes()[0].get_yaxis().get_label().get_text())
self.assertEqual('time', self._window.axes()[0].get_xaxis().get_label().get_text())
def testChangePrimaryVariable(self):
"""
Test that the primary variable may be modified.
"""
self.copyfiles()
self.create()
# Plot something
x_var = 'snow_water_equiv_set_1'
y_var = 'precip_accum_set_1'
self._control._toggles[y_var].CheckBox.setCheckState(QtCore.Qt.Checked)
self._control._toggles[y_var].clicked.emit()
self.assertImage('testChangePrimaryVariable0.png')
# Change the primary variable
self._control.AxisVariable.setCurrentIndex(5)
self._control.AxisVariable.currentIndexChanged.emit(5)
self.assertEqual(self._control.AxisVariable.currentText(), x_var)
self.assertFalse(self._control._toggles[x_var].isEnabled(), "Toggle should be disabled.")
self.assertTrue(self._control._toggles['time'].isEnabled(), "Toggle should be enabled.")
self.assertImage('testChangePrimaryVariable1.png')
def testDelayLoadAndUnload(self):
"""
Test that delayed loading and removal of files works.
"""
self.create()
# Plot should be empty and the message should be visible.
self.assertImage('testEmpty.png')
self.assertTrue(self._control.NoDataMessage.isVisible())
# Load data
self.copyfiles()
self._data.load()
self.assertFalse(self._control.NoDataMessage.isVisible())
# Plot something
var = 'air_temp_set_1'
self._control._toggles[var].CheckBox.setCheckState(QtCore.Qt.Checked)
self._control._toggles[var].clicked.emit()
self.assertImage('testDelayLoadPlot.png')
# Remove data
os.remove(self._filename)
self._data.load()
self.assertTrue(self._control.NoDataMessage.isVisible())
self.assertImage('testEmpty.png')
# Re-load data
self.copyfiles()
self._data.load()
self.assertFalse(self._control.NoDataMessage.isVisible())
self._control._toggles[var].CheckBox.setCheckState(QtCore.Qt.Checked)
self._control._toggles[var].clicked.emit()
self.assertImage('testDelayLoadPlot2.png', allowed=0.98) # The line color/style is different because the cycle keeps going
def testRepr(self):
"""
Test script creation.
"""
self.copyfiles()
self.create()
var = 'air_temp_set_1'
self._control._toggles[var].CheckBox.setCheckState(QtCore.Qt.Checked)
self._control._toggles[var].clicked.emit()
output, imports = self._control.repr()
self.assertIn("x = data('time')", output)
self.assertIn("y = data('air_temp_set_1')", output)
if __name__ == '__main__':
unittest.main(module=__name__, verbosity=2)
| lgpl-2.1 |
bnaul/scikit-learn | sklearn/utils/tests/test_stats.py | 14 | 2429 | import numpy as np
from numpy.testing import assert_allclose
from pytest import approx
from sklearn.utils.stats import _weighted_percentile
def test_weighted_percentile():
y = np.empty(102, dtype=np.float64)
y[:50] = 0
y[-51:] = 2
y[-1] = 100000
y[50] = 1
sw = np.ones(102, dtype=np.float64)
sw[-1] = 0.0
score = _weighted_percentile(y, sw, 50)
assert approx(score) == 1
def test_weighted_percentile_equal():
y = np.empty(102, dtype=np.float64)
y.fill(0.0)
sw = np.ones(102, dtype=np.float64)
sw[-1] = 0.0
score = _weighted_percentile(y, sw, 50)
assert score == 0
def test_weighted_percentile_zero_weight():
y = np.empty(102, dtype=np.float64)
y.fill(1.0)
sw = np.ones(102, dtype=np.float64)
sw.fill(0.0)
score = _weighted_percentile(y, sw, 50)
assert approx(score) == 1.0
def test_weighted_median_equal_weights():
# Checks weighted percentile=0.5 is same as median when weights equal
rng = np.random.RandomState(0)
# Odd size as _weighted_percentile takes lower weighted percentile
x = rng.randint(10, size=11)
weights = np.ones(x.shape)
median = np.median(x)
w_median = _weighted_percentile(x, weights)
assert median == approx(w_median)
def test_weighted_median_integer_weights():
# Checks weighted percentile=0.5 is same as median when manually weight
# data
rng = np.random.RandomState(0)
x = rng.randint(20, size=10)
weights = rng.choice(5, size=10)
x_manual = np.repeat(x, weights)
median = np.median(x_manual)
w_median = _weighted_percentile(x, weights)
assert median == approx(w_median)
def test_weighted_percentile_2d():
# Check for when array 2D and sample_weight 1D
rng = np.random.RandomState(0)
x1 = rng.randint(10, size=10)
w1 = rng.choice(5, size=10)
x2 = rng.randint(20, size=10)
x_2d = np.vstack((x1, x2)).T
w_median = _weighted_percentile(x_2d, w1)
p_axis_0 = [
_weighted_percentile(x_2d[:, i], w1)
for i in range(x_2d.shape[1])
]
assert_allclose(w_median, p_axis_0)
# Check when array and sample_weight boht 2D
w2 = rng.choice(5, size=10)
w_2d = np.vstack((w1, w2)).T
w_median = _weighted_percentile(x_2d, w_2d)
p_axis_0 = [
_weighted_percentile(x_2d[:, i], w_2d[:, i])
for i in range(x_2d.shape[1])
]
assert_allclose(w_median, p_axis_0)
| bsd-3-clause |
KennyCandy/HAR | HAR_v1.py | 1 | 14532 | # Thanks to Zhao Yu for converting the .ipynb notebook to
# this simplified Python script that I edited a little.
# Note that the dataset must be already downloaded for this script to work, do:
# $ cd data/
# $ python download_dataset.py
import tensorflow as tf
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
from sklearn import metrics
import os
if __name__ == "__main__":
# -----------------------------
# step1: load and prepare data
# -----------------------------
# Those are separate normalised input features for the neural network
INPUT_SIGNAL_TYPES = [
"body_acc_x_",
"body_acc_y_",
"body_acc_z_",
"body_gyro_x_",
"body_gyro_y_",
"body_gyro_z_",
"total_acc_x_",
"total_acc_y_",
"total_acc_z_"
]
# Output classes to learn how to classify
LABELS = [
"WALKING",
"WALKING_UPSTAIRS",
"WALKING_DOWNSTAIRS",
"SITTING",
"STANDING",
"LAYING"
]
DATA_PATH = "data/"
DATASET_PATH = DATA_PATH + "UCI HAR Dataset/"
print("\n" + "Dataset is now located at: " + DATASET_PATH)
# Preparing data set:
TRAIN = "train/"
TEST = "test/"
# Load "X" (the neural network's training and testing inputs)
def load_X(X_signals_paths):
X_signals = []
for signal_type_path in X_signals_paths:
file = open(signal_type_path, 'rb')
# Read dataset from disk, dealing with text files' syntax
X_signals.append(
[np.array(serie, dtype=np.float32) for serie in [
row.replace(' ', ' ').strip().split(' ') for row in file
]]
)
file.close()
"""Examples
--------
>> > x = np.arange(4).reshape((2, 2))
>> > x
array([[0, 1],
[2, 3]])
>> > np.transpose(x)
array([[0, 2],
[1, 3]])
>> > x = np.ones((1, 2, 3))
>> > np.transpose(x, (1, 0, 2)).shape
(2, 1, 3)
"""
return np.transpose(np.array(X_signals), (1, 2, 0))
X_train_signals_paths = [
DATASET_PATH + TRAIN + "Inertial Signals/" + signal + "train.txt" for signal in INPUT_SIGNAL_TYPES
]
X_test_signals_paths = [
DATASET_PATH + TEST + "Inertial Signals/" + signal + "test.txt" for signal in INPUT_SIGNAL_TYPES
]
X_train = load_X(X_train_signals_paths) # [7352, 128, 9]
X_test = load_X(X_test_signals_paths) # [7352, 128, 9]
# print(X_train)
print(len(X_train)) # 7352
print(len(X_train[0])) # 128
print(len(X_train[0][0])) # 9
print(type(X_train))
X_train = np.reshape(X_train, [-1, 32, 36])
X_test = np.reshape(X_test, [-1, 32, 36])
print("-----------------X_train---------------")
# print(X_train)
print(len(X_train)) # 7352
print(len(X_train[0])) # 32
print(len(X_train[0][0])) # 36
print(type(X_train))
# exit()
y_train_path = DATASET_PATH + TRAIN + "y_train.txt"
y_test_path = DATASET_PATH + TEST + "y_test.txt"
def one_hot(label):
"""convert label from dense to one hot
argument:
label: ndarray dense label ,shape: [sample_num,1]
return:
one_hot_label: ndarray one hot, shape: [sample_num,n_class]
"""
label_num = len(label)
new_label = label.reshape(label_num) # shape : [sample_num]
# because max is 5, and we will create 6 columns
n_values = np.max(new_label) + 1
return np.eye(n_values)[np.array(new_label, dtype=np.int32)]
# Load "y" (the neural network's training and testing outputs)
def load_y(y_path):
file = open(y_path, 'rb')
# Read dataset from disk, dealing with text file's syntax
y_ = np.array(
[elem for elem in [
row.replace(' ', ' ').strip().split(' ') for row in file
]],
dtype=np.int32
)
file.close()
# Subtract 1 to each output class for friendly 0-based indexing
return y_ - 1
y_train = one_hot(load_y(y_train_path))
y_test = one_hot(load_y(y_test_path))
print("---------y_train----------")
# print(y_train)
print(len(y_train)) # 7352
print(len(y_train[0])) # 6
# exit()
# -----------------------------------
# step2: define parameters for model
# -----------------------------------
class Config(object):
"""
define a class to store parameters,
the input should be feature mat of training and testing
"""
def __init__(self, X_train, X_test):
# Input data
self.train_count = len(X_train) # 7352 training series
self.test_data_count = len(X_test) # 2947 testing series
self.n_steps = len(X_train[0]) # 128 time_steps per series
# Training
self.learning_rate = 0.0025
self.lambda_loss_amount = 0.0015
self.training_epochs = 300
self.batch_size = 700
# LSTM structure
self.n_inputs = len(X_train[0][0]) # Features count is of 9: three 3D sensors features over time
self.n_hidden = 32 # nb of neurons inside the neural network
self.n_classes = 6 # Final output classes
self.W = {
'hidden': tf.Variable(tf.random_normal([self.n_inputs, self.n_hidden])), # [9, 32]
'output': tf.Variable(tf.random_normal([self.n_hidden, self.n_classes])) # [32, 6]
}
self.biases = {
'hidden': tf.Variable(tf.random_normal([self.n_hidden], mean=1.0)), # [32]
'output': tf.Variable(tf.random_normal([self.n_classes])) # [6]
}
config = Config(X_train, X_test)
# print("Some useful info to get an insight on dataset's shape and normalisation:")
# print("features shape, labels shape, each features mean, each features standard deviation")
# print(X_test.shape, y_test.shape,
# np.mean(X_test), np.std(X_test))
# print("the dataset is therefore properly normalised, as expected.")
#
#
# ------------------------------------------------------
# step3: Let's get serious and build the neural network
# ------------------------------------------------------
# [none, 128, 9]
X = tf.placeholder(tf.float32, [None, config.n_steps, config.n_inputs])
# [none, 6]
Y = tf.placeholder(tf.float32, [None, config.n_classes])
print("-------X Y----------")
print(X)
X = tf.reshape(X, shape=[-1, 32, 36])
print(X)
print(Y)
Y = tf.reshape(Y, shape=[-1, 6])
print(Y)
# Weight Initialization
def weight_variable(shape):
# tra ve 1 gia tri random theo thuat toan truncated_ normal
initial = tf.truncated_normal(shape, mean=0.0, stddev=0.1, dtype=tf.float32)
return tf.Variable(initial)
def bias_varibale(shape):
initial = tf.constant(0.1, shape=shape, name='Bias')
return tf.Variable(initial)
# Convolution and Pooling
def conv2d(x, W):
# Must have `strides[0] = strides[3] = 1 `.
# For the most common case of the same horizontal and vertices strides, `strides = [1, stride, stride, 1] `.
return tf.nn.conv2d(input=x, filter=W, strides=[1, 1, 1, 1], padding='SAME', name='conv_2d')
def max_pool_2x2(x):
return tf.nn.max_pool(value=x, ksize=[1, 2, 2, 1],
strides=[1, 2, 2, 1], padding='SAME', name='max_pool')
def LSTM_Network(feature_mat, config):
"""model a LSTM Network,
it stacks 2 LSTM layers, each layer has n_hidden=32 cells
and 1 output layer, it is a full connet layer
argument:
feature_mat: ndarray feature matrix, shape=[batch_size,time_steps,n_inputs]
config: class containing config of network
return:
: matrix output shape [batch_size,n_classes]
"""
W_conv1 = weight_variable([5, 5, 1, 32])
b_conv1 = bias_varibale([32])
# x_image = tf.reshape(x, shape=[-1, 28, 28, 1])
feature_mat_image = tf.reshape(feature_mat, shape=[-1, 32, 36, 1])
h_conv1 = tf.nn.relu(conv2d(feature_mat_image, W_conv1) + b_conv1)
h_pool1 = max_pool_2x2(h_conv1)
# Second Convolutional Layer
W_conv2 = weight_variable([5, 5, 32, 1])
b_conv2 = weight_variable([1])
h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
h_pool2 = max_pool_2x2(h_conv2)
h_pool2 = tf.reshape(h_pool2, shape=[-1, 8, 9])
feature_mat = h_pool2
# print("----h_pool2-----")
# print(feature_mat)
# exit()
# W_fc1 = weight_variable([8 * 9 * 1, 1024])
# b_fc1 = bias_varibale([1024])
# h_pool2_flat = tf.reshape(h_pool2, [-1, 8 * 9 * 1])
# h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)
# print("----h_fc1_drop-----")
# print(h_fc1)
# exit()
#
# # keep_prob = tf.placeholder(tf.float32)
# keep_prob = tf.placeholder(1.0)
# h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob=keep_prob)
# print("----h_fc1_drop-----")
# print(h_fc1_drop)
# exit()
#
# W_fc2 = weight_variable([1024, 10])
# b_fc2 = bias_varibale([10])
#
# y_conv = tf.matmul(h_fc1_drop, W_fc2) + b_fc2
# print("----y_conv-----")
# print(y_conv)
# exit()
# Exchange dim 1 and dim 0
# Ban dau: [0,1,2] = [batch_size, 128, 9] => [batch_size, 32, 36]
feature_mat = tf.transpose(feature_mat, [1, 0, 2])
# New feature_mat's shape: [time_steps, batch_size, n_inputs] [128, batch_size, 9]
# Temporarily crush the feature_mat's dimensions
feature_mat = tf.reshape(feature_mat, [-1, config.n_inputs]) # 9
# New feature_mat's shape: [time_steps*batch_size, n_inputs] # 128 * batch_size
# Linear activation, reshaping inputs to the LSTM's number of hidden:
hidden = tf.nn.relu(tf.matmul(
feature_mat, config.W['hidden']
) + config.biases['hidden'])
# New feature_mat (hidden) shape: [time_steps*batch_size, n_hidden] [128*batch_size, 32]
print("--n_steps--")
print(config.n_steps)
print("--n_steps--")
print(hidden)
exit()
# Split the series because the rnn cell needs time_steps features, each of shape:
hidden = tf.split(0, config.n_steps, hidden)
# New hidden's shape: a list of length "time_step" containing tensors of shape [batch_size, n_hidden]
# Define LSTM cell of first hidden layer:
lstm_cell = tf.nn.rnn_cell.BasicLSTMCell(config.n_hidden, forget_bias=1.0)
# Stack two LSTM layers, both layers has the same shape
lsmt_layers = tf.nn.rnn_cell.MultiRNNCell([lstm_cell] * 2)
# Get LSTM outputs, the states are internal to the LSTM cells,they are not our attention here
outputs, _ = tf.nn.rnn(lsmt_layers, hidden, dtype=tf.float32)
# outputs' shape: a list of lenght "time_step" containing tensors of shape [batch_size, n_hidden]
print("------------------list-------------------")
print(outputs)
# Get last time step's output feature for a "many to one" style classifier,
# as in the image describing RNNs at the top of this page
lstm_last_output = outputs[-1] # Chi lay phan tu cuoi cung voi shape: [?, 32]
print("------------------last outputs-------------------")
print (lstm_last_output)
# Linear activation
return tf.matmul(lstm_last_output, config.W['output']) + config.biases['output']
pred_Y = LSTM_Network(X, config) # shape[?,6]
print("------------------pred_Y-------------------")
print(pred_Y)
# exit()
# Loss,train_step,evaluation
l2 = config.lambda_loss_amount * \
sum(tf.nn.l2_loss(tf_var) for tf_var in tf.trainable_variables())
# Softmax loss and L2
cost = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits(pred_Y, Y)) + l2
train_step = tf.train.AdamOptimizer(
learning_rate=config.learning_rate).minimize(cost)
correct_prediction = tf.equal(tf.argmax(pred_Y, 1), tf.argmax(Y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, dtype=tf.float32))
# --------------------------------------------
# step4: Hooray, now train the neural network
# --------------------------------------------
# Note that log_device_placement can be turned ON but will cause console spam.
sess = tf.InteractiveSession(config=tf.ConfigProto(log_device_placement=False))
tf.initialize_all_variables().run()
best_accuracy = 0.0
# Start training for each batch and loop epochs
for i in range(config.training_epochs):
for start, end in zip(range(0, config.train_count, config.batch_size), # (0, 7352, 1500)
range(config.batch_size, config.train_count + 1,
config.batch_size)): # (1500, 7353, 1500)
print(start)
print(end)
sess.run(train_step, feed_dict={X: X_train[start:end],
Y: y_train[start:end]})
# Test completely at every epoch: calculate accuracy
pred_out, accuracy_out, loss_out = sess.run([pred_Y, accuracy, cost], feed_dict={
X: X_test, Y: y_test})
print("traing iter: {},".format(i) + \
" test accuracy : {},".format(accuracy_out) + \
" loss : {}".format(loss_out))
best_accuracy = max(best_accuracy, accuracy_out)
print("")
print("final test accuracy: {}".format(accuracy_out))
print("best epoch's test accuracy: {}".format(best_accuracy))
print("")
#
# #------------------------------------------------------------------
# # step5: Training is good, but having visual insight is even better
# #------------------------------------------------------------------
# # The code is in the .ipynb
#
# #------------------------------------------------------------------
# # step6: And finally, the multi-class confusion matrix and metrics!
# #------------------------------------------------------------------
# # The code is in the .ipynb
| mit |
kvoyager/GmdhPy | examples/iris_recognition.py | 1 | 3189 | # -*- coding: utf-8 -*-
from __future__ import print_function
import pylab as plt
import numpy as np
# Import datasets, classifiers and performance metrics
from sklearn import datasets, metrics
from sklearn.metrics import confusion_matrix
from gmdhpy.gmdh import Regressor
from gmdhpy.plot_model import PlotModel
def iris_class(value):
if value > 1.5:
return 2
elif value <= 1.5 and value >= 0.5:
return 1
else:
return 0
def plot_confusion_matrix(cm, title='Confusion matrix', cmap=plt.cm.Blues):
plt.imshow(cm, interpolation='nearest', cmap=cmap)
plt.title(title)
plt.colorbar()
tick_marks = np.arange(len(iris.target_names))
plt.xticks(tick_marks, iris.target_names, rotation=45)
plt.yticks(tick_marks, iris.target_names)
plt.tight_layout()
plt.ylabel('True label')
plt.xlabel('Predicted label')
if __name__ == '__main__':
iris = datasets.load_iris()
viris_class = np.vectorize(iris_class, otypes=[np.int])
n_samples = iris.data.shape[0]
data = np.empty_like(iris.data)
target = np.empty_like(iris.target)
j = 0
n = n_samples // 3
for i in range(0, n):
data[j] = iris.data[i]
data[j+1] = iris.data[i+n]
data[j+2] = iris.data[i+2*n]
target[j] = iris.target[i]
target[j+1] = iris.target[i+n]
target[j+2] = iris.target[i+2*n]
j += 3
train_data_is_the_first_half = False
n = n_samples // 2
if train_data_is_the_first_half:
train_x = data[:n]
train_y = target[:n]
test_x = data[n:]
test_y = target[n:]
else:
train_x = data[n:]
train_y = target[n:]
test_x = data[:n]
test_y = target[:n]
model = Regressor(ref_functions='linear_cov',
feature_names=iris.feature_names,
criterion_minimum_width=5,
stop_train_epsilon_condition=0.0001,
l2=0.5,
n_jobs=4)
model.fit(train_x, train_y)
# Now predict the value of the second half:
# predict with GMDH model
pred_y_row = model.predict(test_x)
pred_y = viris_class(pred_y_row)
print(model.get_selected_features_indices())
print(model.get_unselected_features_indices())
print("Selected features: {}".format(model.get_selected_features()))
print("Unselected features: {}".format(model.get_unselected_features()))
fig = plt.figure()
# Compute confusion matrix
cm = confusion_matrix(test_y, pred_y)
np.set_printoptions(precision=2)
print('Confusion matrix, without normalization')
print(cm)
ax1 = fig.add_subplot(121)
plot_confusion_matrix(cm)
# Normalize the confusion matrix by row (i.e by the number of samples
# in each class)
cm_normalized = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
print('Normalized confusion matrix')
print(cm_normalized)
ax2 = fig.add_subplot(122)
plot_confusion_matrix(cm_normalized, title='Normalized confusion matrix')
model.plot_layer_error()
plt.show()
PlotModel(model, filename='iris_model', plot_neuron_name=True, view=True).plot()
| mit |
odlgroup/odl | odl/util/graphics.py | 2 | 16516 | # Copyright 2014-2017 The ODL contributors
#
# This file is part of ODL.
#
# This Source Code Form is subject to the terms of the Mozilla Public License,
# v. 2.0. If a copy of the MPL was not distributed with this file, You can
# obtain one at https://mozilla.org/MPL/2.0/.
"""Functions for graphical output."""
from __future__ import print_function, division, absolute_import
import numpy as np
import warnings
from odl.util.testutils import run_doctests
from odl.util.utility import is_real_dtype
__all__ = ('show_discrete_data',)
def warning_free_pause():
"""Issue a matplotlib pause without the warning."""
import matplotlib.pyplot as plt
with warnings.catch_warnings():
warnings.filterwarnings("ignore",
message="Using default event loop until "
"function specific to this GUI is "
"implemented")
plt.pause(0.0001)
def _safe_minmax(values):
"""Calculate min and max of array with guards for nan and inf."""
# Nan and inf guarded min and max
isfinite = np.isfinite(values)
if np.any(isfinite):
# Only use finite values
values = values[isfinite]
minval = np.min(values)
maxval = np.max(values)
return minval, maxval
def _colorbar_ticks(minval, maxval):
"""Return the ticks (values show) in the colorbar."""
if not (np.isfinite(minval) and np.isfinite(maxval)):
return [0, 0, 0]
elif minval == maxval:
return [minval]
else:
# Add eps to ensure values stay inside the range of the colorbar.
# Otherwise they may occationally not display.
eps = (maxval - minval) / 1e5
return [minval + eps, (maxval + minval) / 2., maxval - eps]
def _digits(minval, maxval):
"""Digits needed to comforatbly display values in [minval, maxval]"""
if minval == maxval:
return 3
else:
return min(10, max(2, int(1 + abs(np.log10(maxval - minval)))))
def _colorbar_format(minval, maxval):
"""Return the format string for the colorbar."""
if not (np.isfinite(minval) and np.isfinite(maxval)):
return str(maxval)
else:
return '%.{}f'.format(_digits(minval, maxval))
def _axes_info(grid, npoints=5):
result = []
min_pt = grid.min()
max_pt = grid.max()
for axis in range(grid.ndim):
xmin = min_pt[axis]
xmax = max_pt[axis]
points = np.linspace(xmin, xmax, npoints)
indices = np.linspace(0, grid.shape[axis] - 1, npoints, dtype=int)
tick_values = grid.coord_vectors[axis][indices]
# Do not use corner point in case of a partition, use outer corner
tick_values[[0, -1]] = xmin, xmax
format_str = '{:.' + str(_digits(xmin, xmax)) + 'f}'
tick_labels = [format_str.format(f) for f in tick_values]
result += [(points, tick_labels)]
return result
def show_discrete_data(values, grid, title=None, method='',
force_show=False, fig=None, **kwargs):
"""Display a discrete 1d or 2d function.
Parameters
----------
values : `numpy.ndarray`
The values to visualize.
grid : `RectGrid` or `RectPartition`
Grid of the values.
title : string, optional
Set the title of the figure.
method : string, optional
1d methods:
'plot' : graph plot
'scatter' : scattered 2d points
(2nd axis <-> value)
2d methods:
'imshow' : image plot with coloring according to value,
including a colorbar.
'scatter' : cloud of scattered 3d points
(3rd axis <-> value)
'wireframe', 'plot_wireframe' : surface plot
force_show : bool, optional
Whether the plot should be forced to be shown now or deferred until
later. Note that some backends always displays the plot, regardless
of this value.
fig : `matplotlib.figure.Figure`, optional
The figure to show in. Expected to be of same "style", as the figure
given by this function. The most common usecase is that fig is the
return value from an earlier call to this function.
Default: New figure
interp : {'nearest', 'linear'}, optional
Interpolation method to use.
Default: 'nearest'
axis_labels : string, optional
Axis labels, default: ['x', 'y']
update_in_place : bool, optional
Update the content of the figure in-place. Intended for faster real
time plotting, typically ~5 times faster.
This is only performed for ``method == 'imshow'`` with real data and
``fig != None``. Otherwise this parameter is treated as False.
Default: False
axis_fontsize : int, optional
Fontsize for the axes. Default: 16
colorbar : bool, optional
Argument relevant for 2d plots using ``method='imshow'``. If ``True``,
include a colorbar in the plot.
Default: True
kwargs : {'figsize', 'saveto', ...}, optional
Extra keyword arguments passed on to display method
See the Matplotlib functions for documentation of extra
options.
Returns
-------
fig : `matplotlib.figure.Figure`
The resulting figure. It is also shown to the user.
See Also
--------
matplotlib.pyplot.plot : Show graph plot
matplotlib.pyplot.imshow : Show data as image
matplotlib.pyplot.scatter : Show scattered 3d points
"""
# Importing pyplot takes ~2 sec, only import when needed.
import matplotlib.pyplot as plt
args_re = []
args_im = []
dsp_kwargs = {}
sub_kwargs = {}
arrange_subplots = (121, 122) # horzontal arrangement
# Create axis labels which remember their original meaning
axis_labels = kwargs.pop('axis_labels', ['x', 'y'])
values_are_complex = not is_real_dtype(values.dtype)
figsize = kwargs.pop('figsize', None)
saveto = kwargs.pop('saveto', None)
interp = kwargs.pop('interp', 'nearest')
axis_fontsize = kwargs.pop('axis_fontsize', 16)
colorbar = kwargs.pop('colorbar', True)
# Normalize input
interp, interp_in = str(interp).lower(), interp
method, method_in = str(method).lower(), method
# Check if we should and can update the plot in-place
update_in_place = kwargs.pop('update_in_place', False)
if (update_in_place and
(fig is None or values_are_complex or values.ndim != 2 or
(values.ndim == 2 and method not in ('', 'imshow')))):
update_in_place = False
if values.ndim == 1: # TODO: maybe a plotter class would be better
if not method:
if interp == 'nearest':
method = 'step'
dsp_kwargs['where'] = 'mid'
elif interp == 'linear':
method = 'plot'
else:
raise ValueError('`interp` {!r} not supported'
''.format(interp_in))
if method == 'plot' or method == 'step' or method == 'scatter':
args_re += [grid.coord_vectors[0], values.real]
args_im += [grid.coord_vectors[0], values.imag]
else:
raise ValueError('`method` {!r} not supported'
''.format(method_in))
elif values.ndim == 2:
if not method:
method = 'imshow'
if method == 'imshow':
args_re = [np.rot90(values.real)]
args_im = [np.rot90(values.imag)] if values_are_complex else []
extent = [grid.min()[0], grid.max()[0],
grid.min()[1], grid.max()[1]]
if interp == 'nearest':
interpolation = 'nearest'
elif interp == 'linear':
interpolation = 'bilinear'
else:
raise ValueError('`interp` {!r} not supported'
''.format(interp_in))
dsp_kwargs.update({'interpolation': interpolation,
'cmap': 'bone',
'extent': extent,
'aspect': 'auto'})
elif method == 'scatter':
pts = grid.points()
args_re = [pts[:, 0], pts[:, 1], values.ravel().real]
args_im = ([pts[:, 0], pts[:, 1], values.ravel().imag]
if values_are_complex else [])
sub_kwargs.update({'projection': '3d'})
elif method in ('wireframe', 'plot_wireframe'):
method = 'plot_wireframe'
x, y = grid.meshgrid
args_re = [x, y, np.rot90(values.real)]
args_im = ([x, y, np.rot90(values.imag)] if values_are_complex
else [])
sub_kwargs.update({'projection': '3d'})
else:
raise ValueError('`method` {!r} not supported'
''.format(method_in))
else:
raise NotImplementedError('no method for {}d display implemented'
''.format(values.ndim))
# Additional keyword args are passed on to the display method
dsp_kwargs.update(**kwargs)
if fig is not None:
# Reuse figure if given as input
if not isinstance(fig, plt.Figure):
raise TypeError('`fig` {} not a matplotlib figure'.format(fig))
if not plt.fignum_exists(fig.number):
# If figure does not exist, user either closed the figure or
# is using IPython, in this case we need a new figure.
fig = plt.figure(figsize=figsize)
updatefig = False
else:
# Set current figure to given input
fig = plt.figure(fig.number)
updatefig = True
if values.ndim > 1 and not update_in_place:
# If the figure is larger than 1d, we can clear it since we
# dont reuse anything. Keeping it causes performance problems.
fig.clf()
else:
fig = plt.figure(figsize=figsize)
updatefig = False
if values_are_complex:
# Real
if len(fig.axes) == 0:
# Create new axis if needed
sub_re = plt.subplot(arrange_subplots[0], **sub_kwargs)
sub_re.set_title('Real part')
sub_re.set_xlabel(axis_labels[0], fontsize=axis_fontsize)
if values.ndim == 2:
sub_re.set_ylabel(axis_labels[1], fontsize=axis_fontsize)
else:
sub_re.set_ylabel('value')
else:
sub_re = fig.axes[0]
display_re = getattr(sub_re, method)
csub_re = display_re(*args_re, **dsp_kwargs)
# Axis ticks
if method == 'imshow' and not grid.is_uniform:
(xpts, xlabels), (ypts, ylabels) = _axes_info(grid)
plt.xticks(xpts, xlabels)
plt.yticks(ypts, ylabels)
if method == 'imshow' and len(fig.axes) < 2:
# Create colorbar if none seems to exist
# Use clim from kwargs if given
if 'clim' not in kwargs:
minval_re, maxval_re = _safe_minmax(values.real)
else:
minval_re, maxval_re = kwargs['clim']
ticks_re = _colorbar_ticks(minval_re, maxval_re)
fmt_re = _colorbar_format(minval_re, maxval_re)
plt.colorbar(csub_re, orientation='horizontal',
ticks=ticks_re, format=fmt_re)
# Imaginary
if len(fig.axes) < 3:
sub_im = plt.subplot(arrange_subplots[1], **sub_kwargs)
sub_im.set_title('Imaginary part')
sub_im.set_xlabel(axis_labels[0], fontsize=axis_fontsize)
if values.ndim == 2:
sub_im.set_ylabel(axis_labels[1], fontsize=axis_fontsize)
else:
sub_im.set_ylabel('value')
else:
sub_im = fig.axes[2]
display_im = getattr(sub_im, method)
csub_im = display_im(*args_im, **dsp_kwargs)
# Axis ticks
if method == 'imshow' and not grid.is_uniform:
(xpts, xlabels), (ypts, ylabels) = _axes_info(grid)
plt.xticks(xpts, xlabels)
plt.yticks(ypts, ylabels)
if method == 'imshow' and len(fig.axes) < 4:
# Create colorbar if none seems to exist
# Use clim from kwargs if given
if 'clim' not in kwargs:
minval_im, maxval_im = _safe_minmax(values.imag)
else:
minval_im, maxval_im = kwargs['clim']
ticks_im = _colorbar_ticks(minval_im, maxval_im)
fmt_im = _colorbar_format(minval_im, maxval_im)
plt.colorbar(csub_im, orientation='horizontal',
ticks=ticks_im, format=fmt_im)
else:
if len(fig.axes) == 0:
# Create new axis object if needed
sub = plt.subplot(111, **sub_kwargs)
sub.set_xlabel(axis_labels[0], fontsize=axis_fontsize)
if values.ndim == 2:
sub.set_ylabel(axis_labels[1], fontsize=axis_fontsize)
else:
sub.set_ylabel('value')
try:
# For 3d plots
sub.set_zlabel('z')
except AttributeError:
pass
else:
sub = fig.axes[0]
if update_in_place:
import matplotlib as mpl
imgs = [obj for obj in sub.get_children()
if isinstance(obj, mpl.image.AxesImage)]
if len(imgs) > 0 and updatefig:
imgs[0].set_data(args_re[0])
csub = imgs[0]
# Update min-max
if 'clim' not in kwargs:
minval, maxval = _safe_minmax(values)
else:
minval, maxval = kwargs['clim']
csub.set_clim(minval, maxval)
else:
display = getattr(sub, method)
csub = display(*args_re, **dsp_kwargs)
else:
display = getattr(sub, method)
csub = display(*args_re, **dsp_kwargs)
# Axis ticks
if method == 'imshow' and not grid.is_uniform:
(xpts, xlabels), (ypts, ylabels) = _axes_info(grid)
plt.xticks(xpts, xlabels)
plt.yticks(ypts, ylabels)
if method == 'imshow' and colorbar:
# Add colorbar
# Use clim from kwargs if given
if 'clim' not in kwargs:
minval, maxval = _safe_minmax(values)
else:
minval, maxval = kwargs['clim']
ticks = _colorbar_ticks(minval, maxval)
fmt = _colorbar_format(minval, maxval)
if len(fig.axes) < 2:
# Create colorbar if none seems to exist
plt.colorbar(mappable=csub, ticks=ticks, format=fmt)
elif update_in_place:
# If it exists and we should update it
csub.colorbar.set_clim(minval, maxval)
csub.colorbar.set_ticks(ticks)
if '%' not in fmt:
labels = [fmt] * len(ticks)
else:
labels = [fmt % t for t in ticks]
csub.colorbar.set_ticklabels(labels)
csub.colorbar.draw_all()
# Set title of window
if title is not None:
if not values_are_complex:
# Do not overwrite title for complex values
plt.title(title)
fig.canvas.manager.set_window_title(title)
# Fixes overlapping stuff at the expense of potentially squashed subplots
if not update_in_place:
fig.tight_layout()
if updatefig or plt.isinteractive():
# If we are running in interactive mode, we can always show the fig
# This causes an artifact, where users of `CallbackShow` without
# interactive mode only shows the figure after the second iteration.
plt.show(block=False)
if not update_in_place:
plt.draw()
warning_free_pause()
else:
try:
sub.draw_artist(csub)
fig.canvas.blit(fig.bbox)
fig.canvas.update()
fig.canvas.flush_events()
except AttributeError:
plt.draw()
warning_free_pause()
if force_show:
plt.show()
if saveto is not None:
fig.savefig(saveto)
return fig
if __name__ == '__main__':
run_doctests()
| mpl-2.0 |
adamrvfisher/TechnicalAnalysisLibrary | DefNormChaikinStratOpt.py | 1 | 4250 | # -*- coding: utf-8 -*-
"""
Created on Tue Apr 4 11:01:58 2017
@author: AmatVictoriaCuramIII
"""
def DefNormChaikinStratOpt(ticker,start,end):
import numpy as np
from pandas_datareader import data
import random as rand
import pandas as pd
empty = [] #reusable list
#set up desired number of datasets for different period analysis
dataset = pd.DataFrame()
iterations = range(0,1000)
ticker = '^GSPC'
s = data.DataReader(ticker, 'yahoo', start=start, end=end)
s['LogRet'] = np.log(s['Adj Close']/s['Adj Close'].shift(1))
s['LogRet'] = s['LogRet'].fillna(0)
s['CLV'] = (((s['Adj Close'] - s['Low']) - (s['High'] - s['Adj Close']))
/ (s['High'] - s['Low']))
s['ADI'] = (s['Volume'] * s['CLV']).cumsum()
for x in iterations:
aa = rand.randint(1,30)
bb = rand.randint(2,60)
if aa > bb:
continue
c = rand.randint(2,60)
d = 5.5 - rand.random() * 7
e = 5.5 - rand.random() * 7
f = 5.5 - rand.random() * 7
g = 5.5 - rand.random() * 7
a = aa #number of days for moving average window
b = bb #numer of days for moving average window
values = s['ADI']
weights = np.repeat(1.0, a)/a
weights2 = np.repeat(1.0, b)/b
smas = np.convolve(values, weights, 'valid')
smas2 = np.convolve(values, weights2, 'valid')
trim = len(s) - len(smas2)
trim2 = len(smas) - len(smas2)
replace = s[:trim]
s = s[trim:]
smas = smas[trim2:]
s['ADIEMAsmall'] = smas
s['ADIEMAlarge'] = smas2
s = replace.append(s)
volumewindow = c
s.loc[:,'AverageRollingVolume'] = s['Volume'].rolling(center=False,
window=volumewindow).mean()
s.loc[:,'Chaikin'] = s['ADIEMAsmall'] - s['ADIEMAlarge']
s.loc[:,'NormChaikin'] = s['Chaikin']/s['AverageRollingVolume']
kk = s[:volumewindow-1]
s = s[volumewindow-1:]
s.loc[:,'Touch'] = np.where(s['NormChaikin'] < d, 1,0) #long signal
s.loc[:,'Touch'] = np.where(s['NormChaikin'] > e, -1, s['Touch']) #short signal
s.loc[:,'Sustain'] = np.where(s['Touch'].shift(1) == 1, 1, 0) # never actually true when optimized
s.loc[:,'Sustain'] = np.where(s['Sustain'].shift(1) == 1, 1,
s['Sustain'])
s.loc[:,'Sustain'] = np.where(s['Touch'].shift(1) == -1, -1, 0) #true when previous day touch is -1, and current RSI is > line 37 threshold
s.loc[:,'Sustain'] = np.where(s['Sustain'].shift(1) == -1, -1,
s['Sustain'])
s.loc[:,'Sustain'] = np.where(s['NormChaikin'] > f, 0, s['Sustain']) #if RSI is greater than threshold, sustain is forced to 0
s.loc[:,'Sustain'] = np.where(s['NormChaikin'] < g, 0, s['Sustain']) #never actually true when optimized
s.loc[:,'Regime'] = s['Touch'] + s['Sustain']
s.loc[:,'Strategy'] = (s['Regime']).shift(1)*s['LogRet']
s.loc[:,'Strategy'] = s['Strategy'].fillna(0)
s = kk.append(s)
if s['Strategy'].std() == 0:
continue
sharpe = (s['Strategy'].mean()-s['LogRet'].mean())/s['Strategy'].std()
if np.isnan(sharpe) == True:
continue
if sharpe < 0.001:
continue
empty.append(a)
empty.append(b)
empty.append(c)
empty.append(d)
empty.append(e)
empty.append(f)
empty.append(g)
empty.append(sharpe)
emptyseries = pd.Series(empty)
dataset[x] = emptyseries.values
empty[:] = []
z1 = dataset.iloc[7]
w1 = np.percentile(z1, 70)
v1 = [] #this variable stores the Nth percentile of top performers
DS1W = pd.DataFrame() #this variable stores your financial advisors for specific dataset
for h in z1:
if h > w1:
v1.append(h)
for j in v1:
r = dataset.columns[(dataset == j).iloc[7]]
DS1W = pd.concat([DS1W,dataset[r]], axis = 1)
return DS1W | apache-2.0 |
pradyu1993/scikit-learn | sklearn/ensemble/tests/test_gradient_boosting.py | 3 | 14807 | """
Testing for the gradient boosting module (sklearn.ensemble.gradient_boosting).
"""
import numpy as np
from numpy.testing import assert_array_equal
from numpy.testing import assert_array_almost_equal
from numpy.testing import assert_equal
from nose.tools import assert_raises
from sklearn.metrics import mean_squared_error
from sklearn.utils import check_random_state
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.ensemble import GradientBoostingRegressor
from sklearn import datasets
# toy sample
X = [[-2, -1], [-1, -1], [-1, -2], [1, 1], [1, 2], [2, 1]]
y = [-1, -1, -1, 1, 1, 1]
T = [[-1, -1], [2, 2], [3, 2]]
true_result = [-1, 1, 1]
rng = np.random.RandomState(0)
# also load the boston dataset
# and randomly permute it
boston = datasets.load_boston()
perm = rng.permutation(boston.target.size)
boston.data = boston.data[perm]
boston.target = boston.target[perm]
# also load the iris dataset
# and randomly permute it
iris = datasets.load_iris()
perm = rng.permutation(iris.target.size)
iris.data = iris.data[perm]
iris.target = iris.target[perm]
def test_classification_toy():
"""Check classification on a toy dataset."""
clf = GradientBoostingClassifier(n_estimators=100, random_state=1)
assert_raises(ValueError, clf.predict, T)
clf.fit(X, y)
assert_array_equal(clf.predict(T), true_result)
assert_equal(100, len(clf.estimators_))
deviance_decrease = (clf.train_score_[:-1] - clf.train_score_[1:])
assert np.any(deviance_decrease >= 0.0), \
"Train deviance does not monotonically decrease."
def test_parameter_checks():
"""Check input parameter validation."""
assert_raises(ValueError, GradientBoostingClassifier, n_estimators=0)
assert_raises(ValueError, GradientBoostingClassifier, n_estimators=-1)
assert_raises(ValueError, GradientBoostingClassifier, learn_rate=0.0)
assert_raises(ValueError, GradientBoostingClassifier, learn_rate=-1.0)
assert_raises(ValueError, GradientBoostingRegressor, loss='foobar')
assert_raises(ValueError, GradientBoostingClassifier,
min_samples_split=0.0)
assert_raises(ValueError, GradientBoostingClassifier,
min_samples_split=-1.0)
assert_raises(ValueError, GradientBoostingClassifier, min_samples_leaf=0)
assert_raises(ValueError, GradientBoostingClassifier, min_samples_leaf=-1.)
assert_raises(ValueError, GradientBoostingClassifier, subsample=0.0)
assert_raises(ValueError, GradientBoostingClassifier, subsample=1.1)
assert_raises(ValueError, GradientBoostingClassifier, subsample=-0.1)
assert_raises(ValueError, GradientBoostingClassifier, max_depth=-0.1)
assert_raises(ValueError, GradientBoostingClassifier, max_depth=0)
assert_raises(ValueError, GradientBoostingClassifier, init={})
# test fit before feature importance
assert_raises(ValueError,
lambda: GradientBoostingClassifier().feature_importances_)
# binomial deviance requires ``n_classes == 2``.
assert_raises(ValueError,
lambda X, y: GradientBoostingClassifier(
loss='bdeviance').fit(X, y),
X, [0, 0, 1, 1, 2, 2])
# multinomial deviance requires ``n_classes > 2``.
assert_raises(ValueError,
lambda X, y: GradientBoostingClassifier(
loss='mdeviance').fit(X, y),
X, [0, 0, 1, 1, 1, 0])
# deviance requires ``n_classes >= 2``.
assert_raises(ValueError,
lambda X, y: GradientBoostingClassifier(
loss='deviance').fit(X, y),
X, [0, 0, 0, 0])
def test_classification_synthetic():
"""Test GradientBoostingClassifier on synthetic dataset used by
Hastie et al. in ESLII Example 12.7. """
X, y = datasets.make_hastie_10_2(n_samples=12000, random_state=1)
X_train, X_test = X[:2000], X[2000:]
y_train, y_test = y[:2000], y[2000:]
gbrt = GradientBoostingClassifier(n_estimators=100, min_samples_split=1,
max_depth=1,
learn_rate=1.0, random_state=0)
gbrt.fit(X_train, y_train)
error_rate = (1.0 - gbrt.score(X_test, y_test))
assert error_rate < 0.085, \
"GB failed with error %.4f" % error_rate
gbrt = GradientBoostingClassifier(n_estimators=200, min_samples_split=1,
max_depth=1,
learn_rate=1.0, subsample=0.5,
random_state=0)
gbrt.fit(X_train, y_train)
error_rate = (1.0 - gbrt.score(X_test, y_test))
assert error_rate < 0.08, \
"Stochastic GB failed with error %.4f" % error_rate
def test_boston():
"""Check consistency on dataset boston house prices with least squares
and least absolute deviation. """
for loss in ("ls", "lad", "huber"):
clf = GradientBoostingRegressor(n_estimators=100, loss=loss,
max_depth=4,
min_samples_split=1, random_state=1)
assert_raises(ValueError, clf.predict, boston.data)
clf.fit(boston.data, boston.target)
y_pred = clf.predict(boston.data)
mse = mean_squared_error(boston.target, y_pred)
assert mse < 6.0, "Failed with loss %s and mse = %.4f" % (loss, mse)
def test_iris():
"""Check consistency on dataset iris."""
for subsample in (1.0, 0.5):
clf = GradientBoostingClassifier(n_estimators=100, loss='deviance',
random_state=1, subsample=subsample)
clf.fit(iris.data, iris.target)
score = clf.score(iris.data, iris.target)
assert score > 0.9, "Failed with subsample %.1f " \
"and score = %f" % (subsample, score)
def test_regression_synthetic():
"""Test on synthetic regression datasets used in Leo Breiman,
`Bagging Predictors?. Machine Learning 24(2): 123-140 (1996). """
random_state = check_random_state(1)
regression_params = {'n_estimators': 100, 'max_depth': 4,
'min_samples_split': 1, 'learn_rate': 0.1,
'loss': 'ls'}
# Friedman1
X, y = datasets.make_friedman1(n_samples=1200,
random_state=random_state, noise=1.0)
X_train, y_train = X[:200], y[:200]
X_test, y_test = X[200:], y[200:]
clf = GradientBoostingRegressor()
clf.fit(X_train, y_train)
mse = mean_squared_error(y_test, clf.predict(X_test))
assert mse < 5.0, "Failed on Friedman1 with mse = %.4f" % mse
# Friedman2
X, y = datasets.make_friedman2(n_samples=1200, random_state=random_state)
X_train, y_train = X[:200], y[:200]
X_test, y_test = X[200:], y[200:]
clf = GradientBoostingRegressor(**regression_params)
clf.fit(X_train, y_train)
mse = mean_squared_error(y_test, clf.predict(X_test))
assert mse < 1700.0, "Failed on Friedman2 with mse = %.4f" % mse
# Friedman3
X, y = datasets.make_friedman3(n_samples=1200, random_state=random_state)
X_train, y_train = X[:200], y[:200]
X_test, y_test = X[200:], y[200:]
clf = GradientBoostingRegressor(**regression_params)
clf.fit(X_train, y_train)
mse = mean_squared_error(y_test, clf.predict(X_test))
assert mse < 0.015, "Failed on Friedman3 with mse = %.4f" % mse
# def test_feature_importances():
# X = np.array(boston.data, dtype=np.float32)
# y = np.array(boston.target, dtype=np.float32)
# clf = GradientBoostingRegressor(n_estimators=100, max_depth=5,
# min_samples_split=1, random_state=1)
# clf.fit(X, y)
# feature_importances = clf.feature_importances_
# # true feature importance ranking
# true_ranking = np.array([3, 1, 8, 2, 10, 9, 4, 11, 0, 6, 7, 5, 12])
# assert_array_equal(true_ranking, feature_importances.argsort())
def test_probability():
"""Predict probabilities."""
clf = GradientBoostingClassifier(n_estimators=100, random_state=1)
assert_raises(ValueError, clf.predict_proba, T)
clf.fit(X, y)
assert_array_equal(clf.predict(T), true_result)
# check if probabilities are in [0, 1].
y_proba = clf.predict_proba(T)
assert np.all(y_proba >= 0.0)
assert np.all(y_proba <= 1.0)
# derive predictions from probabilities
y_pred = clf.classes_.take(y_proba.argmax(axis=1), axis=0)
assert_array_equal(y_pred, true_result)
def test_check_inputs():
"""Test input checks (shape and type of X and y)."""
clf = GradientBoostingClassifier(n_estimators=100, random_state=1)
assert_raises(ValueError, clf.fit, X, y + [0, 1])
from scipy import sparse
X_sparse = sparse.csr_matrix(X)
clf = GradientBoostingClassifier(n_estimators=100, random_state=1)
assert_raises(TypeError, clf.fit, X_sparse, y)
clf = GradientBoostingClassifier().fit(X, y)
assert_raises(TypeError, clf.predict, X_sparse)
def test_check_inputs_predict():
"""X has wrong shape """
clf = GradientBoostingClassifier(n_estimators=100, random_state=1)
clf.fit(X, y)
x = np.array([1.0, 2.0])[:, np.newaxis]
assert_raises(ValueError, clf.predict, x)
x = np.array([])
assert_raises(ValueError, clf.predict, x)
x = np.array([1.0, 2.0, 3.0])[:, np.newaxis]
assert_raises(ValueError, clf.predict, x)
clf = GradientBoostingRegressor(n_estimators=100, random_state=1)
clf.fit(X, rng.rand(len(X)))
x = np.array([1.0, 2.0])[:, np.newaxis]
assert_raises(ValueError, clf.predict, x)
x = np.array([])
assert_raises(ValueError, clf.predict, x)
x = np.array([1.0, 2.0, 3.0])[:, np.newaxis]
assert_raises(ValueError, clf.predict, x)
def test_check_max_features():
"""test if max_features is valid. """
clf = GradientBoostingRegressor(n_estimators=100, random_state=1,
max_features=0)
assert_raises(ValueError, clf.fit, X, y)
clf = GradientBoostingRegressor(n_estimators=100, random_state=1,
max_features=(len(X[0]) + 1))
assert_raises(ValueError, clf.fit, X, y)
def test_staged_predict():
"""Test whether staged decision function eventually gives
the same prediction.
"""
X, y = datasets.make_friedman1(n_samples=1200,
random_state=1, noise=1.0)
X_train, y_train = X[:200], y[:200]
X_test, y_test = X[200:], y[200:]
clf = GradientBoostingRegressor()
# test raise ValueError if not fitted
assert_raises(ValueError, lambda X: np.fromiter(
clf.staged_predict(X), dtype=np.float64), X_test)
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
# test if prediction for last stage equals ``predict``
for y in clf.staged_predict(X_test):
assert_equal(y.shape, y_pred.shape)
assert_array_equal(y_pred, y)
def test_serialization():
"""Check model serialization."""
clf = GradientBoostingClassifier(n_estimators=100, random_state=1)
clf.fit(X, y)
assert_array_equal(clf.predict(T), true_result)
assert_equal(100, len(clf.estimators_))
try:
import cPickle as pickle
except ImportError:
import pickle
serialized_clf = pickle.dumps(clf, protocol=pickle.HIGHEST_PROTOCOL)
clf = None
clf = pickle.loads(serialized_clf)
assert_array_equal(clf.predict(T), true_result)
assert_equal(100, len(clf.estimators_))
def test_degenerate_targets():
"""Check if we can fit even though all targets are equal. """
clf = GradientBoostingClassifier(n_estimators=100, random_state=1)
# classifier should raise exception
assert_raises(ValueError, clf.fit, X, np.ones(len(X)))
clf = GradientBoostingRegressor(n_estimators=100, random_state=1)
clf.fit(X, np.ones(len(X)))
clf.predict(rng.rand(2))
assert_array_equal(np.ones((1,), dtype=np.float64),
clf.predict(rng.rand(2)))
def test_quantile_loss():
"""Check if quantile loss with alpha=0.5 equals lad. """
clf_quantile = GradientBoostingRegressor(n_estimators=100, loss='quantile',
max_depth=4, alpha=0.5,
random_state=7)
clf_quantile.fit(boston.data, boston.target)
y_quantile = clf_quantile.predict(boston.data)
clf_lad = GradientBoostingRegressor(n_estimators=100, loss='lad',
max_depth=4, random_state=7)
clf_lad.fit(boston.data, boston.target)
y_lad = clf_lad.predict(boston.data)
assert_array_almost_equal(y_quantile, y_lad, decimal=4)
def test_symbol_labels():
"""Test with non-integer class labels. """
clf = GradientBoostingClassifier(n_estimators=100, random_state=1)
symbol_y = map(str, y)
clf.fit(X, symbol_y)
assert_array_equal(clf.predict(T), map(str, true_result))
assert_equal(100, len(clf.estimators_))
def test_float_class_labels():
"""Test with float class labels. """
clf = GradientBoostingClassifier(n_estimators=100, random_state=1)
float_y = np.asarray(y, dtype=np.float32)
clf.fit(X, float_y)
assert_array_equal(clf.predict(T),
np.asarray(true_result, dtype=np.float32))
assert_equal(100, len(clf.estimators_))
def test_shape_y():
"""Test with float class labels. """
clf = GradientBoostingClassifier(n_estimators=100, random_state=1)
y_ = np.asarray(y, dtype=np.int32)
y_ = y_[:, np.newaxis]
clf.fit(X, y_)
assert_array_equal(clf.predict(T), true_result)
assert_equal(100, len(clf.estimators_))
def test_mem_layout():
"""Test with different memory layouts of X and y"""
X_ = np.asfortranarray(X)
clf = GradientBoostingClassifier(n_estimators=100, random_state=1)
clf.fit(X_, y)
assert_array_equal(clf.predict(T), true_result)
assert_equal(100, len(clf.estimators_))
X_ = np.ascontiguousarray(X)
clf = GradientBoostingClassifier(n_estimators=100, random_state=1)
clf.fit(X_, y)
assert_array_equal(clf.predict(T), true_result)
assert_equal(100, len(clf.estimators_))
y_ = np.asarray(y, dtype=np.int32)
y_ = y_[:, np.newaxis]
y_ = np.ascontiguousarray(y_)
clf = GradientBoostingClassifier(n_estimators=100, random_state=1)
clf.fit(X, y_)
assert_array_equal(clf.predict(T), true_result)
assert_equal(100, len(clf.estimators_))
y_ = np.asarray(y, dtype=np.int32)
y_ = y_[:, np.newaxis]
y_ = np.asfortranarray(y_)
clf = GradientBoostingClassifier(n_estimators=100, random_state=1)
clf.fit(X, y_)
assert_array_equal(clf.predict(T), true_result)
assert_equal(100, len(clf.estimators_))
| bsd-3-clause |
louispotok/pandas | pandas/core/accessor.py | 4 | 7483 | # -*- coding: utf-8 -*-
"""
accessor.py contains base classes for implementing accessor properties
that can be mixed into or pinned onto other pandas classes.
"""
import warnings
from pandas.util._decorators import Appender
class DirNamesMixin(object):
_accessors = frozenset([])
_deprecations = frozenset(
['asobject', 'base', 'data', 'flags', 'itemsize', 'strides'])
def _dir_deletions(self):
""" delete unwanted __dir__ for this object """
return self._accessors | self._deprecations
def _dir_additions(self):
""" add additional __dir__ for this object """
rv = set()
for accessor in self._accessors:
try:
getattr(self, accessor)
rv.add(accessor)
except AttributeError:
pass
return rv
def __dir__(self):
"""
Provide method name lookup and completion
Only provide 'public' methods
"""
rv = set(dir(type(self)))
rv = (rv - self._dir_deletions()) | self._dir_additions()
return sorted(rv)
class PandasDelegate(object):
""" an abstract base class for delegating methods/properties """
def _delegate_property_get(self, name, *args, **kwargs):
raise TypeError("You cannot access the "
"property {name}".format(name=name))
def _delegate_property_set(self, name, value, *args, **kwargs):
raise TypeError("The property {name} cannot be set".format(name=name))
def _delegate_method(self, name, *args, **kwargs):
raise TypeError("You cannot call method {name}".format(name=name))
@classmethod
def _add_delegate_accessors(cls, delegate, accessors, typ,
overwrite=False):
"""
add accessors to cls from the delegate class
Parameters
----------
cls : the class to add the methods/properties to
delegate : the class to get methods/properties & doc-strings
acccessors : string list of accessors to add
typ : 'property' or 'method'
overwrite : boolean, default False
overwrite the method/property in the target class if it exists
"""
def _create_delegator_property(name):
def _getter(self):
return self._delegate_property_get(name)
def _setter(self, new_values):
return self._delegate_property_set(name, new_values)
_getter.__name__ = name
_setter.__name__ = name
return property(fget=_getter, fset=_setter,
doc=getattr(delegate, name).__doc__)
def _create_delegator_method(name):
def f(self, *args, **kwargs):
return self._delegate_method(name, *args, **kwargs)
f.__name__ = name
f.__doc__ = getattr(delegate, name).__doc__
return f
for name in accessors:
if typ == 'property':
f = _create_delegator_property(name)
else:
f = _create_delegator_method(name)
# don't overwrite existing methods/properties
if overwrite or not hasattr(cls, name):
setattr(cls, name, f)
# Ported with modifications from xarray
# https://github.com/pydata/xarray/blob/master/xarray/core/extensions.py
# 1. We don't need to catch and re-raise AttributeErrors as RuntimeErrors
# 2. We use a UserWarning instead of a custom Warning
class CachedAccessor(object):
"""Custom property-like object (descriptor) for caching accessors.
Parameters
----------
name : str
The namespace this will be accessed under, e.g. ``df.foo``
accessor : cls
The class with the extension methods. The class' __init__ method
should expect one of a ``Series``, ``DataFrame`` or ``Index`` as
the single argument ``data``
"""
def __init__(self, name, accessor):
self._name = name
self._accessor = accessor
def __get__(self, obj, cls):
if obj is None:
# we're accessing the attribute of the class, i.e., Dataset.geo
return self._accessor
accessor_obj = self._accessor(obj)
# Replace the property with the accessor object. Inspired by:
# http://www.pydanny.com/cached-property.html
# We need to use object.__setattr__ because we overwrite __setattr__ on
# NDFrame
object.__setattr__(obj, self._name, accessor_obj)
return accessor_obj
def _register_accessor(name, cls):
def decorator(accessor):
if hasattr(cls, name):
warnings.warn(
'registration of accessor {!r} under name {!r} for type '
'{!r} is overriding a preexisting attribute with the same '
'name.'.format(accessor, name, cls),
UserWarning,
stacklevel=2)
setattr(cls, name, CachedAccessor(name, accessor))
cls._accessors.add(name)
return accessor
return decorator
_doc = """Register a custom accessor on %(klass)s objects.
Parameters
----------
name : str
Name under which the accessor should be registered. A warning is issued
if this name conflicts with a preexisting attribute.
Notes
-----
When accessed, your accessor will be initialized with the pandas object
the user is interacting with. So the signature must be
.. code-block:: python
def __init__(self, pandas_object):
For consistency with pandas methods, you should raise an ``AttributeError``
if the data passed to your accessor has an incorrect dtype.
>>> pd.Series(['a', 'b']).dt
Traceback (most recent call last):
...
AttributeError: Can only use .dt accessor with datetimelike values
Examples
--------
In your library code::
import pandas as pd
@pd.api.extensions.register_dataframe_accessor("geo")
class GeoAccessor(object):
def __init__(self, pandas_obj):
self._obj = pandas_obj
@property
def center(self):
# return the geographic center point of this DataFrame
lat = self._obj.latitude
lon = self._obj.longitude
return (float(lon.mean()), float(lat.mean()))
def plot(self):
# plot this array's data on a map, e.g., using Cartopy
pass
Back in an interactive IPython session:
>>> ds = pd.DataFrame({'longitude': np.linspace(0, 10),
... 'latitude': np.linspace(0, 20)})
>>> ds.geo.center
(5.0, 10.0)
>>> ds.geo.plot()
# plots data on a map
See also
--------
%(others)s
"""
@Appender(_doc % dict(klass="DataFrame",
others=("register_series_accessor, "
"register_index_accessor")))
def register_dataframe_accessor(name):
from pandas import DataFrame
return _register_accessor(name, DataFrame)
@Appender(_doc % dict(klass="Series",
others=("register_dataframe_accessor, "
"register_index_accessor")))
def register_series_accessor(name):
from pandas import Series
return _register_accessor(name, Series)
@Appender(_doc % dict(klass="Index",
others=("register_dataframe_accessor, "
"register_series_accessor")))
def register_index_accessor(name):
from pandas import Index
return _register_accessor(name, Index)
| bsd-3-clause |
newemailjdm/scipy | scipy/stats/kde.py | 27 | 17303 | #-------------------------------------------------------------------------------
#
# Define classes for (uni/multi)-variate kernel density estimation.
#
# Currently, only Gaussian kernels are implemented.
#
# Written by: Robert Kern
#
# Date: 2004-08-09
#
# Modified: 2005-02-10 by Robert Kern.
# Contributed to Scipy
# 2005-10-07 by Robert Kern.
# Some fixes to match the new scipy_core
#
# Copyright 2004-2005 by Enthought, Inc.
#
#-------------------------------------------------------------------------------
from __future__ import division, print_function, absolute_import
# Standard library imports.
import warnings
# Scipy imports.
from scipy._lib.six import callable, string_types
from scipy import linalg, special
from numpy import atleast_2d, reshape, zeros, newaxis, dot, exp, pi, sqrt, \
ravel, power, atleast_1d, squeeze, sum, transpose
import numpy as np
from numpy.random import randint, multivariate_normal
# Local imports.
from . import mvn
__all__ = ['gaussian_kde']
class gaussian_kde(object):
"""Representation of a kernel-density estimate using Gaussian kernels.
Kernel density estimation is a way to estimate the probability density
function (PDF) of a random variable in a non-parametric way.
`gaussian_kde` works for both uni-variate and multi-variate data. It
includes automatic bandwidth determination. The estimation works best for
a unimodal distribution; bimodal or multi-modal distributions tend to be
oversmoothed.
Parameters
----------
dataset : array_like
Datapoints to estimate from. In case of univariate data this is a 1-D
array, otherwise a 2-D array with shape (# of dims, # of data).
bw_method : str, scalar or callable, optional
The method used to calculate the estimator bandwidth. This can be
'scott', 'silverman', a scalar constant or a callable. If a scalar,
this will be used directly as `kde.factor`. If a callable, it should
take a `gaussian_kde` instance as only parameter and return a scalar.
If None (default), 'scott' is used. See Notes for more details.
Attributes
----------
dataset : ndarray
The dataset with which `gaussian_kde` was initialized.
d : int
Number of dimensions.
n : int
Number of datapoints.
factor : float
The bandwidth factor, obtained from `kde.covariance_factor`, with which
the covariance matrix is multiplied.
covariance : ndarray
The covariance matrix of `dataset`, scaled by the calculated bandwidth
(`kde.factor`).
inv_cov : ndarray
The inverse of `covariance`.
Methods
-------
evaluate
__call__
integrate_gaussian
integrate_box_1d
integrate_box
integrate_kde
pdf
logpdf
resample
set_bandwidth
covariance_factor
Notes
-----
Bandwidth selection strongly influences the estimate obtained from the KDE
(much more so than the actual shape of the kernel). Bandwidth selection
can be done by a "rule of thumb", by cross-validation, by "plug-in
methods" or by other means; see [3]_, [4]_ for reviews. `gaussian_kde`
uses a rule of thumb, the default is Scott's Rule.
Scott's Rule [1]_, implemented as `scotts_factor`, is::
n**(-1./(d+4)),
with ``n`` the number of data points and ``d`` the number of dimensions.
Silverman's Rule [2]_, implemented as `silverman_factor`, is::
(n * (d + 2) / 4.)**(-1. / (d + 4)).
Good general descriptions of kernel density estimation can be found in [1]_
and [2]_, the mathematics for this multi-dimensional implementation can be
found in [1]_.
References
----------
.. [1] D.W. Scott, "Multivariate Density Estimation: Theory, Practice, and
Visualization", John Wiley & Sons, New York, Chicester, 1992.
.. [2] B.W. Silverman, "Density Estimation for Statistics and Data
Analysis", Vol. 26, Monographs on Statistics and Applied Probability,
Chapman and Hall, London, 1986.
.. [3] B.A. Turlach, "Bandwidth Selection in Kernel Density Estimation: A
Review", CORE and Institut de Statistique, Vol. 19, pp. 1-33, 1993.
.. [4] D.M. Bashtannyk and R.J. Hyndman, "Bandwidth selection for kernel
conditional density estimation", Computational Statistics & Data
Analysis, Vol. 36, pp. 279-298, 2001.
Examples
--------
Generate some random two-dimensional data:
>>> from scipy import stats
>>> def measure(n):
... "Measurement model, return two coupled measurements."
... m1 = np.random.normal(size=n)
... m2 = np.random.normal(scale=0.5, size=n)
... return m1+m2, m1-m2
>>> m1, m2 = measure(2000)
>>> xmin = m1.min()
>>> xmax = m1.max()
>>> ymin = m2.min()
>>> ymax = m2.max()
Perform a kernel density estimate on the data:
>>> X, Y = np.mgrid[xmin:xmax:100j, ymin:ymax:100j]
>>> positions = np.vstack([X.ravel(), Y.ravel()])
>>> values = np.vstack([m1, m2])
>>> kernel = stats.gaussian_kde(values)
>>> Z = np.reshape(kernel(positions).T, X.shape)
Plot the results:
>>> import matplotlib.pyplot as plt
>>> fig, ax = plt.subplots()
>>> ax.imshow(np.rot90(Z), cmap=plt.cm.gist_earth_r,
... extent=[xmin, xmax, ymin, ymax])
>>> ax.plot(m1, m2, 'k.', markersize=2)
>>> ax.set_xlim([xmin, xmax])
>>> ax.set_ylim([ymin, ymax])
>>> plt.show()
"""
def __init__(self, dataset, bw_method=None):
self.dataset = atleast_2d(dataset)
if not self.dataset.size > 1:
raise ValueError("`dataset` input should have multiple elements.")
self.d, self.n = self.dataset.shape
self.set_bandwidth(bw_method=bw_method)
def evaluate(self, points):
"""Evaluate the estimated pdf on a set of points.
Parameters
----------
points : (# of dimensions, # of points)-array
Alternatively, a (# of dimensions,) vector can be passed in and
treated as a single point.
Returns
-------
values : (# of points,)-array
The values at each point.
Raises
------
ValueError : if the dimensionality of the input points is different than
the dimensionality of the KDE.
"""
points = atleast_2d(points)
d, m = points.shape
if d != self.d:
if d == 1 and m == self.d:
# points was passed in as a row vector
points = reshape(points, (self.d, 1))
m = 1
else:
msg = "points have dimension %s, dataset has dimension %s" % (d,
self.d)
raise ValueError(msg)
result = zeros((m,), dtype=float)
if m >= self.n:
# there are more points than data, so loop over data
for i in range(self.n):
diff = self.dataset[:, i, newaxis] - points
tdiff = dot(self.inv_cov, diff)
energy = sum(diff*tdiff,axis=0) / 2.0
result = result + exp(-energy)
else:
# loop over points
for i in range(m):
diff = self.dataset - points[:, i, newaxis]
tdiff = dot(self.inv_cov, diff)
energy = sum(diff * tdiff, axis=0) / 2.0
result[i] = sum(exp(-energy), axis=0)
result = result / self._norm_factor
return result
__call__ = evaluate
def integrate_gaussian(self, mean, cov):
"""
Multiply estimated density by a multivariate Gaussian and integrate
over the whole space.
Parameters
----------
mean : aray_like
A 1-D array, specifying the mean of the Gaussian.
cov : array_like
A 2-D array, specifying the covariance matrix of the Gaussian.
Returns
-------
result : scalar
The value of the integral.
Raises
------
ValueError :
If the mean or covariance of the input Gaussian differs from
the KDE's dimensionality.
"""
mean = atleast_1d(squeeze(mean))
cov = atleast_2d(cov)
if mean.shape != (self.d,):
raise ValueError("mean does not have dimension %s" % self.d)
if cov.shape != (self.d, self.d):
raise ValueError("covariance does not have dimension %s" % self.d)
# make mean a column vector
mean = mean[:, newaxis]
sum_cov = self.covariance + cov
diff = self.dataset - mean
tdiff = dot(linalg.inv(sum_cov), diff)
energies = sum(diff * tdiff, axis=0) / 2.0
result = sum(exp(-energies), axis=0) / sqrt(linalg.det(2 * pi *
sum_cov)) / self.n
return result
def integrate_box_1d(self, low, high):
"""
Computes the integral of a 1D pdf between two bounds.
Parameters
----------
low : scalar
Lower bound of integration.
high : scalar
Upper bound of integration.
Returns
-------
value : scalar
The result of the integral.
Raises
------
ValueError
If the KDE is over more than one dimension.
"""
if self.d != 1:
raise ValueError("integrate_box_1d() only handles 1D pdfs")
stdev = ravel(sqrt(self.covariance))[0]
normalized_low = ravel((low - self.dataset) / stdev)
normalized_high = ravel((high - self.dataset) / stdev)
value = np.mean(special.ndtr(normalized_high) -
special.ndtr(normalized_low))
return value
def integrate_box(self, low_bounds, high_bounds, maxpts=None):
"""Computes the integral of a pdf over a rectangular interval.
Parameters
----------
low_bounds : array_like
A 1-D array containing the lower bounds of integration.
high_bounds : array_like
A 1-D array containing the upper bounds of integration.
maxpts : int, optional
The maximum number of points to use for integration.
Returns
-------
value : scalar
The result of the integral.
"""
if maxpts is not None:
extra_kwds = {'maxpts': maxpts}
else:
extra_kwds = {}
value, inform = mvn.mvnun(low_bounds, high_bounds, self.dataset,
self.covariance, **extra_kwds)
if inform:
msg = ('An integral in mvn.mvnun requires more points than %s' %
(self.d * 1000))
warnings.warn(msg)
return value
def integrate_kde(self, other):
"""
Computes the integral of the product of this kernel density estimate
with another.
Parameters
----------
other : gaussian_kde instance
The other kde.
Returns
-------
value : scalar
The result of the integral.
Raises
------
ValueError
If the KDEs have different dimensionality.
"""
if other.d != self.d:
raise ValueError("KDEs are not the same dimensionality")
# we want to iterate over the smallest number of points
if other.n < self.n:
small = other
large = self
else:
small = self
large = other
sum_cov = small.covariance + large.covariance
sum_cov_chol = linalg.cho_factor(sum_cov)
result = 0.0
for i in range(small.n):
mean = small.dataset[:, i, newaxis]
diff = large.dataset - mean
tdiff = linalg.cho_solve(sum_cov_chol, diff)
energies = sum(diff * tdiff, axis=0) / 2.0
result += sum(exp(-energies), axis=0)
result /= sqrt(linalg.det(2 * pi * sum_cov)) * large.n * small.n
return result
def resample(self, size=None):
"""
Randomly sample a dataset from the estimated pdf.
Parameters
----------
size : int, optional
The number of samples to draw. If not provided, then the size is
the same as the underlying dataset.
Returns
-------
resample : (self.d, `size`) ndarray
The sampled dataset.
"""
if size is None:
size = self.n
norm = transpose(multivariate_normal(zeros((self.d,), float),
self.covariance, size=size))
indices = randint(0, self.n, size=size)
means = self.dataset[:, indices]
return means + norm
def scotts_factor(self):
return power(self.n, -1./(self.d+4))
def silverman_factor(self):
return power(self.n*(self.d+2.0)/4.0, -1./(self.d+4))
# Default method to calculate bandwidth, can be overwritten by subclass
covariance_factor = scotts_factor
covariance_factor.__doc__ = """Computes the coefficient (`kde.factor`) that
multiplies the data covariance matrix to obtain the kernel covariance
matrix. The default is `scotts_factor`. A subclass can overwrite this
method to provide a different method, or set it through a call to
`kde.set_bandwidth`."""
def set_bandwidth(self, bw_method=None):
"""Compute the estimator bandwidth with given method.
The new bandwidth calculated after a call to `set_bandwidth` is used
for subsequent evaluations of the estimated density.
Parameters
----------
bw_method : str, scalar or callable, optional
The method used to calculate the estimator bandwidth. This can be
'scott', 'silverman', a scalar constant or a callable. If a
scalar, this will be used directly as `kde.factor`. If a callable,
it should take a `gaussian_kde` instance as only parameter and
return a scalar. If None (default), nothing happens; the current
`kde.covariance_factor` method is kept.
Notes
-----
.. versionadded:: 0.11
Examples
--------
>>> import scipy.stats as stats
>>> x1 = np.array([-7, -5, 1, 4, 5.])
>>> kde = stats.gaussian_kde(x1)
>>> xs = np.linspace(-10, 10, num=50)
>>> y1 = kde(xs)
>>> kde.set_bandwidth(bw_method='silverman')
>>> y2 = kde(xs)
>>> kde.set_bandwidth(bw_method=kde.factor / 3.)
>>> y3 = kde(xs)
>>> import matplotlib.pyplot as plt
>>> fig, ax = plt.subplots()
>>> ax.plot(x1, np.ones(x1.shape) / (4. * x1.size), 'bo',
... label='Data points (rescaled)')
>>> ax.plot(xs, y1, label='Scott (default)')
>>> ax.plot(xs, y2, label='Silverman')
>>> ax.plot(xs, y3, label='Const (1/3 * Silverman)')
>>> ax.legend()
>>> plt.show()
"""
if bw_method is None:
pass
elif bw_method == 'scott':
self.covariance_factor = self.scotts_factor
elif bw_method == 'silverman':
self.covariance_factor = self.silverman_factor
elif np.isscalar(bw_method) and not isinstance(bw_method, string_types):
self._bw_method = 'use constant'
self.covariance_factor = lambda: bw_method
elif callable(bw_method):
self._bw_method = bw_method
self.covariance_factor = lambda: self._bw_method(self)
else:
msg = "`bw_method` should be 'scott', 'silverman', a scalar " \
"or a callable."
raise ValueError(msg)
self._compute_covariance()
def _compute_covariance(self):
"""Computes the covariance matrix for each Gaussian kernel using
covariance_factor().
"""
self.factor = self.covariance_factor()
# Cache covariance and inverse covariance of the data
if not hasattr(self, '_data_inv_cov'):
self._data_covariance = atleast_2d(np.cov(self.dataset, rowvar=1,
bias=False))
self._data_inv_cov = linalg.inv(self._data_covariance)
self.covariance = self._data_covariance * self.factor**2
self.inv_cov = self._data_inv_cov / self.factor**2
self._norm_factor = sqrt(linalg.det(2*pi*self.covariance)) * self.n
def pdf(self, x):
"""
Evaluate the estimated pdf on a provided set of points.
Notes
-----
This is an alias for `gaussian_kde.evaluate`. See the ``evaluate``
docstring for more details.
"""
return self.evaluate(x)
def logpdf(self, x):
"""
Evaluate the log of the estimated pdf on a provided set of points.
Notes
-----
See `gaussian_kde.evaluate` for more details; this method simply
returns ``np.log(gaussian_kde.evaluate(x))``.
"""
return np.log(self.evaluate(x))
| bsd-3-clause |
tombstone/models | research/namignizer/data_utils.py | 19 | 4238 | # Copyright 2016 Google Inc. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Utilities for parsing Kaggle baby names files."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import os
import numpy as np
import tensorflow as tf
import pandas as pd
# the default end of name rep will be zero
_EON = 0
def read_names(names_path):
"""read data from downloaded file. See SmallNames.txt for example format
or go to https://www.kaggle.com/kaggle/us-baby-names for full lists
Args:
names_path: path to the csv file similar to the example type
Returns:
Dataset: a namedtuple of two elements: deduped names and their associated
counts. The names contain only 26 chars and are all lower case
"""
names_data = pd.read_csv(names_path)
names_data.Name = names_data.Name.str.lower()
name_data = names_data.groupby(by=["Name"])["Count"].sum()
name_counts = np.array(name_data.tolist())
names_deduped = np.array(name_data.index.tolist())
Dataset = collections.namedtuple('Dataset', ['Name', 'Count'])
return Dataset(names_deduped, name_counts)
def _letter_to_number(letter):
"""converts letters to numbers between 1 and 27"""
# ord of lower case 'a' is 97
return ord(letter) - 96
def namignizer_iterator(names, counts, batch_size, num_steps, epoch_size):
"""Takes a list of names and counts like those output from read_names, and
makes an iterator yielding a batch_size by num_steps array of random names
separated by an end of name token. The names are chosen randomly according
to their counts. The batch may end mid-name
Args:
names: a set of lowercase names composed of 26 characters
counts: a list of the frequency of those names
batch_size: int
num_steps: int
epoch_size: number of batches to yield
Yields:
(x, y): a batch_size by num_steps array of ints representing letters, where
x will be the input and y will be the target
"""
name_distribution = counts / counts.sum()
for i in range(epoch_size):
data = np.zeros(batch_size * num_steps + 1)
samples = np.random.choice(names, size=batch_size * num_steps // 2,
replace=True, p=name_distribution)
data_index = 0
for sample in samples:
if data_index >= batch_size * num_steps:
break
for letter in map(_letter_to_number, sample) + [_EON]:
if data_index >= batch_size * num_steps:
break
data[data_index] = letter
data_index += 1
x = data[:batch_size * num_steps].reshape((batch_size, num_steps))
y = data[1:batch_size * num_steps + 1].reshape((batch_size, num_steps))
yield (x, y)
def name_to_batch(name, batch_size, num_steps):
""" Takes a single name and fills a batch with it
Args:
name: lowercase composed of 26 characters
batch_size: int
num_steps: int
Returns:
x, y: a batch_size by num_steps array of ints representing letters, where
x will be the input and y will be the target. The array is filled up
to the length of the string, the rest is filled with zeros
"""
data = np.zeros(batch_size * num_steps + 1)
data_index = 0
for letter in map(_letter_to_number, name) + [_EON]:
data[data_index] = letter
data_index += 1
x = data[:batch_size * num_steps].reshape((batch_size, num_steps))
y = data[1:batch_size * num_steps + 1].reshape((batch_size, num_steps))
return x, y
| apache-2.0 |
nguyentu1602/statsmodels | statsmodels/datasets/spector/data.py | 25 | 2000 | """Spector and Mazzeo (1980) - Program Effectiveness Data"""
__docformat__ = 'restructuredtext'
COPYRIGHT = """Used with express permission of the original author, who
retains all rights. """
TITLE = __doc__
SOURCE = """
http://pages.stern.nyu.edu/~wgreene/Text/econometricanalysis.htm
The raw data was downloaded from Bill Greene's Econometric Analysis web site,
though permission was obtained from the original researcher, Dr. Lee Spector,
Professor of Economics, Ball State University."""
DESCRSHORT = """Experimental data on the effectiveness of the personalized
system of instruction (PSI) program"""
DESCRLONG = DESCRSHORT
NOTE = """::
Number of Observations - 32
Number of Variables - 4
Variable name definitions::
Grade - binary variable indicating whether or not a student's grade
improved. 1 indicates an improvement.
TUCE - Test score on economics test
PSI - participation in program
GPA - Student's grade point average
"""
import numpy as np
from statsmodels.datasets import utils as du
from os.path import dirname, abspath
def load():
"""
Load the Spector dataset and returns a Dataset class instance.
Returns
-------
Dataset instance:
See DATASET_PROPOSAL.txt for more information.
"""
data = _get_data()
return du.process_recarray(data, endog_idx=3, dtype=float)
def load_pandas():
"""
Load the Spector dataset and returns a Dataset class instance.
Returns
-------
Dataset instance:
See DATASET_PROPOSAL.txt for more information.
"""
data = _get_data()
return du.process_recarray_pandas(data, endog_idx=3, dtype=float)
def _get_data():
filepath = dirname(abspath(__file__))
##### EDIT THE FOLLOWING TO POINT TO DatasetName.csv #####
data = np.recfromtxt(open(filepath + '/spector.csv',"rb"), delimiter=" ",
names=True, dtype=float, usecols=(1,2,3,4))
return data
| bsd-3-clause |
smsolivier/VEF | tex/pres/dvs.py | 1 | 1898 | #!/usr/bin/env python3
import numpy as np
import matplotlib.pyplot as plt
import ld as LD
import mhfem_acc as MH
from hidespines import *
import sys
''' compares diffusion and transport '''
if (len(sys.argv) > 1):
outfile = sys.argv[1:]
else:
outfile = None
N = 100
xb = 2
xe = np.linspace(0, xb, N+1)
n = 8
Sigmaa = lambda x: .1
Sigmat = lambda x: 1
q = lambda x, mu: 1
BCL = 0
BCR = 1
tol = 1e-8
ld = LD.LD(xe, n, Sigmaa, Sigmat, q, BCL, BCR)
mh = MH.MHFEM(xe, Sigmaa, Sigmat, BCL, BCR, CENT=1)
mh.discretize(np.ones(N)/3, np.ones(N)/2)
xd, phid = mh.solve(np.ones(N), np.zeros(N))
x, phi, it = ld.sourceIteration(tol)
edd = ld.getEddington(.5*(ld.psiL + ld.psiR))
psiEdge = ld.edgePsi()
top = 0
for i in range(n):
top += np.fabs(ld.mu[i])*psiEdge[i,:] * ld.w[i]
B = top/ld.zeroMoment(psiEdge)
mhc = MH.MHFEM(xe, Sigmaa, Sigmat, BCL, BCR, CENT=1)
mhc.discretize(edd, B)
xc, phic = mhc.solve(np.ones(N), np.zeros(N))
fsize = 20
plt.figure()
plt.plot(x*Sigmat(x), phi, label='S$_8$')
plt.plot(xd*Sigmat(xd), phid, label='Diffusion')
plt.xlabel(r'$\Sigma_t x$', fontsize=fsize)
plt.ylabel(r'$\phi(x)$', fontsize=fsize)
plt.legend(loc='best', frameon=False)
hidespines(plt.gca())
if (outfile != None):
plt.savefig(outfile[0], transparent=True)
plt.figure()
plt.axhline(1/3, color='k', alpha=.4)
plt.plot(x*Sigmat(x), edd)
plt.xlabel(r'$\Sigma_t x$', fontsize=fsize)
plt.ylabel(r'$\langle \mu^2 \rightangle(x)$', fontsize=fsize)
hidespines(plt.gca())
if (outfile != None):
plt.savefig(outfile[1], transparent=True)
plt.figure()
plt.plot(x*Sigmat(x), phi, '--', label='S$_8$')
plt.plot(x*Sigmat(x), phic, label='Corrected Diffusion')
plt.legend(loc='best', frameon=False)
plt.xlabel(r'$\Sigma_t x$', fontsize=fsize)
plt.ylabel(r'$\phi(x)$', fontsize=fsize)
hidespines(plt.gca())
if (outfile != None):
plt.savefig(outfile[2], transparent=True)
else:
plt.show() | mit |
allentran/fed-rates-bot | fed_bot/model/data.py | 1 | 8747 | __author__ = 'allentran'
import json
import os
import re
import datetime
import unidecode
from spacy.en import English
import requests
import pandas as pd
import numpy as np
import allen_utils
logger = allen_utils.get_logger(__name__)
class Interval(object):
def __init__(self, start, end):
assert isinstance(start, datetime.date) and isinstance(end, datetime.date)
self.start = start
self.end = end
def contains(self, new_date):
assert isinstance(new_date, datetime.date)
return (new_date >= self.start) and (new_date <= self.end)
fed_regimes = {
0: Interval(datetime.date(1951, 4, 2), datetime.date(1970, 1, 31)),
1: Interval(datetime.date(1970, 2, 1), datetime.date(1978, 3, 7)),
2: Interval(datetime.date(1978, 3, 8), datetime.date(1979, 8, 6)),
3: Interval(datetime.date(1979, 8, 7), datetime.date(1987, 8, 11)),
4: Interval(datetime.date(1987, 8, 12), datetime.date(2006, 1, 31)),
5: Interval(datetime.date(2006, 2, 1), datetime.date(2020, 1, 31)),
}
def find_regime(date):
for regime, interval in fed_regimes.iteritems():
if interval.contains(date):
return regime
raise ValueError("Could not find regime for date, %s", date)
class PairedDocAndRates(object):
def __init__(self, date, sentences, is_minutes):
self.date = date
self.sentences = sentences
self.is_minutes = is_minutes
self.rates = None
self.regime = find_regime(date)
def match_rates(self, rates_df, days = [30, 90, 180]):
def get_closest_rate(days_to_add):
future_date = self.date + datetime.timedelta(days=days_to_add)
diff = abs(future_date - rates_df['date'])
if (last_available_date - future_date).total_seconds() >= 0:
closest_index = diff.argmin()
return float(rates_df.iloc[closest_index]['value'])
else:
return None
future_rates = {}
last_available_date = rates_df['date'].iloc[-1]
current_rate = get_closest_rate(0)
if current_rate:
future_rates['0'] = current_rate
for add_days in days:
future_rate = get_closest_rate(add_days)
if future_rate:
future_rates[str(add_days)] = future_rate
self.rates = future_rates
def to_dict(self):
return dict(
date = self.date.strftime('%Y-%m-%d'),
sentences = self.sentences,
rates = self.rates,
is_minutes = self.is_minutes,
regime = self.regime
)
class Vocab(object):
def __init__(self):
self.vocab = {}
self.special_words = [
'$CARDINAL$',
'$DATE$',
'$UNKNOWN$'
]
def update_count(self, word):
if word not in self.vocab:
self.vocab[word] = 1
else:
self.vocab[word] += 1
def to_dict(self, min_count=5):
position_dict = {word: idx for idx, word in enumerate(self.special_words)}
counter = len(self.special_words)
for word, word_count in self.vocab.iteritems():
if word_count >= min_count:
position_dict[word] = counter
counter += 1
return position_dict
class DataTransformer(object):
def __init__(self, data_dir, min_sentence_length):
self.url = 'https://api.stlouisfed.org/fred/series/observations'
self.data_dir = data_dir
self.min_sentence_length = min_sentence_length
self.replace_entities = {
'DATE': '$DATE$',
'CARDINAL': '$CARDINAL$'
}
self.nlp = English()
# custom token replacement
self.regexes = [
(re.compile(r'\d{4}'), '$DATE$'),
(re.compile(r'\d+[\.,]*\d+'), '$CARDINAL$')
]
self.vocab = Vocab()
self.word_positions = None
self.rates = None
self.docs = None
def get_rates(self, api_key):
params = dict(
api_key=api_key,
file_type='json',
series_id='FEDFUNDS'
)
r = requests.get(self.url, params=params)
if r.status_code == 200:
self.rates = pd.DataFrame(r.json()['observations'])
self.rates['date'] = self.rates['date'].apply(lambda s: datetime.datetime.strptime(s, '%Y-%m-%d').date())
self.rates.sort('date')
def build_vocab(self):
def process_doc(doc_path):
with open(doc_path, 'r') as f:
text = unidecode.unidecode(unicode(f.read().decode('iso-8859-1')))
text = ' '.join(text.split()).strip()
if len(text) > 0:
doc = self.nlp(unicode(text.lower()))
doc_words = set()
for sent in doc.sents:
if len(sent) > self.min_sentence_length:
for token in doc:
if token.text not in doc_words:
self.vocab.update_count(token.text)
doc_words.add(token.text)
file_re = re.compile(r'\d{8}')
for root, dirs, filenames in os.walk(self.data_dir):
for filename in filenames:
if file_re.search(filename):
filepath = os.path.join(root, filename)
process_doc(filepath)
logger.info("Built vocab from: %s", filepath)
self.word_positions = self.vocab.to_dict()
def strip_text(self, text):
doc = self.nlp(unicode(text).lower())
# spacy entity replacement
ents_dict = {ent.text: self.replace_entities[ent.label_] for ent in doc.ents if ent.label_ in self.replace_entities.keys()}
for ent in ents_dict:
text = text.replace(ent, ents_dict[ent])
return text
def get_docs(self, min_sentence_length=8):
def parse_doc(doc_path):
with open(doc_path, 'r') as f:
text = unidecode.unidecode(unicode(f.read().decode('iso-8859-1')))
text = ' '.join(text.split()).strip()
if len(text) > 0:
date = datetime.datetime.strptime(date_re.search(doc_path).group(0), '%Y%m%d').date()
stripped_text = self.strip_text(text)
doc = self.nlp(unicode(stripped_text))
sentences = list(doc.sents)
doc_sents = []
for sent in sentences[1:]:
if len(sent) > min_sentence_length:
sentence_as_idxes = []
for token in sent:
skip = False
for regex, replacement_token in self.regexes:
match = regex.match(token.text)
if match:
sentence_as_idxes.append(self.word_positions[replacement_token])
skip = True
if not skip:
try:
sentence_as_idxes.append(self.word_positions[token.text])
except KeyError:
sentence_as_idxes.append(self.word_positions['$UNKNOWN$'])
doc_sents.append(sentence_as_idxes)
paired_doc = PairedDocAndRates(date, doc_sents, doc_path.find('minutes') > -1)
paired_doc.match_rates(self.rates)
return paired_doc
date_re = re.compile(r'\d{8}')
file_re = re.compile(r'\d{8}')
docs = []
for root, dirs, filenames in os.walk(self.data_dir):
for filename in filenames:
if file_re.search(filename):
filepath = os.path.join(root, filename)
parsed_doc = parse_doc(filepath)
if parsed_doc:
logger.info("Parsed %s", filepath)
docs.append(parsed_doc)
self.docs = docs
def save_output(self):
with open(os.path.join(self.data_dir, 'paired_data.json'), 'w') as f:
json.dump([doc.to_dict() for doc in self.docs], f, indent=2, sort_keys=True)
with open(os.path.join(self.data_dir, 'dictionary.json'), 'w') as f:
json.dump(self.vocab.to_dict(), f, indent=2, sort_keys=True)
if __name__ == "__main__":
data_transformer = DataTransformer('data', min_sentence_length=8)
data_transformer.build_vocab()
data_transformer.get_rates('51c09c6b8aa464671aa8ac96c76a8416')
data_transformer.get_docs()
data_transformer.save_output()
| mit |
tomlof/scikit-learn | sklearn/utils/multiclass.py | 41 | 14732 |
# Author: Arnaud Joly, Joel Nothman, Hamzeh Alsalhi
#
# License: BSD 3 clause
"""
Multi-class / multi-label utility function
==========================================
"""
from __future__ import division
from collections import Sequence
from itertools import chain
from scipy.sparse import issparse
from scipy.sparse.base import spmatrix
from scipy.sparse import dok_matrix
from scipy.sparse import lil_matrix
import numpy as np
from ..externals.six import string_types
from .validation import check_array
from ..utils.fixes import bincount
from ..utils.fixes import array_equal
def _unique_multiclass(y):
if hasattr(y, '__array__'):
return np.unique(np.asarray(y))
else:
return set(y)
def _unique_indicator(y):
return np.arange(check_array(y, ['csr', 'csc', 'coo']).shape[1])
_FN_UNIQUE_LABELS = {
'binary': _unique_multiclass,
'multiclass': _unique_multiclass,
'multilabel-indicator': _unique_indicator,
}
def unique_labels(*ys):
"""Extract an ordered array of unique labels
We don't allow:
- mix of multilabel and multiclass (single label) targets
- mix of label indicator matrix and anything else,
because there are no explicit labels)
- mix of label indicator matrices of different sizes
- mix of string and integer labels
At the moment, we also don't allow "multiclass-multioutput" input type.
Parameters
----------
*ys : array-likes,
Returns
-------
out : numpy array of shape [n_unique_labels]
An ordered array of unique labels.
Examples
--------
>>> from sklearn.utils.multiclass import unique_labels
>>> unique_labels([3, 5, 5, 5, 7, 7])
array([3, 5, 7])
>>> unique_labels([1, 2, 3, 4], [2, 2, 3, 4])
array([1, 2, 3, 4])
>>> unique_labels([1, 2, 10], [5, 11])
array([ 1, 2, 5, 10, 11])
"""
if not ys:
raise ValueError('No argument has been passed.')
# Check that we don't mix label format
ys_types = set(type_of_target(x) for x in ys)
if ys_types == set(["binary", "multiclass"]):
ys_types = set(["multiclass"])
if len(ys_types) > 1:
raise ValueError("Mix type of y not allowed, got types %s" % ys_types)
label_type = ys_types.pop()
# Check consistency for the indicator format
if (label_type == "multilabel-indicator" and
len(set(check_array(y, ['csr', 'csc', 'coo']).shape[1]
for y in ys)) > 1):
raise ValueError("Multi-label binary indicator input with "
"different numbers of labels")
# Get the unique set of labels
_unique_labels = _FN_UNIQUE_LABELS.get(label_type, None)
if not _unique_labels:
raise ValueError("Unknown label type: %s" % repr(ys))
ys_labels = set(chain.from_iterable(_unique_labels(y) for y in ys))
# Check that we don't mix string type with number type
if (len(set(isinstance(label, string_types) for label in ys_labels)) > 1):
raise ValueError("Mix of label input types (string and number)")
return np.array(sorted(ys_labels))
def _is_integral_float(y):
return y.dtype.kind == 'f' and np.all(y.astype(int) == y)
def is_multilabel(y):
""" Check if ``y`` is in a multilabel format.
Parameters
----------
y : numpy array of shape [n_samples]
Target values.
Returns
-------
out : bool,
Return ``True``, if ``y`` is in a multilabel format, else ```False``.
Examples
--------
>>> import numpy as np
>>> from sklearn.utils.multiclass import is_multilabel
>>> is_multilabel([0, 1, 0, 1])
False
>>> is_multilabel([[1], [0, 2], []])
False
>>> is_multilabel(np.array([[1, 0], [0, 0]]))
True
>>> is_multilabel(np.array([[1], [0], [0]]))
False
>>> is_multilabel(np.array([[1, 0, 0]]))
True
"""
if hasattr(y, '__array__'):
y = np.asarray(y)
if not (hasattr(y, "shape") and y.ndim == 2 and y.shape[1] > 1):
return False
if issparse(y):
if isinstance(y, (dok_matrix, lil_matrix)):
y = y.tocsr()
return (len(y.data) == 0 or np.unique(y.data).size == 1 and
(y.dtype.kind in 'biu' or # bool, int, uint
_is_integral_float(np.unique(y.data))))
else:
labels = np.unique(y)
return len(labels) < 3 and (y.dtype.kind in 'biu' or # bool, int, uint
_is_integral_float(labels))
def check_classification_targets(y):
"""Ensure that target y is of a non-regression type.
Only the following target types (as defined in type_of_target) are allowed:
'binary', 'multiclass', 'multiclass-multioutput',
'multilabel-indicator', 'multilabel-sequences'
Parameters
----------
y : array-like
"""
y_type = type_of_target(y)
if y_type not in ['binary', 'multiclass', 'multiclass-multioutput',
'multilabel-indicator', 'multilabel-sequences']:
raise ValueError("Unknown label type: %r" % y_type)
def type_of_target(y):
"""Determine the type of data indicated by target `y`
Parameters
----------
y : array-like
Returns
-------
target_type : string
One of:
* 'continuous': `y` is an array-like of floats that are not all
integers, and is 1d or a column vector.
* 'continuous-multioutput': `y` is a 2d array of floats that are
not all integers, and both dimensions are of size > 1.
* 'binary': `y` contains <= 2 discrete values and is 1d or a column
vector.
* 'multiclass': `y` contains more than two discrete values, is not a
sequence of sequences, and is 1d or a column vector.
* 'multiclass-multioutput': `y` is a 2d array that contains more
than two discrete values, is not a sequence of sequences, and both
dimensions are of size > 1.
* 'multilabel-indicator': `y` is a label indicator matrix, an array
of two dimensions with at least two columns, and at most 2 unique
values.
* 'unknown': `y` is array-like but none of the above, such as a 3d
array, sequence of sequences, or an array of non-sequence objects.
Examples
--------
>>> import numpy as np
>>> type_of_target([0.1, 0.6])
'continuous'
>>> type_of_target([1, -1, -1, 1])
'binary'
>>> type_of_target(['a', 'b', 'a'])
'binary'
>>> type_of_target([1.0, 2.0])
'binary'
>>> type_of_target([1, 0, 2])
'multiclass'
>>> type_of_target([1.0, 0.0, 3.0])
'multiclass'
>>> type_of_target(['a', 'b', 'c'])
'multiclass'
>>> type_of_target(np.array([[1, 2], [3, 1]]))
'multiclass-multioutput'
>>> type_of_target([[1, 2]])
'multiclass-multioutput'
>>> type_of_target(np.array([[1.5, 2.0], [3.0, 1.6]]))
'continuous-multioutput'
>>> type_of_target(np.array([[0, 1], [1, 1]]))
'multilabel-indicator'
"""
valid = ((isinstance(y, (Sequence, spmatrix)) or hasattr(y, '__array__'))
and not isinstance(y, string_types))
if not valid:
raise ValueError('Expected array-like (array or non-string sequence), '
'got %r' % y)
if is_multilabel(y):
return 'multilabel-indicator'
try:
y = np.asarray(y)
except ValueError:
# Known to fail in numpy 1.3 for array of arrays
return 'unknown'
# The old sequence of sequences format
try:
if (not hasattr(y[0], '__array__') and isinstance(y[0], Sequence)
and not isinstance(y[0], string_types)):
raise ValueError('You appear to be using a legacy multi-label data'
' representation. Sequence of sequences are no'
' longer supported; use a binary array or sparse'
' matrix instead.')
except IndexError:
pass
# Invalid inputs
if y.ndim > 2 or (y.dtype == object and len(y) and
not isinstance(y.flat[0], string_types)):
return 'unknown' # [[[1, 2]]] or [obj_1] and not ["label_1"]
if y.ndim == 2 and y.shape[1] == 0:
return 'unknown' # [[]]
if y.ndim == 2 and y.shape[1] > 1:
suffix = "-multioutput" # [[1, 2], [1, 2]]
else:
suffix = "" # [1, 2, 3] or [[1], [2], [3]]
# check float and contains non-integer float values
if y.dtype.kind == 'f' and np.any(y != y.astype(int)):
# [.1, .2, 3] or [[.1, .2, 3]] or [[1., .2]] and not [1., 2., 3.]
return 'continuous' + suffix
if (len(np.unique(y)) > 2) or (y.ndim >= 2 and len(y[0]) > 1):
return 'multiclass' + suffix # [1, 2, 3] or [[1., 2., 3]] or [[1, 2]]
else:
return 'binary' # [1, 2] or [["a"], ["b"]]
def _check_partial_fit_first_call(clf, classes=None):
"""Private helper function for factorizing common classes param logic
Estimators that implement the ``partial_fit`` API need to be provided with
the list of possible classes at the first call to partial_fit.
Subsequent calls to partial_fit should check that ``classes`` is still
consistent with a previous value of ``clf.classes_`` when provided.
This function returns True if it detects that this was the first call to
``partial_fit`` on ``clf``. In that case the ``classes_`` attribute is also
set on ``clf``.
"""
if getattr(clf, 'classes_', None) is None and classes is None:
raise ValueError("classes must be passed on the first call "
"to partial_fit.")
elif classes is not None:
if getattr(clf, 'classes_', None) is not None:
if not array_equal(clf.classes_, unique_labels(classes)):
raise ValueError(
"`classes=%r` is not the same as on last call "
"to partial_fit, was: %r" % (classes, clf.classes_))
else:
# This is the first call to partial_fit
clf.classes_ = unique_labels(classes)
return True
# classes is None and clf.classes_ has already previously been set:
# nothing to do
return False
def class_distribution(y, sample_weight=None):
"""Compute class priors from multioutput-multiclass target data
Parameters
----------
y : array like or sparse matrix of size (n_samples, n_outputs)
The labels for each example.
sample_weight : array-like of shape = (n_samples,), optional
Sample weights.
Returns
-------
classes : list of size n_outputs of arrays of size (n_classes,)
List of classes for each column.
n_classes : list of integers of size n_outputs
Number of classes in each column
class_prior : list of size n_outputs of arrays of size (n_classes,)
Class distribution of each column.
"""
classes = []
n_classes = []
class_prior = []
n_samples, n_outputs = y.shape
if issparse(y):
y = y.tocsc()
y_nnz = np.diff(y.indptr)
for k in range(n_outputs):
col_nonzero = y.indices[y.indptr[k]:y.indptr[k + 1]]
# separate sample weights for zero and non-zero elements
if sample_weight is not None:
nz_samp_weight = np.asarray(sample_weight)[col_nonzero]
zeros_samp_weight_sum = (np.sum(sample_weight) -
np.sum(nz_samp_weight))
else:
nz_samp_weight = None
zeros_samp_weight_sum = y.shape[0] - y_nnz[k]
classes_k, y_k = np.unique(y.data[y.indptr[k]:y.indptr[k + 1]],
return_inverse=True)
class_prior_k = bincount(y_k, weights=nz_samp_weight)
# An explicit zero was found, combine its weight with the weight
# of the implicit zeros
if 0 in classes_k:
class_prior_k[classes_k == 0] += zeros_samp_weight_sum
# If an there is an implicit zero and it is not in classes and
# class_prior, make an entry for it
if 0 not in classes_k and y_nnz[k] < y.shape[0]:
classes_k = np.insert(classes_k, 0, 0)
class_prior_k = np.insert(class_prior_k, 0,
zeros_samp_weight_sum)
classes.append(classes_k)
n_classes.append(classes_k.shape[0])
class_prior.append(class_prior_k / class_prior_k.sum())
else:
for k in range(n_outputs):
classes_k, y_k = np.unique(y[:, k], return_inverse=True)
classes.append(classes_k)
n_classes.append(classes_k.shape[0])
class_prior_k = bincount(y_k, weights=sample_weight)
class_prior.append(class_prior_k / class_prior_k.sum())
return (classes, n_classes, class_prior)
def _ovr_decision_function(predictions, confidences, n_classes):
"""Compute a continuous, tie-breaking ovr decision function.
It is important to include a continuous value, not only votes,
to make computing AUC or calibration meaningful.
Parameters
----------
predictions : array-like, shape (n_samples, n_classifiers)
Predicted classes for each binary classifier.
confidences : array-like, shape (n_samples, n_classifiers)
Decision functions or predicted probabilities for positive class
for each binary classifier.
n_classes : int
Number of classes. n_classifiers must be
``n_classes * (n_classes - 1 ) / 2``
"""
n_samples = predictions.shape[0]
votes = np.zeros((n_samples, n_classes))
sum_of_confidences = np.zeros((n_samples, n_classes))
k = 0
for i in range(n_classes):
for j in range(i + 1, n_classes):
sum_of_confidences[:, i] -= confidences[:, k]
sum_of_confidences[:, j] += confidences[:, k]
votes[predictions[:, k] == 0, i] += 1
votes[predictions[:, k] == 1, j] += 1
k += 1
max_confidences = sum_of_confidences.max()
min_confidences = sum_of_confidences.min()
if max_confidences == min_confidences:
return votes
# Scale the sum_of_confidences to (-0.5, 0.5) and add it with votes.
# The motivation is to use confidence levels as a way to break ties in
# the votes without switching any decision made based on a difference
# of 1 vote.
eps = np.finfo(sum_of_confidences.dtype).eps
max_abs_confidence = max(abs(max_confidences), abs(min_confidences))
scale = (0.5 - eps) / max_abs_confidence
return votes + sum_of_confidences * scale
| bsd-3-clause |
pratapvardhan/scikit-learn | sklearn/linear_model/tests/test_coordinate_descent.py | 25 | 25114 | # Authors: Olivier Grisel <[email protected]>
# Alexandre Gramfort <[email protected]>
# License: BSD 3 clause
from sys import version_info
import numpy as np
from scipy import interpolate, sparse
from copy import deepcopy
from sklearn.datasets import load_boston
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import SkipTest
from sklearn.utils.testing import assert_true
from sklearn.utils.testing import assert_greater
from sklearn.utils.testing import assert_raises
from sklearn.utils.testing import assert_warns
from sklearn.utils.testing import assert_warns_message
from sklearn.utils.testing import ignore_warnings
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import TempMemmap
from sklearn.linear_model.coordinate_descent import Lasso, \
LassoCV, ElasticNet, ElasticNetCV, MultiTaskLasso, MultiTaskElasticNet, \
MultiTaskElasticNetCV, MultiTaskLassoCV, lasso_path, enet_path
from sklearn.linear_model import LassoLarsCV, lars_path
from sklearn.utils import check_array
def check_warnings():
if version_info < (2, 6):
raise SkipTest("Testing for warnings is not supported in versions \
older than Python 2.6")
def test_lasso_zero():
# Check that the lasso can handle zero data without crashing
X = [[0], [0], [0]]
y = [0, 0, 0]
clf = Lasso(alpha=0.1).fit(X, y)
pred = clf.predict([[1], [2], [3]])
assert_array_almost_equal(clf.coef_, [0])
assert_array_almost_equal(pred, [0, 0, 0])
assert_almost_equal(clf.dual_gap_, 0)
def test_lasso_toy():
# Test Lasso on a toy example for various values of alpha.
# When validating this against glmnet notice that glmnet divides it
# against nobs.
X = [[-1], [0], [1]]
Y = [-1, 0, 1] # just a straight line
T = [[2], [3], [4]] # test sample
clf = Lasso(alpha=1e-8)
clf.fit(X, Y)
pred = clf.predict(T)
assert_array_almost_equal(clf.coef_, [1])
assert_array_almost_equal(pred, [2, 3, 4])
assert_almost_equal(clf.dual_gap_, 0)
clf = Lasso(alpha=0.1)
clf.fit(X, Y)
pred = clf.predict(T)
assert_array_almost_equal(clf.coef_, [.85])
assert_array_almost_equal(pred, [1.7, 2.55, 3.4])
assert_almost_equal(clf.dual_gap_, 0)
clf = Lasso(alpha=0.5)
clf.fit(X, Y)
pred = clf.predict(T)
assert_array_almost_equal(clf.coef_, [.25])
assert_array_almost_equal(pred, [0.5, 0.75, 1.])
assert_almost_equal(clf.dual_gap_, 0)
clf = Lasso(alpha=1)
clf.fit(X, Y)
pred = clf.predict(T)
assert_array_almost_equal(clf.coef_, [.0])
assert_array_almost_equal(pred, [0, 0, 0])
assert_almost_equal(clf.dual_gap_, 0)
def test_enet_toy():
# Test ElasticNet for various parameters of alpha and l1_ratio.
# Actually, the parameters alpha = 0 should not be allowed. However,
# we test it as a border case.
# ElasticNet is tested with and without precomputed Gram matrix
X = np.array([[-1.], [0.], [1.]])
Y = [-1, 0, 1] # just a straight line
T = [[2.], [3.], [4.]] # test sample
# this should be the same as lasso
clf = ElasticNet(alpha=1e-8, l1_ratio=1.0)
clf.fit(X, Y)
pred = clf.predict(T)
assert_array_almost_equal(clf.coef_, [1])
assert_array_almost_equal(pred, [2, 3, 4])
assert_almost_equal(clf.dual_gap_, 0)
clf = ElasticNet(alpha=0.5, l1_ratio=0.3, max_iter=100,
precompute=False)
clf.fit(X, Y)
pred = clf.predict(T)
assert_array_almost_equal(clf.coef_, [0.50819], decimal=3)
assert_array_almost_equal(pred, [1.0163, 1.5245, 2.0327], decimal=3)
assert_almost_equal(clf.dual_gap_, 0)
clf.set_params(max_iter=100, precompute=True)
clf.fit(X, Y) # with Gram
pred = clf.predict(T)
assert_array_almost_equal(clf.coef_, [0.50819], decimal=3)
assert_array_almost_equal(pred, [1.0163, 1.5245, 2.0327], decimal=3)
assert_almost_equal(clf.dual_gap_, 0)
clf.set_params(max_iter=100, precompute=np.dot(X.T, X))
clf.fit(X, Y) # with Gram
pred = clf.predict(T)
assert_array_almost_equal(clf.coef_, [0.50819], decimal=3)
assert_array_almost_equal(pred, [1.0163, 1.5245, 2.0327], decimal=3)
assert_almost_equal(clf.dual_gap_, 0)
clf = ElasticNet(alpha=0.5, l1_ratio=0.5)
clf.fit(X, Y)
pred = clf.predict(T)
assert_array_almost_equal(clf.coef_, [0.45454], 3)
assert_array_almost_equal(pred, [0.9090, 1.3636, 1.8181], 3)
assert_almost_equal(clf.dual_gap_, 0)
def build_dataset(n_samples=50, n_features=200, n_informative_features=10,
n_targets=1):
"""
build an ill-posed linear regression problem with many noisy features and
comparatively few samples
"""
random_state = np.random.RandomState(0)
if n_targets > 1:
w = random_state.randn(n_features, n_targets)
else:
w = random_state.randn(n_features)
w[n_informative_features:] = 0.0
X = random_state.randn(n_samples, n_features)
y = np.dot(X, w)
X_test = random_state.randn(n_samples, n_features)
y_test = np.dot(X_test, w)
return X, y, X_test, y_test
def test_lasso_cv():
X, y, X_test, y_test = build_dataset()
max_iter = 150
clf = LassoCV(n_alphas=10, eps=1e-3, max_iter=max_iter).fit(X, y)
assert_almost_equal(clf.alpha_, 0.056, 2)
clf = LassoCV(n_alphas=10, eps=1e-3, max_iter=max_iter, precompute=True)
clf.fit(X, y)
assert_almost_equal(clf.alpha_, 0.056, 2)
# Check that the lars and the coordinate descent implementation
# select a similar alpha
lars = LassoLarsCV(normalize=False, max_iter=30).fit(X, y)
# for this we check that they don't fall in the grid of
# clf.alphas further than 1
assert_true(np.abs(
np.searchsorted(clf.alphas_[::-1], lars.alpha_) -
np.searchsorted(clf.alphas_[::-1], clf.alpha_)) <= 1)
# check that they also give a similar MSE
mse_lars = interpolate.interp1d(lars.cv_alphas_, lars.cv_mse_path_.T)
np.testing.assert_approx_equal(mse_lars(clf.alphas_[5]).mean(),
clf.mse_path_[5].mean(), significant=2)
# test set
assert_greater(clf.score(X_test, y_test), 0.99)
def test_lasso_cv_positive_constraint():
X, y, X_test, y_test = build_dataset()
max_iter = 500
# Ensure the unconstrained fit has a negative coefficient
clf_unconstrained = LassoCV(n_alphas=3, eps=1e-1, max_iter=max_iter, cv=2,
n_jobs=1)
clf_unconstrained.fit(X, y)
assert_true(min(clf_unconstrained.coef_) < 0)
# On same data, constrained fit has non-negative coefficients
clf_constrained = LassoCV(n_alphas=3, eps=1e-1, max_iter=max_iter,
positive=True, cv=2, n_jobs=1)
clf_constrained.fit(X, y)
assert_true(min(clf_constrained.coef_) >= 0)
def test_lasso_path_return_models_vs_new_return_gives_same_coefficients():
# Test that lasso_path with lars_path style output gives the
# same result
# Some toy data
X = np.array([[1, 2, 3.1], [2.3, 5.4, 4.3]]).T
y = np.array([1, 2, 3.1])
alphas = [5., 1., .5]
# Use lars_path and lasso_path(new output) with 1D linear interpolation
# to compute the same path
alphas_lars, _, coef_path_lars = lars_path(X, y, method='lasso')
coef_path_cont_lars = interpolate.interp1d(alphas_lars[::-1],
coef_path_lars[:, ::-1])
alphas_lasso2, coef_path_lasso2, _ = lasso_path(X, y, alphas=alphas,
return_models=False)
coef_path_cont_lasso = interpolate.interp1d(alphas_lasso2[::-1],
coef_path_lasso2[:, ::-1])
assert_array_almost_equal(
coef_path_cont_lasso(alphas), coef_path_cont_lars(alphas),
decimal=1)
def test_enet_path():
# We use a large number of samples and of informative features so that
# the l1_ratio selected is more toward ridge than lasso
X, y, X_test, y_test = build_dataset(n_samples=200, n_features=100,
n_informative_features=100)
max_iter = 150
# Here we have a small number of iterations, and thus the
# ElasticNet might not converge. This is to speed up tests
clf = ElasticNetCV(alphas=[0.01, 0.05, 0.1], eps=2e-3,
l1_ratio=[0.5, 0.7], cv=3,
max_iter=max_iter)
ignore_warnings(clf.fit)(X, y)
# Well-conditioned settings, we should have selected our
# smallest penalty
assert_almost_equal(clf.alpha_, min(clf.alphas_))
# Non-sparse ground truth: we should have selected an elastic-net
# that is closer to ridge than to lasso
assert_equal(clf.l1_ratio_, min(clf.l1_ratio))
clf = ElasticNetCV(alphas=[0.01, 0.05, 0.1], eps=2e-3,
l1_ratio=[0.5, 0.7], cv=3,
max_iter=max_iter, precompute=True)
ignore_warnings(clf.fit)(X, y)
# Well-conditioned settings, we should have selected our
# smallest penalty
assert_almost_equal(clf.alpha_, min(clf.alphas_))
# Non-sparse ground truth: we should have selected an elastic-net
# that is closer to ridge than to lasso
assert_equal(clf.l1_ratio_, min(clf.l1_ratio))
# We are in well-conditioned settings with low noise: we should
# have a good test-set performance
assert_greater(clf.score(X_test, y_test), 0.99)
# Multi-output/target case
X, y, X_test, y_test = build_dataset(n_features=10, n_targets=3)
clf = MultiTaskElasticNetCV(n_alphas=5, eps=2e-3, l1_ratio=[0.5, 0.7],
cv=3, max_iter=max_iter)
ignore_warnings(clf.fit)(X, y)
# We are in well-conditioned settings with low noise: we should
# have a good test-set performance
assert_greater(clf.score(X_test, y_test), 0.99)
assert_equal(clf.coef_.shape, (3, 10))
# Mono-output should have same cross-validated alpha_ and l1_ratio_
# in both cases.
X, y, _, _ = build_dataset(n_features=10)
clf1 = ElasticNetCV(n_alphas=5, eps=2e-3, l1_ratio=[0.5, 0.7])
clf1.fit(X, y)
clf2 = MultiTaskElasticNetCV(n_alphas=5, eps=2e-3, l1_ratio=[0.5, 0.7])
clf2.fit(X, y[:, np.newaxis])
assert_almost_equal(clf1.l1_ratio_, clf2.l1_ratio_)
assert_almost_equal(clf1.alpha_, clf2.alpha_)
def test_path_parameters():
X, y, _, _ = build_dataset()
max_iter = 100
clf = ElasticNetCV(n_alphas=50, eps=1e-3, max_iter=max_iter,
l1_ratio=0.5, tol=1e-3)
clf.fit(X, y) # new params
assert_almost_equal(0.5, clf.l1_ratio)
assert_equal(50, clf.n_alphas)
assert_equal(50, len(clf.alphas_))
def test_warm_start():
X, y, _, _ = build_dataset()
clf = ElasticNet(alpha=0.1, max_iter=5, warm_start=True)
ignore_warnings(clf.fit)(X, y)
ignore_warnings(clf.fit)(X, y) # do a second round with 5 iterations
clf2 = ElasticNet(alpha=0.1, max_iter=10)
ignore_warnings(clf2.fit)(X, y)
assert_array_almost_equal(clf2.coef_, clf.coef_)
def test_lasso_alpha_warning():
X = [[-1], [0], [1]]
Y = [-1, 0, 1] # just a straight line
clf = Lasso(alpha=0)
assert_warns(UserWarning, clf.fit, X, Y)
def test_lasso_positive_constraint():
X = [[-1], [0], [1]]
y = [1, 0, -1] # just a straight line with negative slope
lasso = Lasso(alpha=0.1, max_iter=1000, positive=True)
lasso.fit(X, y)
assert_true(min(lasso.coef_) >= 0)
lasso = Lasso(alpha=0.1, max_iter=1000, precompute=True, positive=True)
lasso.fit(X, y)
assert_true(min(lasso.coef_) >= 0)
def test_enet_positive_constraint():
X = [[-1], [0], [1]]
y = [1, 0, -1] # just a straight line with negative slope
enet = ElasticNet(alpha=0.1, max_iter=1000, positive=True)
enet.fit(X, y)
assert_true(min(enet.coef_) >= 0)
def test_enet_cv_positive_constraint():
X, y, X_test, y_test = build_dataset()
max_iter = 500
# Ensure the unconstrained fit has a negative coefficient
enetcv_unconstrained = ElasticNetCV(n_alphas=3, eps=1e-1,
max_iter=max_iter,
cv=2, n_jobs=1)
enetcv_unconstrained.fit(X, y)
assert_true(min(enetcv_unconstrained.coef_) < 0)
# On same data, constrained fit has non-negative coefficients
enetcv_constrained = ElasticNetCV(n_alphas=3, eps=1e-1, max_iter=max_iter,
cv=2, positive=True, n_jobs=1)
enetcv_constrained.fit(X, y)
assert_true(min(enetcv_constrained.coef_) >= 0)
def test_uniform_targets():
enet = ElasticNetCV(fit_intercept=True, n_alphas=3)
m_enet = MultiTaskElasticNetCV(fit_intercept=True, n_alphas=3)
lasso = LassoCV(fit_intercept=True, n_alphas=3)
m_lasso = MultiTaskLassoCV(fit_intercept=True, n_alphas=3)
models_single_task = (enet, lasso)
models_multi_task = (m_enet, m_lasso)
rng = np.random.RandomState(0)
X_train = rng.random_sample(size=(10, 3))
X_test = rng.random_sample(size=(10, 3))
y1 = np.empty(10)
y2 = np.empty((10, 2))
for model in models_single_task:
for y_values in (0, 5):
y1.fill(y_values)
assert_array_equal(model.fit(X_train, y1).predict(X_test), y1)
assert_array_equal(model.alphas_, [np.finfo(float).resolution]*3)
for model in models_multi_task:
for y_values in (0, 5):
y2[:, 0].fill(y_values)
y2[:, 1].fill(2 * y_values)
assert_array_equal(model.fit(X_train, y2).predict(X_test), y2)
assert_array_equal(model.alphas_, [np.finfo(float).resolution]*3)
def test_multi_task_lasso_and_enet():
X, y, X_test, y_test = build_dataset()
Y = np.c_[y, y]
# Y_test = np.c_[y_test, y_test]
clf = MultiTaskLasso(alpha=1, tol=1e-8).fit(X, Y)
assert_true(0 < clf.dual_gap_ < 1e-5)
assert_array_almost_equal(clf.coef_[0], clf.coef_[1])
clf = MultiTaskElasticNet(alpha=1, tol=1e-8).fit(X, Y)
assert_true(0 < clf.dual_gap_ < 1e-5)
assert_array_almost_equal(clf.coef_[0], clf.coef_[1])
def test_lasso_readonly_data():
X = np.array([[-1], [0], [1]])
Y = np.array([-1, 0, 1]) # just a straight line
T = np.array([[2], [3], [4]]) # test sample
with TempMemmap((X, Y)) as (X, Y):
clf = Lasso(alpha=0.5)
clf.fit(X, Y)
pred = clf.predict(T)
assert_array_almost_equal(clf.coef_, [.25])
assert_array_almost_equal(pred, [0.5, 0.75, 1.])
assert_almost_equal(clf.dual_gap_, 0)
def test_multi_task_lasso_readonly_data():
X, y, X_test, y_test = build_dataset()
Y = np.c_[y, y]
with TempMemmap((X, Y)) as (X, Y):
Y = np.c_[y, y]
clf = MultiTaskLasso(alpha=1, tol=1e-8).fit(X, Y)
assert_true(0 < clf.dual_gap_ < 1e-5)
assert_array_almost_equal(clf.coef_[0], clf.coef_[1])
def test_enet_multitarget():
n_targets = 3
X, y, _, _ = build_dataset(n_samples=10, n_features=8,
n_informative_features=10, n_targets=n_targets)
estimator = ElasticNet(alpha=0.01, fit_intercept=True)
estimator.fit(X, y)
coef, intercept, dual_gap = (estimator.coef_, estimator.intercept_,
estimator.dual_gap_)
for k in range(n_targets):
estimator.fit(X, y[:, k])
assert_array_almost_equal(coef[k, :], estimator.coef_)
assert_array_almost_equal(intercept[k], estimator.intercept_)
assert_array_almost_equal(dual_gap[k], estimator.dual_gap_)
def test_multioutput_enetcv_error():
X = np.random.randn(10, 2)
y = np.random.randn(10, 2)
clf = ElasticNetCV()
assert_raises(ValueError, clf.fit, X, y)
def test_multitask_enet_and_lasso_cv():
X, y, _, _ = build_dataset(n_features=50, n_targets=3)
clf = MultiTaskElasticNetCV().fit(X, y)
assert_almost_equal(clf.alpha_, 0.00556, 3)
clf = MultiTaskLassoCV().fit(X, y)
assert_almost_equal(clf.alpha_, 0.00278, 3)
X, y, _, _ = build_dataset(n_targets=3)
clf = MultiTaskElasticNetCV(n_alphas=10, eps=1e-3, max_iter=100,
l1_ratio=[0.3, 0.5], tol=1e-3)
clf.fit(X, y)
assert_equal(0.5, clf.l1_ratio_)
assert_equal((3, X.shape[1]), clf.coef_.shape)
assert_equal((3, ), clf.intercept_.shape)
assert_equal((2, 10, 3), clf.mse_path_.shape)
assert_equal((2, 10), clf.alphas_.shape)
X, y, _, _ = build_dataset(n_targets=3)
clf = MultiTaskLassoCV(n_alphas=10, eps=1e-3, max_iter=100, tol=1e-3)
clf.fit(X, y)
assert_equal((3, X.shape[1]), clf.coef_.shape)
assert_equal((3, ), clf.intercept_.shape)
assert_equal((10, 3), clf.mse_path_.shape)
assert_equal(10, len(clf.alphas_))
def test_1d_multioutput_enet_and_multitask_enet_cv():
X, y, _, _ = build_dataset(n_features=10)
y = y[:, np.newaxis]
clf = ElasticNetCV(n_alphas=5, eps=2e-3, l1_ratio=[0.5, 0.7])
clf.fit(X, y[:, 0])
clf1 = MultiTaskElasticNetCV(n_alphas=5, eps=2e-3, l1_ratio=[0.5, 0.7])
clf1.fit(X, y)
assert_almost_equal(clf.l1_ratio_, clf1.l1_ratio_)
assert_almost_equal(clf.alpha_, clf1.alpha_)
assert_almost_equal(clf.coef_, clf1.coef_[0])
assert_almost_equal(clf.intercept_, clf1.intercept_[0])
def test_1d_multioutput_lasso_and_multitask_lasso_cv():
X, y, _, _ = build_dataset(n_features=10)
y = y[:, np.newaxis]
clf = LassoCV(n_alphas=5, eps=2e-3)
clf.fit(X, y[:, 0])
clf1 = MultiTaskLassoCV(n_alphas=5, eps=2e-3)
clf1.fit(X, y)
assert_almost_equal(clf.alpha_, clf1.alpha_)
assert_almost_equal(clf.coef_, clf1.coef_[0])
assert_almost_equal(clf.intercept_, clf1.intercept_[0])
def test_sparse_input_dtype_enet_and_lassocv():
X, y, _, _ = build_dataset(n_features=10)
clf = ElasticNetCV(n_alphas=5)
clf.fit(sparse.csr_matrix(X), y)
clf1 = ElasticNetCV(n_alphas=5)
clf1.fit(sparse.csr_matrix(X, dtype=np.float32), y)
assert_almost_equal(clf.alpha_, clf1.alpha_, decimal=6)
assert_almost_equal(clf.coef_, clf1.coef_, decimal=6)
clf = LassoCV(n_alphas=5)
clf.fit(sparse.csr_matrix(X), y)
clf1 = LassoCV(n_alphas=5)
clf1.fit(sparse.csr_matrix(X, dtype=np.float32), y)
assert_almost_equal(clf.alpha_, clf1.alpha_, decimal=6)
assert_almost_equal(clf.coef_, clf1.coef_, decimal=6)
def test_precompute_invalid_argument():
X, y, _, _ = build_dataset()
for clf in [ElasticNetCV(precompute="invalid"),
LassoCV(precompute="invalid")]:
assert_raises(ValueError, clf.fit, X, y)
def test_warm_start_convergence():
X, y, _, _ = build_dataset()
model = ElasticNet(alpha=1e-3, tol=1e-3).fit(X, y)
n_iter_reference = model.n_iter_
# This dataset is not trivial enough for the model to converge in one pass.
assert_greater(n_iter_reference, 2)
# Check that n_iter_ is invariant to multiple calls to fit
# when warm_start=False, all else being equal.
model.fit(X, y)
n_iter_cold_start = model.n_iter_
assert_equal(n_iter_cold_start, n_iter_reference)
# Fit the same model again, using a warm start: the optimizer just performs
# a single pass before checking that it has already converged
model.set_params(warm_start=True)
model.fit(X, y)
n_iter_warm_start = model.n_iter_
assert_equal(n_iter_warm_start, 1)
def test_warm_start_convergence_with_regularizer_decrement():
boston = load_boston()
X, y = boston.data, boston.target
# Train a model to converge on a lightly regularized problem
final_alpha = 1e-5
low_reg_model = ElasticNet(alpha=final_alpha).fit(X, y)
# Fitting a new model on a more regularized version of the same problem.
# Fitting with high regularization is easier it should converge faster
# in general.
high_reg_model = ElasticNet(alpha=final_alpha * 10).fit(X, y)
assert_greater(low_reg_model.n_iter_, high_reg_model.n_iter_)
# Fit the solution to the original, less regularized version of the
# problem but from the solution of the highly regularized variant of
# the problem as a better starting point. This should also converge
# faster than the original model that starts from zero.
warm_low_reg_model = deepcopy(high_reg_model)
warm_low_reg_model.set_params(warm_start=True, alpha=final_alpha)
warm_low_reg_model.fit(X, y)
assert_greater(low_reg_model.n_iter_, warm_low_reg_model.n_iter_)
def test_random_descent():
# Test that both random and cyclic selection give the same results.
# Ensure that the test models fully converge and check a wide
# range of conditions.
# This uses the coordinate descent algo using the gram trick.
X, y, _, _ = build_dataset(n_samples=50, n_features=20)
clf_cyclic = ElasticNet(selection='cyclic', tol=1e-8)
clf_cyclic.fit(X, y)
clf_random = ElasticNet(selection='random', tol=1e-8, random_state=42)
clf_random.fit(X, y)
assert_array_almost_equal(clf_cyclic.coef_, clf_random.coef_)
assert_almost_equal(clf_cyclic.intercept_, clf_random.intercept_)
# This uses the descent algo without the gram trick
clf_cyclic = ElasticNet(selection='cyclic', tol=1e-8)
clf_cyclic.fit(X.T, y[:20])
clf_random = ElasticNet(selection='random', tol=1e-8, random_state=42)
clf_random.fit(X.T, y[:20])
assert_array_almost_equal(clf_cyclic.coef_, clf_random.coef_)
assert_almost_equal(clf_cyclic.intercept_, clf_random.intercept_)
# Sparse Case
clf_cyclic = ElasticNet(selection='cyclic', tol=1e-8)
clf_cyclic.fit(sparse.csr_matrix(X), y)
clf_random = ElasticNet(selection='random', tol=1e-8, random_state=42)
clf_random.fit(sparse.csr_matrix(X), y)
assert_array_almost_equal(clf_cyclic.coef_, clf_random.coef_)
assert_almost_equal(clf_cyclic.intercept_, clf_random.intercept_)
# Multioutput case.
new_y = np.hstack((y[:, np.newaxis], y[:, np.newaxis]))
clf_cyclic = MultiTaskElasticNet(selection='cyclic', tol=1e-8)
clf_cyclic.fit(X, new_y)
clf_random = MultiTaskElasticNet(selection='random', tol=1e-8,
random_state=42)
clf_random.fit(X, new_y)
assert_array_almost_equal(clf_cyclic.coef_, clf_random.coef_)
assert_almost_equal(clf_cyclic.intercept_, clf_random.intercept_)
# Raise error when selection is not in cyclic or random.
clf_random = ElasticNet(selection='invalid')
assert_raises(ValueError, clf_random.fit, X, y)
def test_enet_path_positive():
# Test that the coefs returned by positive=True in enet_path are positive
X, y, _, _ = build_dataset(n_samples=50, n_features=50)
for path in [enet_path, lasso_path]:
pos_path_coef = path(X, y, positive=True)[1]
assert_true(np.all(pos_path_coef >= 0))
def test_sparse_dense_descent_paths():
# Test that dense and sparse input give the same input for descent paths.
X, y, _, _ = build_dataset(n_samples=50, n_features=20)
csr = sparse.csr_matrix(X)
for path in [enet_path, lasso_path]:
_, coefs, _ = path(X, y, fit_intercept=False)
_, sparse_coefs, _ = path(csr, y, fit_intercept=False)
assert_array_almost_equal(coefs, sparse_coefs)
def test_check_input_false():
X, y, _, _ = build_dataset(n_samples=20, n_features=10)
X = check_array(X, order='F', dtype='float64')
y = check_array(X, order='F', dtype='float64')
clf = ElasticNet(selection='cyclic', tol=1e-8)
# Check that no error is raised if data is provided in the right format
clf.fit(X, y, check_input=False)
X = check_array(X, order='F', dtype='float32')
clf.fit(X, y, check_input=True)
# Check that an error is raised if data is provided in the wrong dtype,
# because of check bypassing
assert_raises(ValueError, clf.fit, X, y, check_input=False)
# With no input checking, providing X in C order should result in false
# computation
X = check_array(X, order='C', dtype='float64')
assert_raises(ValueError, clf.fit, X, y, check_input=False)
def test_overrided_gram_matrix():
X, y, _, _ = build_dataset(n_samples=20, n_features=10)
Gram = X.T.dot(X)
clf = ElasticNet(selection='cyclic', tol=1e-8, precompute=Gram,
fit_intercept=True)
assert_warns_message(UserWarning,
"Gram matrix was provided but X was centered"
" to fit intercept, "
"or X was normalized : recomputing Gram matrix.",
clf.fit, X, y)
def test_lasso_non_float_y():
X = [[0, 0], [1, 1], [-1, -1]]
y = [0, 1, 2]
y_float = [0.0, 1.0, 2.0]
for model in [ElasticNet, Lasso]:
clf = model(fit_intercept=False)
clf.fit(X, y)
clf_float = model(fit_intercept=False)
clf_float.fit(X, y_float)
assert_array_equal(clf.coef_, clf_float.coef_)
| bsd-3-clause |
zasdfgbnm/tensorflow | tensorflow/contrib/factorization/python/ops/kmeans.py | 5 | 17810 | # Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""A canned Estimator for k-means clustering."""
# TODO(ccolby): Move clustering_ops.py into this file and streamline the code.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import time
from tensorflow.contrib.factorization.python.ops import clustering_ops
from tensorflow.python.estimator import estimator
from tensorflow.python.estimator import model_fn as model_fn_lib
from tensorflow.python.estimator.export import export_output
from tensorflow.python.framework import ops
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import control_flow_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import metrics
from tensorflow.python.ops import state_ops
from tensorflow.python.platform import tf_logging as logging
from tensorflow.python.saved_model import signature_constants
from tensorflow.python.summary import summary
from tensorflow.python.training import session_run_hook
from tensorflow.python.training import training_util
class _LossRelativeChangeHook(session_run_hook.SessionRunHook):
"""Stops when the change in loss goes below a tolerance."""
def __init__(self, loss_tensor, tolerance):
"""Creates a _LossRelativeChangeHook.
Args:
loss_tensor: A scalar tensor of the loss value.
tolerance: A relative tolerance of loss change between iterations.
"""
self._loss_tensor = loss_tensor
self._tolerance = tolerance
self._prev_loss = None
def before_run(self, run_context):
del run_context # unused
return session_run_hook.SessionRunArgs(self._loss_tensor)
def after_run(self, run_context, run_values):
loss = run_values.results
assert loss is not None
if self._prev_loss:
relative_change = (abs(loss - self._prev_loss) /
(1 + abs(self._prev_loss)))
if relative_change < self._tolerance:
run_context.request_stop()
self._prev_loss = loss
class _InitializeClustersHook(session_run_hook.SessionRunHook):
"""Initializes the cluster centers.
The chief repeatedly invokes an initialization op until all cluster centers
are initialized. The workers wait for the initialization phase to complete.
"""
def __init__(self, init_op, is_initialized_var, is_chief):
"""Creates an _InitializeClustersHook.
Args:
init_op: An op that, when run, will choose some initial cluster centers.
This op may need to be run multiple times to choose all the centers.
is_initialized_var: A boolean variable reporting whether all initial
centers have been chosen.
is_chief: A boolean specifying whether this task is the chief.
"""
self._init_op = init_op
self._is_initialized_var = is_initialized_var
self._is_chief = is_chief
def after_create_session(self, session, coord):
del coord # unused
assert self._init_op.graph is ops.get_default_graph()
assert self._is_initialized_var.graph is self._init_op.graph
while True:
try:
if session.run(self._is_initialized_var):
break
elif self._is_chief:
session.run(self._init_op)
else:
time.sleep(1)
except RuntimeError as e:
logging.info(e)
def _parse_tensor_or_dict(features):
"""Helper function to convert the input points into a usable format.
Args:
features: The input points.
Returns:
If `features` is a dict of `k` features, each of which is a vector of `n`
scalars, the return value is a Tensor of shape `(n, k)` representing `n`
input points, where the items in the `k` dimension are sorted
lexicographically by `features` key. If `features` is not a dict, it is
returned unmodified.
"""
if isinstance(features, dict):
keys = sorted(features.keys())
with ops.colocate_with(features[keys[0]]):
features = array_ops.concat([features[k] for k in keys], axis=1)
return features
class _ModelFn(object):
"""Model function for the estimator."""
def __init__(self, num_clusters, initial_clusters, distance_metric,
random_seed, use_mini_batch, mini_batch_steps_per_iteration,
kmeans_plus_plus_num_retries, relative_tolerance):
self._num_clusters = num_clusters
self._initial_clusters = initial_clusters
self._distance_metric = distance_metric
self._random_seed = random_seed
self._use_mini_batch = use_mini_batch
self._mini_batch_steps_per_iteration = mini_batch_steps_per_iteration
self._kmeans_plus_plus_num_retries = kmeans_plus_plus_num_retries
self._relative_tolerance = relative_tolerance
def model_fn(self, features, mode, config):
"""Model function for the estimator.
Note that this does not take a `labels` arg. This works, but `input_fn` must
return either `features` or, equivalently, `(features, None)`.
Args:
features: The input points. See @{tf.estimator.Estimator}.
mode: See @{tf.estimator.Estimator}.
config: See @{tf.estimator.Estimator}.
Returns:
A @{tf.estimator.EstimatorSpec} (see @{tf.estimator.Estimator}) specifying
this behavior:
* `train_op`: Execute one mini-batch or full-batch run of Lloyd's
algorithm.
* `loss`: The sum of the squared distances from each input point to its
closest center.
* `eval_metric_ops`: Maps `SCORE` to `loss`.
* `predictions`: Maps `ALL_DISTANCES` to the distance from each input
point to each cluster center; maps `CLUSTER_INDEX` to the index of
the closest cluster center for each input point.
"""
# input_points is a single Tensor. Therefore, the sharding functionality
# in clustering_ops is unused, and some of the values below are lists of a
# single item.
input_points = _parse_tensor_or_dict(features)
# Let N = the number of input_points.
# all_distances: A list of one matrix of shape (N, num_clusters). Each value
# is the distance from an input point to a cluster center.
# model_predictions: A list of one vector of shape (N). Each value is the
# cluster id of an input point.
# losses: Similar to cluster_idx but provides the distance to the cluster
# center.
# is_initialized: scalar indicating whether the initial cluster centers
# have been chosen; see init_op.
# cluster_centers_var: a Variable containing the cluster centers.
# init_op: an op to choose the initial cluster centers. A single worker
# repeatedly executes init_op until is_initialized becomes True.
# training_op: an op that runs an iteration of training, either an entire
# Lloyd iteration or a mini-batch of a Lloyd iteration. Multiple workers
# may execute this op, but only after is_initialized becomes True.
(all_distances, model_predictions, losses, is_initialized, init_op,
training_op) = clustering_ops.KMeans(
inputs=input_points,
num_clusters=self._num_clusters,
initial_clusters=self._initial_clusters,
distance_metric=self._distance_metric,
use_mini_batch=self._use_mini_batch,
mini_batch_steps_per_iteration=self._mini_batch_steps_per_iteration,
random_seed=self._random_seed,
kmeans_plus_plus_num_retries=self._kmeans_plus_plus_num_retries
).training_graph()
loss = math_ops.reduce_sum(losses)
summary.scalar('loss/raw', loss)
incr_step = state_ops.assign_add(training_util.get_global_step(), 1)
training_op = control_flow_ops.with_dependencies([training_op, incr_step],
loss)
training_hooks = [
_InitializeClustersHook(init_op, is_initialized, config.is_chief)
]
if self._relative_tolerance is not None:
training_hooks.append(
_LossRelativeChangeHook(loss, self._relative_tolerance))
export_outputs = {
KMeansClustering.ALL_DISTANCES:
export_output.PredictOutput(all_distances[0]),
KMeansClustering.CLUSTER_INDEX:
export_output.PredictOutput(model_predictions[0]),
signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY:
export_output.PredictOutput(model_predictions[0])
}
return model_fn_lib.EstimatorSpec(
mode=mode,
predictions={
KMeansClustering.ALL_DISTANCES: all_distances[0],
KMeansClustering.CLUSTER_INDEX: model_predictions[0],
},
loss=loss,
train_op=training_op,
eval_metric_ops={KMeansClustering.SCORE: metrics.mean(loss)},
training_hooks=training_hooks,
export_outputs=export_outputs)
# TODO(agarwal,ands): support sharded input.
class KMeansClustering(estimator.Estimator):
"""An Estimator for K-Means clustering."""
# Valid values for the distance_metric constructor argument.
SQUARED_EUCLIDEAN_DISTANCE = clustering_ops.SQUARED_EUCLIDEAN_DISTANCE
COSINE_DISTANCE = clustering_ops.COSINE_DISTANCE
# Values for initial_clusters constructor argument.
RANDOM_INIT = clustering_ops.RANDOM_INIT
KMEANS_PLUS_PLUS_INIT = clustering_ops.KMEANS_PLUS_PLUS_INIT
# Metric returned by evaluate(): The sum of the squared distances from each
# input point to its closest center.
SCORE = 'score'
# Keys returned by predict().
# ALL_DISTANCES: The distance from each input point to each cluster center.
# CLUSTER_INDEX: The index of the closest cluster center for each input point.
CLUSTER_INDEX = 'cluster_index'
ALL_DISTANCES = 'all_distances'
def __init__(self,
num_clusters,
model_dir=None,
initial_clusters=RANDOM_INIT,
distance_metric=SQUARED_EUCLIDEAN_DISTANCE,
random_seed=0,
use_mini_batch=True,
mini_batch_steps_per_iteration=1,
kmeans_plus_plus_num_retries=2,
relative_tolerance=None,
config=None):
"""Creates an Estimator for running KMeans training and inference.
This Estimator implements the following variants of the K-means algorithm:
If `use_mini_batch` is False, it runs standard full batch K-means. Each
training step runs a single iteration of K-Means and must process the full
input at once. To run in this mode, the `input_fn` passed to `train` must
return the entire input dataset.
If `use_mini_batch` is True, it runs a generalization of the mini-batch
K-means algorithm. It runs multiple iterations, where each iteration is
composed of `mini_batch_steps_per_iteration` steps. Each training step
accumulates the contribution from one mini-batch into temporary storage.
Every `mini_batch_steps_per_iteration` steps, the cluster centers are
updated and the temporary storage cleared for the next iteration. Note
that:
* If `mini_batch_steps_per_iteration=1`, the algorithm reduces to the
standard K-means mini-batch algorithm.
* If `mini_batch_steps_per_iteration = num_inputs / batch_size`, the
algorithm becomes an asynchronous version of the full-batch algorithm.
However, there is no guarantee by this implementation that each input
is seen exactly once per iteration. Also, different updates are applied
asynchronously without locking. So this asynchronous version may not
behave exactly like a full-batch version.
Args:
num_clusters: An integer tensor specifying the number of clusters. This
argument is ignored if `initial_clusters` is a tensor or numpy array.
model_dir: The directory to save the model results and log files.
initial_clusters: Specifies how the initial cluster centers are chosen.
One of the following:
* a tensor or numpy array with the initial cluster centers.
* a callable `f(inputs, k)` that selects and returns up to `k` centers
from an input batch. `f` is free to return any number of centers
from `0` to `k`. It will be invoked on successive input batches
as necessary until all `num_clusters` centers are chosen.
* `KMeansClustering.RANDOM_INIT`: Choose centers randomly from an input
batch. If the batch size is less than `num_clusters` then the
entire batch is chosen to be initial cluster centers and the
remaining centers are chosen from successive input batches.
* `KMeansClustering.KMEANS_PLUS_PLUS_INIT`: Use kmeans++ to choose
centers from the first input batch. If the batch size is less
than `num_clusters`, a TensorFlow runtime error occurs.
distance_metric: The distance metric used for clustering. One of:
* `KMeansClustering.SQUARED_EUCLIDEAN_DISTANCE`: Euclidean distance
between vectors `u` and `v` is defined as `||u - v||_2` which is
the square root of the sum of the absolute squares of the elements'
difference.
* `KMeansClustering.COSINE_DISTANCE`: Cosine distance between vectors
`u` and `v` is defined as `1 - (u . v) / (||u||_2 ||v||_2)`.
random_seed: Python integer. Seed for PRNG used to initialize centers.
use_mini_batch: A boolean specifying whether to use the mini-batch k-means
algorithm. See explanation above.
mini_batch_steps_per_iteration: The number of steps after which the
updated cluster centers are synced back to a master copy. Used only if
`use_mini_batch=True`. See explanation above.
kmeans_plus_plus_num_retries: For each point that is sampled during
kmeans++ initialization, this parameter specifies the number of
additional points to draw from the current distribution before selecting
the best. If a negative value is specified, a heuristic is used to
sample `O(log(num_to_sample))` additional points. Used only if
`initial_clusters=KMeansClustering.KMEANS_PLUS_PLUS_INIT`.
relative_tolerance: A relative tolerance of change in the loss between
iterations. Stops learning if the loss changes less than this amount.
This may not work correctly if `use_mini_batch=True`.
config: See @{tf.estimator.Estimator}.
Raises:
ValueError: An invalid argument was passed to `initial_clusters` or
`distance_metric`.
"""
if isinstance(initial_clusters, str) and initial_clusters not in [
KMeansClustering.RANDOM_INIT, KMeansClustering.KMEANS_PLUS_PLUS_INIT
]:
raise ValueError(
"Unsupported initialization algorithm '%s'" % initial_clusters)
if distance_metric not in [
KMeansClustering.SQUARED_EUCLIDEAN_DISTANCE,
KMeansClustering.COSINE_DISTANCE
]:
raise ValueError("Unsupported distance metric '%s'" % distance_metric)
super(KMeansClustering, self).__init__(
model_fn=_ModelFn(
num_clusters, initial_clusters, distance_metric, random_seed,
use_mini_batch, mini_batch_steps_per_iteration,
kmeans_plus_plus_num_retries, relative_tolerance).model_fn,
model_dir=model_dir,
config=config)
def _predict_one_key(self, input_fn, predict_key):
for result in self.predict(input_fn=input_fn, predict_keys=[predict_key]):
yield result[predict_key]
def predict_cluster_index(self, input_fn):
"""Finds the index of the closest cluster center to each input point.
Args:
input_fn: Input points. See @{tf.estimator.Estimator.predict}.
Yields:
The index of the closest cluster center for each input point.
"""
for index in self._predict_one_key(input_fn,
KMeansClustering.CLUSTER_INDEX):
yield index
def score(self, input_fn):
"""Returns the sum of squared distances to nearest clusters.
Note that this function is different from the corresponding one in sklearn
which returns the negative sum.
Args:
input_fn: Input points. See @{tf.estimator.Estimator.evaluate}. Only one
batch is retrieved.
Returns:
The sum of the squared distance from each point in the first batch of
inputs to its nearest cluster center.
"""
return self.evaluate(input_fn=input_fn, steps=1)[KMeansClustering.SCORE]
def transform(self, input_fn):
"""Transforms each input point to its distances to all cluster centers.
Note that if `distance_metric=KMeansClustering.SQUARED_EUCLIDEAN_DISTANCE`,
this
function returns the squared Euclidean distance while the corresponding
sklearn function returns the Euclidean distance.
Args:
input_fn: Input points. See @{tf.estimator.Estimator.predict}.
Yields:
The distances from each input point to each cluster center.
"""
for distances in self._predict_one_key(input_fn,
KMeansClustering.ALL_DISTANCES):
yield distances
def cluster_centers(self):
"""Returns the cluster centers."""
return self.get_variable_value(clustering_ops.CLUSTERS_VAR_NAME)
| apache-2.0 |
thorwhalen/ut | ppi/naive_bayes_graph.py | 1 | 8556 | __author__ = 'thor'
import copy
import re
# import pandas as pd
# import numpy as np
# import ut as ut
#
# import ut.daf.get
class BipartiteStats(object):
"""
The class that manages the count data.
"""
# _count
# a
# b
# ab
# ba
def __init__(self, get_a_list_from_item=None, get_b_list_from_item=None):
self._count = CountVal(0.0)
self.a = KeyVal()
self.b = KeyVal()
self.ab = KeyVal()
self.ba = KeyVal()
self.get_a_list_from_item = get_a_list_from_item or get_a_list_from_item_default
self.get_b_list_from_item = get_b_list_from_item or get_b_list_from_item_default
def count_data(self, item_iterator, get_a_list_from_item=None, get_b_list_from_item=None):
self.__init__(get_a_list_from_item=get_a_list_from_item,
get_b_list_from_item=get_b_list_from_item)
for item in item_iterator:
self._count.increment()
a_list = self.get_a_list_from_item(item)
b_list = self.get_b_list_from_item(item)
for a in a_list:
self.a.add(KeyVal({a: Val(1.0)}))
for b in b_list:
self.b.add(KeyVal({b: Val(1.0)}))
for a in a_list:
for b in b_list:
self.ab.add(KeyVal({a: KeyVal({b: Val(1.0)})}))
self.ba.add(KeyVal({b: KeyVal({a: Val(1.0)})}))
# def normalize(self, alpha=1, beta=1):
# prior_num = Val(float(alpha))
# prior_denom = Val(float(alpha + beta))
# self.ab = (self.ab + prior_num) / (self.b + prior_denom)
# self.ba = (self.ab + prior_num) / (self.a + prior_denom)
# self.a = (self.a + prior_num) / (self._count + prior_denom)
# self.b = (self.b + prior_num) / (self._count + prior_denom)
# default functions
def get_a_list_from_item_default(pair_set):
return pair_set[0]
def get_b_list_from_item_default(pair_set):
return pair_set[1]
class BipartiteEdgeCounts(object):
"""
The class that manages the count data.
"""
# _count
# a_count
# b_count
# ab_count
# ba_count
def __init__(self, get_a_list_from_item=None, get_b_list_from_item=None):
self._count = CountVal(0.0)
self.a_count = KeyCount()
self.b_count = KeyCount()
self.ab_count = KeyCount()
self.ba_count = KeyCount()
self.get_a_list_from_item = get_a_list_from_item or get_a_list_from_item_default
self.get_b_list_from_item = get_b_list_from_item or get_b_list_from_item_default
def learn(self, item_iterator):
self.__init__()
for item in item_iterator:
self._count.increment()
a_list = self.get_a_list_from_item(item)
b_list = self.get_b_list_from_item(item)
for a in a_list:
self.a_count.increment(a)
for b in b_list:
self.b_count.increment(b)
for a in a_list:
for b in b_list:
self.ab_count.add(KeyVal({a: KeyVal({b: Val(1.0)})}))
self.ba_count.add(KeyVal({b: KeyVal({a: Val(1.0)})}))
class Val(object):
"""
The mother class of other Val classes.
A Val should hold a value and be able to add and subtract from it.
This mother class implements normal addition of floats, but should be overridden to
implement other types of values such as multiplication, addition of vectors,
merging of likelihoods etc.
Most of the time, you'll only need to override the add() and the sub() methods.
You may also want to override the default value. This value should act as the
'unit' or 'neutral' value of the add operation (therefore the sub operation as well).
For example, the unit value of multiplication (which will still be called "add") is 1.0.
"""
v = 0.0
def __init__(self, v):
if isinstance(v, Val):
self.v = copy.deepcopy(v.v)
else:
self.v = copy.deepcopy(v)
def add(self, y):
self.v = self.v + y.v
def sub(self, y):
self.v = self.v - y.v
def mul(self, y):
self.v = self.v * y.v
def div(self, y):
self.v = self.v / y.v
def unwrapped(self):
if hasattr(self.v, 'v'):
return self.v.unwrapped()
else:
return self.v
def __add__(self, y):
x = copy.deepcopy(self)
x.add(y)
return x
def __sub__(self, y):
x = copy.deepcopy(self)
x.sub(y)
return x
def __mul__(self, y):
x = copy.deepcopy(self)
x.mul(y)
return x
def __div__(self, y):
x = copy.deepcopy(self)
x.div(y)
return x
def __str__(self):
return str(self.v)
def __repr__(self):
return str(self.v)
class CountVal(Val):
v = 0.0
def __init__(self, v=0.0):
super(CountVal, self).__init__(v)
self.v = float(v)
def increment(self):
self.v += 1.0
class LHVal(Val):
"""
An LHVal manages a binary likelihood.
That is, it holds (as a single float) the binary likelihood distribution and allows one to
merge two such distributions.
"""
v = .5; # where the value will be stored
def __init__(self, v=.5):
super(LHVal, self).__init__(v)
self.v = float(v)
def add(self, y):
self.v = (self.v * y.v) / (self.v * y.v + (1 - self.v) * (1 - y.v))
def sub(self, y):
self.v = (self.v / y.v) / (self.v / y.v + (1 - self.v) / (1 - y.v))
class KeyVal(Val):
"""
Here the type of the value is a dict (to implement a map).
The addition of two dicts (therefore the add() method) v and w.
The add(val) method will here be defined to be a sum-update of the (key,value)
pairs of the
Extends a map so that one can add and subtract dict pairs by adding or subtracting
the (key-aligned) values
"""
def __init__(self, v=None):
if v is None:
self.v = dict()
else:
super(KeyVal, self).__init__(v)
def add(self, kv):
if hasattr(kv.v, 'keys'):
for k in list(kv.v.keys()):
if k in list(self.v.keys()):
self.v[k].add(kv.v[k])
else:
self.v[k] = kv.v[k]
else:
for k in list(self.v.keys()):
self.v[k].v = self.v[k].v + kv.v
def sub(self, kv):
if hasattr(kv.v, 'keys'):
for k in list(kv.v.keys()):
if k in list(self.v.keys()):
self.v[k].sub(kv.v[k])
else:
for k in list(self.v.keys()):
self.v[k].v = self.v[k].v - kv.v
def mul(self, kv):
if hasattr(kv.v, 'keys'):
for k in list(kv.v.keys()):
if k in list(self.v.keys()):
self.v[k].mul(kv.v[k])
else:
self.v[k] = kv.v[k]
else:
for k in list(self.v.keys()):
self.v[k].v = self.v[k].v * kv.v
def div(self, kv):
if hasattr(kv.v, 'keys'):
for k in list(kv.v.keys()):
if k in list(self.v.keys()):
self.v[k].div(kv.v[k])
else:
for k in list(self.v.keys()):
self.v[k].v = self.v[k].v / kv.v
def unwrapped(self):
return {k: v.unwrapped() for k, v in self.v.items()}
# d = dict()
# for k in self.v.keys():
# this_v = self.v[k]
# # print hasattr(this_v, 'v')
# # d.update({k: this_v.unwrapped()})
# if hasattr(this_v, 'v'):
# # print 'oh!'
# d.update({k: this_v.unwrapped()})
# else:
# # print 'ah?'
# d.update({k: this_v})
# return d
class KeyCount(KeyVal):
# v = dict()
# init_val_constructor = None;
"""
Extends a map so that one can add and subtract dict pairs by adding or subtracting the (key-aligned) values
"""
def __init__(self, v=None):
if v is None:
self.v = dict()
else:
super(KeyCount, self).__init__(v)
def increment(self, k):
if k in self.v:
self.v[k].add(Val(1.0))
else:
self.v[k] = Val(1.0)
# if __name__ == "__main__":
# d = ut.daf.get.rand(nrows=9)
# s = d['A'].iloc[0:5]
# ss = d['B'].iloc[3:8]
# t = s + ss
# print t
| mit |
shikhardb/scikit-learn | sklearn/datasets/tests/test_samples_generator.py | 67 | 14842 | from __future__ import division
from collections import defaultdict
from functools import partial
import numpy as np
from sklearn.externals.six.moves import zip
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_true
from sklearn.utils.testing import assert_less
from sklearn.utils.testing import assert_raises
from sklearn.utils.testing import assert_warns
from sklearn.datasets import make_classification
from sklearn.datasets import make_multilabel_classification
from sklearn.datasets import make_hastie_10_2
from sklearn.datasets import make_regression
from sklearn.datasets import make_blobs
from sklearn.datasets import make_friedman1
from sklearn.datasets import make_friedman2
from sklearn.datasets import make_friedman3
from sklearn.datasets import make_low_rank_matrix
from sklearn.datasets import make_sparse_coded_signal
from sklearn.datasets import make_sparse_uncorrelated
from sklearn.datasets import make_spd_matrix
from sklearn.datasets import make_swiss_roll
from sklearn.datasets import make_s_curve
from sklearn.datasets import make_biclusters
from sklearn.datasets import make_checkerboard
from sklearn.utils.validation import assert_all_finite
def test_make_classification():
X, y = make_classification(n_samples=100, n_features=20, n_informative=5,
n_redundant=1, n_repeated=1, n_classes=3,
n_clusters_per_class=1, hypercube=False,
shift=None, scale=None, weights=[0.1, 0.25],
random_state=0)
assert_equal(X.shape, (100, 20), "X shape mismatch")
assert_equal(y.shape, (100,), "y shape mismatch")
assert_equal(np.unique(y).shape, (3,), "Unexpected number of classes")
assert_equal(sum(y == 0), 10, "Unexpected number of samples in class #0")
assert_equal(sum(y == 1), 25, "Unexpected number of samples in class #1")
assert_equal(sum(y == 2), 65, "Unexpected number of samples in class #2")
def test_make_classification_informative_features():
"""Test the construction of informative features in make_classification
Also tests `n_clusters_per_class`, `n_classes`, `hypercube` and
fully-specified `weights`.
"""
# Create very separate clusters; check that vertices are unique and
# correspond to classes
class_sep = 1e6
make = partial(make_classification, class_sep=class_sep, n_redundant=0,
n_repeated=0, flip_y=0, shift=0, scale=1, shuffle=False)
for n_informative, weights, n_clusters_per_class in [(2, [1], 1),
(2, [1/3] * 3, 1),
(2, [1/4] * 4, 1),
(2, [1/2] * 2, 2),
(2, [3/4, 1/4], 2),
(10, [1/3] * 3, 10)
]:
n_classes = len(weights)
n_clusters = n_classes * n_clusters_per_class
n_samples = n_clusters * 50
for hypercube in (False, True):
X, y = make(n_samples=n_samples, n_classes=n_classes,
weights=weights, n_features=n_informative,
n_informative=n_informative,
n_clusters_per_class=n_clusters_per_class,
hypercube=hypercube, random_state=0)
assert_equal(X.shape, (n_samples, n_informative))
assert_equal(y.shape, (n_samples,))
# Cluster by sign, viewed as strings to allow uniquing
signs = np.sign(X)
signs = signs.view(dtype='|S{0}'.format(signs.strides[0]))
unique_signs, cluster_index = np.unique(signs,
return_inverse=True)
assert_equal(len(unique_signs), n_clusters,
"Wrong number of clusters, or not in distinct "
"quadrants")
clusters_by_class = defaultdict(set)
for cluster, cls in zip(cluster_index, y):
clusters_by_class[cls].add(cluster)
for clusters in clusters_by_class.values():
assert_equal(len(clusters), n_clusters_per_class,
"Wrong number of clusters per class")
assert_equal(len(clusters_by_class), n_classes,
"Wrong number of classes")
assert_array_almost_equal(np.bincount(y) / len(y) // weights,
[1] * n_classes,
err_msg="Wrong number of samples "
"per class")
# Ensure on vertices of hypercube
for cluster in range(len(unique_signs)):
centroid = X[cluster_index == cluster].mean(axis=0)
if hypercube:
assert_array_almost_equal(np.abs(centroid),
[class_sep] * n_informative,
decimal=0,
err_msg="Clusters are not "
"centered on hypercube "
"vertices")
else:
assert_raises(AssertionError,
assert_array_almost_equal,
np.abs(centroid),
[class_sep] * n_informative,
decimal=0,
err_msg="Clusters should not be cenetered "
"on hypercube vertices")
assert_raises(ValueError, make, n_features=2, n_informative=2, n_classes=5,
n_clusters_per_class=1)
assert_raises(ValueError, make, n_features=2, n_informative=2, n_classes=3,
n_clusters_per_class=2)
def test_make_multilabel_classification_return_sequences():
for allow_unlabeled, min_length in zip((True, False), (0, 1)):
X, Y = assert_warns(DeprecationWarning, make_multilabel_classification,
n_samples=100, n_features=20, n_classes=3,
random_state=0, allow_unlabeled=allow_unlabeled)
assert_equal(X.shape, (100, 20), "X shape mismatch")
if not allow_unlabeled:
assert_equal(max([max(y) for y in Y]), 2)
assert_equal(min([len(y) for y in Y]), min_length)
assert_true(max([len(y) for y in Y]) <= 3)
def test_make_multilabel_classification_return_indicator():
for allow_unlabeled, min_length in zip((True, False), (0, 1)):
X, Y = make_multilabel_classification(n_samples=25, n_features=20,
n_classes=3, random_state=0,
return_indicator=True,
allow_unlabeled=allow_unlabeled)
assert_equal(X.shape, (25, 20), "X shape mismatch")
assert_equal(Y.shape, (25, 3), "Y shape mismatch")
assert_true(np.all(np.sum(Y, axis=0) > min_length))
# Also test return_distributions
X2, Y2, p_c, p_w_c = make_multilabel_classification(
n_samples=25, n_features=20, n_classes=3, random_state=0,
return_indicator=True, allow_unlabeled=allow_unlabeled,
return_distributions=True)
assert_array_equal(X, X2)
assert_array_equal(Y, Y2)
assert_equal(p_c.shape, (3,))
assert_almost_equal(p_c.sum(), 1)
assert_equal(p_w_c.shape, (20, 3))
assert_almost_equal(p_w_c.sum(axis=0), [1] * 3)
def test_make_hastie_10_2():
X, y = make_hastie_10_2(n_samples=100, random_state=0)
assert_equal(X.shape, (100, 10), "X shape mismatch")
assert_equal(y.shape, (100,), "y shape mismatch")
assert_equal(np.unique(y).shape, (2,), "Unexpected number of classes")
def test_make_regression():
X, y, c = make_regression(n_samples=100, n_features=10, n_informative=3,
effective_rank=5, coef=True, bias=0.0,
noise=1.0, random_state=0)
assert_equal(X.shape, (100, 10), "X shape mismatch")
assert_equal(y.shape, (100,), "y shape mismatch")
assert_equal(c.shape, (10,), "coef shape mismatch")
assert_equal(sum(c != 0.0), 3, "Unexpected number of informative features")
# Test that y ~= np.dot(X, c) + bias + N(0, 1.0).
assert_almost_equal(np.std(y - np.dot(X, c)), 1.0, decimal=1)
# Test with small number of features.
X, y = make_regression(n_samples=100, n_features=1) # n_informative=3
assert_equal(X.shape, (100, 1))
def test_make_regression_multitarget():
X, y, c = make_regression(n_samples=100, n_features=10, n_informative=3,
n_targets=3, coef=True, noise=1., random_state=0)
assert_equal(X.shape, (100, 10), "X shape mismatch")
assert_equal(y.shape, (100, 3), "y shape mismatch")
assert_equal(c.shape, (10, 3), "coef shape mismatch")
assert_array_equal(sum(c != 0.0), 3,
"Unexpected number of informative features")
# Test that y ~= np.dot(X, c) + bias + N(0, 1.0)
assert_almost_equal(np.std(y - np.dot(X, c)), 1.0, decimal=1)
def test_make_blobs():
X, y = make_blobs(n_samples=50, n_features=2,
centers=[[0.0, 0.0], [1.0, 1.0], [0.0, 1.0]],
random_state=0)
assert_equal(X.shape, (50, 2), "X shape mismatch")
assert_equal(y.shape, (50,), "y shape mismatch")
assert_equal(np.unique(y).shape, (3,), "Unexpected number of blobs")
def test_make_friedman1():
X, y = make_friedman1(n_samples=5, n_features=10, noise=0.0,
random_state=0)
assert_equal(X.shape, (5, 10), "X shape mismatch")
assert_equal(y.shape, (5,), "y shape mismatch")
assert_array_almost_equal(y,
10 * np.sin(np.pi * X[:, 0] * X[:, 1])
+ 20 * (X[:, 2] - 0.5) ** 2
+ 10 * X[:, 3] + 5 * X[:, 4])
def test_make_friedman2():
X, y = make_friedman2(n_samples=5, noise=0.0, random_state=0)
assert_equal(X.shape, (5, 4), "X shape mismatch")
assert_equal(y.shape, (5,), "y shape mismatch")
assert_array_almost_equal(y,
(X[:, 0] ** 2
+ (X[:, 1] * X[:, 2] - 1
/ (X[:, 1] * X[:, 3])) ** 2) ** 0.5)
def test_make_friedman3():
X, y = make_friedman3(n_samples=5, noise=0.0, random_state=0)
assert_equal(X.shape, (5, 4), "X shape mismatch")
assert_equal(y.shape, (5,), "y shape mismatch")
assert_array_almost_equal(y, np.arctan((X[:, 1] * X[:, 2]
- 1 / (X[:, 1] * X[:, 3]))
/ X[:, 0]))
def test_make_low_rank_matrix():
X = make_low_rank_matrix(n_samples=50, n_features=25, effective_rank=5,
tail_strength=0.01, random_state=0)
assert_equal(X.shape, (50, 25), "X shape mismatch")
from numpy.linalg import svd
u, s, v = svd(X)
assert_less(sum(s) - 5, 0.1, "X rank is not approximately 5")
def test_make_sparse_coded_signal():
Y, D, X = make_sparse_coded_signal(n_samples=5, n_components=8,
n_features=10, n_nonzero_coefs=3,
random_state=0)
assert_equal(Y.shape, (10, 5), "Y shape mismatch")
assert_equal(D.shape, (10, 8), "D shape mismatch")
assert_equal(X.shape, (8, 5), "X shape mismatch")
for col in X.T:
assert_equal(len(np.flatnonzero(col)), 3, 'Non-zero coefs mismatch')
assert_array_almost_equal(np.dot(D, X), Y)
assert_array_almost_equal(np.sqrt((D ** 2).sum(axis=0)),
np.ones(D.shape[1]))
def test_make_sparse_uncorrelated():
X, y = make_sparse_uncorrelated(n_samples=5, n_features=10, random_state=0)
assert_equal(X.shape, (5, 10), "X shape mismatch")
assert_equal(y.shape, (5,), "y shape mismatch")
def test_make_spd_matrix():
X = make_spd_matrix(n_dim=5, random_state=0)
assert_equal(X.shape, (5, 5), "X shape mismatch")
assert_array_almost_equal(X, X.T)
from numpy.linalg import eig
eigenvalues, _ = eig(X)
assert_array_equal(eigenvalues > 0, np.array([True] * 5),
"X is not positive-definite")
def test_make_swiss_roll():
X, t = make_swiss_roll(n_samples=5, noise=0.0, random_state=0)
assert_equal(X.shape, (5, 3), "X shape mismatch")
assert_equal(t.shape, (5,), "t shape mismatch")
assert_array_almost_equal(X[:, 0], t * np.cos(t))
assert_array_almost_equal(X[:, 2], t * np.sin(t))
def test_make_s_curve():
X, t = make_s_curve(n_samples=5, noise=0.0, random_state=0)
assert_equal(X.shape, (5, 3), "X shape mismatch")
assert_equal(t.shape, (5,), "t shape mismatch")
assert_array_almost_equal(X[:, 0], np.sin(t))
assert_array_almost_equal(X[:, 2], np.sign(t) * (np.cos(t) - 1))
def test_make_biclusters():
X, rows, cols = make_biclusters(
shape=(100, 100), n_clusters=4, shuffle=True, random_state=0)
assert_equal(X.shape, (100, 100), "X shape mismatch")
assert_equal(rows.shape, (4, 100), "rows shape mismatch")
assert_equal(cols.shape, (4, 100,), "columns shape mismatch")
assert_all_finite(X)
assert_all_finite(rows)
assert_all_finite(cols)
X2, _, _ = make_biclusters(shape=(100, 100), n_clusters=4,
shuffle=True, random_state=0)
assert_array_almost_equal(X, X2)
def test_make_checkerboard():
X, rows, cols = make_checkerboard(
shape=(100, 100), n_clusters=(20, 5),
shuffle=True, random_state=0)
assert_equal(X.shape, (100, 100), "X shape mismatch")
assert_equal(rows.shape, (100, 100), "rows shape mismatch")
assert_equal(cols.shape, (100, 100,), "columns shape mismatch")
X, rows, cols = make_checkerboard(
shape=(100, 100), n_clusters=2, shuffle=True, random_state=0)
assert_all_finite(X)
assert_all_finite(rows)
assert_all_finite(cols)
X1, _, _ = make_checkerboard(shape=(100, 100), n_clusters=2,
shuffle=True, random_state=0)
X2, _, _ = make_checkerboard(shape=(100, 100), n_clusters=2,
shuffle=True, random_state=0)
assert_array_equal(X1, X2)
| bsd-3-clause |
paulorauber/rlnn | model/rl_ffnn.py | 1 | 2771 | import numpy as np
from collections import deque
from sklearn.utils import check_random_state
def ffnn_batch_update(env, network, transition_buffer, gamma, batch_size,
random_state=None):
random_state = check_random_state(random_state)
# Batch is a random sample of transitions
indices = random_state.choice(len(transition_buffer), size=batch_size)
batch = [transition_buffer[i] for i in indices]
# Setting up targets
X = np.zeros((batch_size, env.d_states))
Y = np.zeros((batch_size, env.n_actions))
mask = np.zeros((batch_size, env.n_actions))
for i, transition in enumerate(batch):
(s, a, next_r, next_s) = transition
X[i] = s
mask[i, a] = 1
if next_s is None:
Y[i, a] = next_r
else:
Y[i, a] = next_r + gamma*np.max(network.predict(next_s))
network.fit_batch(X, Y, mask)
def q_ffnn(env, network, nepisodes, gamma=0.99, min_epsilon=0.1,
max_epsilon=1.0, decay_epsilon=0.99, max_queue=8192,
batch_size=128, verbose=0, random_state=None):
random_state = check_random_state(random_state)
transition_buffer = deque(maxlen=max_queue)
if verbose > 0:
episode_return = 0.0
episode_gamma = 1.0
epsilon = max(min_epsilon, max_epsilon)
for episode in range(nepisodes):
if verbose > 0:
print('Episode {0}.'.format(episode + 1))
s = env.start()
if verbose > 1:
step = 0
print('Step {0}.'.format(step+1))
if verbose > 2:
print(env)
while not env.ended():
if random_state.uniform(0, 1) < epsilon:
a = random_state.choice(env.n_actions)
else:
a = np.argmax(network.predict(s))
next_s, next_r = env.next_state_reward(a)
if not env.ended():
transition_buffer.append((s, a, next_r, next_s))
else:
transition_buffer.append((s, a, next_r, None))
s = next_s
if len(transition_buffer) > batch_size:
ffnn_batch_update(env, network, transition_buffer, gamma,
batch_size, random_state=random_state)
if verbose > 0:
episode_return += episode_gamma*next_r
episode_gamma *= gamma
if verbose > 1:
step += 1
print('Step {0}.'.format(step+1))
if verbose > 2:
print(env)
epsilon = max(min_epsilon, epsilon*decay_epsilon)
if verbose > 0:
print('Return: {0}.'.format(episode_return))
episode_return = 0.0
episode_gamma = 1.0
return network | mit |
escherba/clustering-metrics | clustering_metrics/ranking.py | 1 | 19888 | """
Motivation
----------
Assume that there is a data set of mostly unique samples where a hidden binary
variable is dependent on the number of similar samples that exist in the set
(i.e. a sample is called positive if it has many neighbors) and that our goal
is to label all samples in this set. Given sparse enough data, if a clustering
method relies on the same sample property on which the ground truth similarity
space is defined, it will naturally separate the samples into two groups --
those found in clusters and containing mostly positives, and those found
outside clusters and containing mostly negatives. There would exist only one
possible perfect clustering---one with a single, entirely homogeneous cluster C
that covers all positives present in the data set. If we were to produce such a
clustering, we could correctly label all positive samples in one step with the
simple rule, *all positive samples belong to cluster C*. Under an imperfect
clustering, however, the presence of the given sample in a cluster of size two
or more implies the sample is only somewhat more likely to be positive, with
the confidence of the positive call monotonously increasing with the size of
the cluster. In other words, our expectation from a good clustering is that it
will help us minimize the amount of work labeling samples.
This idea for this metric originated when mining for positive spam examples in
large data sets of short user-generated content. Given large enough data sets,
spam content naturally forms clusters either because creative rewriting of
every single individual spam message is too expensive for spammers to employ,
or because, even if human or algorithmic rewriting is applied, one can still
find features that link individual spam messages to their creator or to the
product or service being promoted in the spam campaign. The finding was
consistent with what is reported in literature [104]_.
Algorithm
---------
Given a clustering, we order the clusters from the largest one to the smallest
one. We then plot a cumulative step function where the width of the bin under a
given "step" is proportional to cluster size, and the height of the bin is
proportional to the expected number of positive samples seen so far [103]_. If a
sample is in a cluster of size one, we assume it is likely to be negative and
is therefore checked on an individual basis (the specific setting of cluster
size at which the expectation changes is our 'threshold' parameter. The result
of this assumption is that the expected contribution from unclustered
samples is equal to their actual contribution (we assume individual checking
always gives a correct answer). After two-way normalization, a perfect
clustering (i.e. where a single perfectly homogeneous cluster covers the entire
set of positives) will have the AUL score of 1.0. A failure to will result in
the AUL of 0.5. A perverse clustering, i.e. one where many negative samples fall
into clusters whose size is above our threshold, or where many positive samples
remain unclustered (fall into clusters of size below the threshold one) the AUL
somewhere between 0.0 and 0.5.
A special treatment is necessary for cases where clusters are tied by size. If
one were to treat tied clusters as a single group, one would obtain AUL of 1.0
when no clusters at all are present, which is against our desiderata. On the
other hand, if one were to treat tied clusters entirely separately, one would
obtain different results depending on the properties of the sorting algorithm,
also an undesirable situation. Always placing "heavy" clusters (i.e. those
containing more positives) towards the beginning or towards the end of the tied
group will result in, respectively, overestimating or underestimating the true
AUL. The solution here is to average the positive counts among all clusters in a
tied group, and then walk through them one by one, with the stepwise cumulative
function asymptotically approaching a diagonal from the group's bottom left
corner to the top right one. This way, a complete absence of clustering (i.e.
all clusters are of size one) will always result in AUL of 0.5.
The resulting AUL measure has some similarity with the Gini coefficient of
inequality [105]_ except we plot the corresponding curve in the opposite
direction (from "richest" to "poorest"), and do not subtract 0.5 from the
resulting score.
.. [103] We take the expected number of positives and not the actual number seen
so far as the vertical scale in order to penalize non-homogeneous
clusters. Otherwise the y=1.0 ceiling would be reached early in the
process even in very bad cases, for example when there is only one giant
non-homogeneous cluster.
References
----------
.. [104] `Whissell, J. S., & Clarke, C. L. (2011, September). Clustering for
semi-supervised spam filtering. In Proceedings of the 8th Annual
Collaboration, Electronic messaging, Anti-Abuse and Spam Conference
(pp. 125-134). ACM.
<https://doi.org/10.1145/2030376.2030391>`_
.. [105] `Wikipedia entry for Gini coefficient of inequality
<https://en.wikipedia.org/wiki/Gini_coefficient>`_
"""
import warnings
import numpy as np
from itertools import izip, chain
from operator import itemgetter
from pymaptools.iter import aggregate_tuples
from pymaptools.containers import labels_to_clusters
from clustering_metrics.skutils import auc, roc_curve
def num2bool(num):
"""True if zero or positive real, False otherwise
When binarizing class labels, this lets us be consistent with Scikit-Learn
where binary labels can be {0, 1} with 0 being negative or {-1, 1} with -1
being negative.
"""
return num > 0
class LiftCurve(object):
"""Lift Curve for cluster-size correlated classification
"""
def __init__(self, score_groups):
self._score_groups = list(score_groups)
@classmethod
def from_counts(cls, counts_true, counts_pred):
"""Instantiates class from arrays of true and predicted counts
Parameters
----------
counts_true : array, shape = [n_clusters]
Count of positives in cluster
counts_pred : array, shape = [n_clusters]
Predicted number of positives in each cluster
"""
# convert input to a series of tuples
count_groups = izip(counts_pred, counts_true)
# sort tuples by predicted count in descending order
count_groups = sorted(count_groups, key=itemgetter(0), reverse=True)
# group tuples by predicted count so as to handle ties correctly
return cls(aggregate_tuples(count_groups))
@classmethod
def from_clusters(cls, clusters, is_class_pos=num2bool):
"""Instantiates class from clusters of class-coded points
Parameters
----------
clusters : collections.Iterable
List of lists of class labels
is_class_pos: label_true -> Bool
Boolean predicate used to binarize true (class) labels
"""
# take all non-empty clusters, score them by size and by number of
# ground truth positives
data = ((len(cluster), sum(is_class_pos(class_label) for class_label in cluster))
for cluster in clusters if cluster)
scores_pred, scores_true = zip(*data) or ([], [])
return cls.from_counts(scores_true, scores_pred)
@classmethod
def from_labels(cls, labels_true, labels_pred, is_class_pos=num2bool):
"""Instantiates class from arrays of classes and cluster sizes
Parameters
----------
labels_true : array, shape = [n_samples]
Class labels. If binary, 'is_class_pos' is optional
labels_pred : array, shape = [n_samples]
Cluster labels to evaluate
is_class_pos: label_true -> Bool
Boolean predicate used to binarize true (class) labels
"""
clusters = labels_to_clusters(labels_true, labels_pred)
return cls.from_clusters(clusters, is_class_pos=is_class_pos)
def aul_score(self, threshold=1, plot=False):
"""Calculate AUL score
Parameters
----------
threshold : int, optional (default=1)
only predicted scores above this number considered accurate
plot : bool, optional (default=False)
whether to return X and Y data series for plotting
"""
total_any = 0
total_true = 0
assumed_vertical = 0
aul = 0.0
if plot:
xs, ys = [], []
bin_height = 0.0
bin_right_edge = 0.0
# second pass: iterate over each group of predicted scores of the same
# size and calculate the AUL metric
for pred_score, true_scores in self._score_groups:
# number of clusters
num_true_scores = len(true_scores)
# sum total of positives in all clusters of given size
group_height = sum(true_scores)
total_true += group_height
# cluster size x number of clusters of given size
group_width = pred_score * num_true_scores
total_any += group_width
if pred_score > threshold:
# penalize non-homogeneous clusters simply by assuming that they
# are homogeneous, in which case their expected vertical
# contribution should be equal to their horizontal contribution.
height_incr = group_width
else:
# clusters of size one are by definition homogeneous so their
# expected vertical contribution equals sum total of any
# remaining true positives.
height_incr = group_height
assumed_vertical += height_incr
if plot:
avg_true_score = group_height / float(num_true_scores)
for _ in true_scores:
bin_height += avg_true_score
aul += bin_height * pred_score
if plot:
xs.append(bin_right_edge)
bin_right_edge += pred_score
xs.append(bin_right_edge)
ys.append(bin_height)
ys.append(bin_height)
else:
# if not tasked with generating plots, use a geometric method
# instead of looping
aul += (total_true * group_width -
((num_true_scores - 1) * pred_score * group_height) / 2.0)
if total_true > total_any:
warnings.warn(
"Number of positives found (%d) exceeds total count of %d"
% (total_true, total_any)
)
rect_area = assumed_vertical * total_any
# special case: since normalizing the AUL defines it as always smaller
# than the bounding rectangle, when denominator in the expression below
# is zero, the AUL score is also equal to zero.
aul = 0.0 if rect_area == 0 else aul / rect_area
if plot:
xs = np.array(xs, dtype=float) / total_any
ys = np.array(ys, dtype=float) / assumed_vertical
return aul, xs, ys
else:
return aul
def plot(self, threshold=1, fill=True, marker=None, save_to=None): # pragma: no cover
"""Create a graphical representation of Lift Curve
Requires Matplotlib
Parameters
----------
threshold : int, optional (default=1)
only predicted scores above this number considered accurate
marker : str, optional (default=None)
Whether to draw marker at each bend
save_to : str, optional (default=None)
If specified, save the plot to path instead of displaying
"""
from matplotlib import pyplot as plt
score, xs, ys = self.aul_score(threshold=threshold, plot=True)
fig, ax = plt.subplots()
ax.plot(xs, ys, marker=marker, linestyle='-')
if fill:
ax.fill([0.0] + list(xs) + [1.0], [0.0] + list(ys) + [0.0], 'b', alpha=0.2)
ax.plot([0.0, 1.0], [0.0, 1.0], linestyle='--', color='grey')
ax.plot([0.0, 1.0], [1.0, 1.0], linestyle='--', color='grey')
ax.plot([1.0, 1.0], [0.0, 1.0], linestyle='--', color='grey')
ax.set_xlim(xmin=0.0, xmax=1.03)
ax.set_ylim(ymin=0.0, ymax=1.04)
ax.set_xlabel("portion total")
ax.set_ylabel("portion expected positive")
ax.set_title("Lift Curve (AUL=%.3f)" % score)
if save_to is None:
fig.show()
else:
fig.savefig(save_to)
plt.close(fig)
def aul_score_from_clusters(clusters):
"""Calculate AUL score given clusters of class-coded points
Parameters
----------
clusters : collections.Iterable
List of clusters where each point is binary-coded according to true
class.
Returns
-------
aul : float
"""
return LiftCurve.from_clusters(clusters).aul_score()
def aul_score_from_labels(y_true, labels_pred):
"""AUL score given array of classes and array of cluster sizes
Parameters
----------
y_true : array, shape = [n_samples]
True binary labels in range {0, 1}
labels_pred : array, shape = [n_samples]
Cluster labels to evaluate
Returns
-------
aul : float
"""
return LiftCurve.from_labels(y_true, labels_pred).aul_score()
class RocCurve(object):
"""Receiver Operating Characteristic (ROC)
::
>>> c = RocCurve.from_labels([0, 0, 1, 1],
... [0.1, 0.4, 0.35, 0.8])
>>> c.auc_score()
0.75
>>> c.max_informedness()
0.5
"""
def __init__(self, fprs, tprs, thresholds=None, pos_label=None,
sample_weight=None):
self.fprs = fprs
self.tprs = tprs
self.thresholds = thresholds
self.pos_label = pos_label
self.sample_weight = sample_weight
def plot(self, fill=True, marker=None, save_to=None): # pragma: no cover
"""Plot the ROC curve
"""
from matplotlib import pyplot as plt
score = self.auc_score()
xs, ys = self.fprs, self.tprs
fig, ax = plt.subplots()
ax.plot(xs, ys, marker=marker, linestyle='-')
if fill:
ax.fill([0.0] + list(xs) + [1.0], [0.0] + list(ys) + [0.0], 'b', alpha=0.2)
ax.plot([0.0, 1.0], [0.0, 1.0], linestyle='--', color='grey')
ax.plot([0.0, 1.0], [1.0, 1.0], linestyle='--', color='grey')
ax.plot([1.0, 1.0], [0.0, 1.0], linestyle='--', color='grey')
ax.set_xlim(xmin=0.0, xmax=1.03)
ax.set_ylim(ymin=0.0, ymax=1.04)
ax.set_ylabel('TPR')
ax.set_xlabel('FPR')
ax.set_title("ROC Curve (AUC=%.3f)" % score)
if save_to is None:
fig.show()
else:
fig.savefig(save_to)
plt.close(fig)
@classmethod
def from_scores(cls, scores_neg, scores_pos):
"""Instantiate given scores of two ground truth classes
The score arrays don't have to be the same length.
"""
scores_pos = ((1, x) for x in scores_pos if not np.isnan(x))
scores_neg = ((0, x) for x in scores_neg if not np.isnan(x))
all_scores = zip(*chain(scores_neg, scores_pos)) or ([], [])
return cls.from_labels(*all_scores)
@classmethod
def from_labels(cls, labels_true, y_score, is_class_pos=num2bool):
"""Instantiate assuming binary labeling of {0, 1}
labels_true : array, shape = [n_samples]
Class labels. If binary, 'is_class_pos' is optional
y_score : array, shape = [n_samples]
Predicted scores
is_class_pos: label_true -> Bool
Boolean predicate used to binarize true (class) labels
"""
# num2bool Y labels
y_true = map(is_class_pos, labels_true)
# calculate axes
fprs, tprs, thresholds = roc_curve(
y_true, y_score, pos_label=True)
return cls(fprs, tprs, thresholds=thresholds)
@classmethod
def from_clusters(cls, clusters, is_class_pos=num2bool):
"""Instantiates class from clusters of class-coded points
Parameters
----------
clusters : collections.Iterable
List of lists of class labels
is_class_pos: label_true -> Bool
Boolean predicate used to binarize true (class) labels
"""
y_true = []
y_score = []
for cluster in clusters:
pred_cluster = len(cluster)
for point in cluster:
true_cluster = is_class_pos(point)
y_true.append(true_cluster)
y_score.append(pred_cluster)
return cls.from_labels(y_true, y_score)
def auc_score(self):
"""Replacement for Scikit-Learn's method
If number of Y classes is other than two, a warning will be triggered
but no exception thrown (the return value will be a NaN). Also, we
don't reorder arrays during ROC calculation since they are assumed to be
in order.
"""
return auc(self.fprs, self.tprs, reorder=False)
def optimal_cutoff(self, scoring_method):
"""Optimal cutoff point on ROC curve under scoring method
The scoring method must take two arguments: fpr and tpr.
"""
max_index = np.NINF
opt_pair = (np.nan, np.nan)
for pair in izip(self.fprs, self.tprs):
index = scoring_method(*pair)
if index > max_index:
opt_pair = pair
max_index = index
return opt_pair, max_index
@staticmethod
def _informedness(fpr, tpr):
return tpr - fpr
def max_informedness(self):
"""Maximum value of Informedness (TPR minus FPR) on a ROC curve
A diagram of what this measure looks like is shown in [101]_. Note a
correspondence between the definitions of this measure and that of
Kolmogorov-Smirnov's supremum statistic.
References
----------
.. [101] `Wikipedia entry for Youden's J statistic
<https://en.wikipedia.org/wiki/Youden%27s_J_statistic>`_
"""
return self.optimal_cutoff(self._informedness)[1]
def roc_auc_score(y_true, y_score, sample_weight=None):
"""AUC score for a ROC curve
Replaces Scikit Learn implementation (given binary ``y_true``).
"""
return RocCurve.from_labels(y_true, y_score).auc_score()
def dist_auc(scores0, scores1):
"""AUC score for two distributions, with NaN correction
Note: arithmetic mean appears to be appropriate here, as other means don't
result in total of 1.0 when sides are switched.
"""
scores0_len = len(scores0)
scores1_len = len(scores1)
scores0p = [x for x in scores0 if not np.isnan(x)]
scores1p = [x for x in scores1 if not np.isnan(x)]
scores0n_len = scores0_len - len(scores0p)
scores1n_len = scores1_len - len(scores1p)
# ``nan_pairs`` are pairs for which it is impossible to define order, due
# to at least one of the members of each being a NaN. ``def_pairs`` are
# pairs for which order can be established.
all_pairs = 2 * scores0_len * scores1_len
nan_pairs = scores0n_len * scores1_len + scores1n_len * scores0_len
def_pairs = all_pairs - nan_pairs
# the final score is the average of the score for the defined portion and
# of random-chance AUC (0.5), weighted according to the number of pairs in
# each group.
auc_score = RocCurve.from_scores(scores0p, scores1p).auc_score()
return np.average([auc_score, 0.5], weights=[def_pairs, nan_pairs])
| bsd-3-clause |
ghislainv/deforestprob | forestatrisk/__init__.py | 1 | 1461 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
# ==============================================================================
# author :Ghislain Vieilledent
# email :[email protected], [email protected]
# web :https://ecology.ghislainv.fr
# python_version :>=2.7
# license :GPLv3
# ==============================================================================
from __future__ import division, print_function # Python 3 compatibility
import os
import matplotlib
if os.environ.get("DISPLAY", "") == "":
print("no display found. Using non-interactive Agg backend")
matplotlib.use("Agg")
import matplotlib.pyplot as plt
import numpy as np
# from data import country
from .accuracy import confmat, accuracy
from .interpolate_rho import interpolate_rho
from .miscellaneous import invlogit, make_dir
from . import plot
from .model_binomial_iCAR import model_binomial_iCAR
from .model_random_forest import model_random_forest
from .sample import sample
from .cellneigh import cellneigh, cellneigh_ctry
from .predict_raster import predict_raster
from .predict_raster_binomial_iCAR import predict_raster_binomial_iCAR
from .resample_sum import resample_sum
from .deforest import deforest
from .validation import accuracy_indices, validation
from .validation_npix import validation_npix
from .emissions import emissions
from .countpix import countpix
from .diffproj import r_diffproj, mat_diffproj
# End
| gpl-3.0 |
chiahaoliu/2016_summer_XPD | XPD_view/plot_analysis.py | 1 | 3819 | import matplotlib.pyplot as plt
from analysis_concurrent import analysis_concurrent
#from file import get_files
import multiprocessing
class reducedRepPlot:
def __init__(self, tif_list, x_start, x_stop, y_start, y_stop, selection):
"""
constructor for reducedRepPlot object
:param file_path: path to file directory
:type file_path: str
:param x_start: start val for x analysis
:param x_stop: stop val for x analysis
:param y_start:
:param y_stop:
"""
#self.tif_list = get_files(file_path)
self.tif_list = tif_list
assert x_start >= 0 and x_start < x_stop
assert x_stop <= 2048 #TODO change so resolution is flexible
assert y_start >= 0 and y_start < y_stop
assert y_stop <= 2048 #TODO change so resolution is flexible
self.x_start = x_start
self.x_stop = x_stop
self.y_start = y_start
self.y_stop = y_stop
self.selection = selection
def selectionSort(self, alist):
for fillslot in range(len(alist) - 1, 0, -1):
positionOfMax = 0
for location in range(1, fillslot + 1):
if alist[location][0] > alist[positionOfMax][0]:
positionOfMax = location
temp = alist[fillslot]
alist[fillslot] = alist[positionOfMax]
alist[positionOfMax] = temp
for arr in alist:
arr.pop(0)
return alist
# def check_lists(self, list, nested_list, cpu_num):
# flattened_list = [val for sublist in nested_list for val in sublist]
# for i in range(0,cpu_num):
# flattened_list.remove(i)
#
# print(flattened_list == list)
def plot(self):
"""
This function will plot analysis data as a funciton of the number of images. uses multiprocessing to speed things up
:return: void
"""
a = analysis_concurrent(self.y_start, self.y_stop, self.x_start, self.x_stop, self.selection)
trunc_list = []
cpu_count = 10 #multiprocessing.cpu_count()
temp_list = []
for i in range(0, cpu_count):
if i == cpu_count-1:
temp_list = self.tif_list[(i*len(self.tif_list)//cpu_count) : (((1+i)*len(self.tif_list)//cpu_count) +
(len(self.tif_list)%cpu_count))]
temp_list.insert(0, i)
else:
temp_list = self.tif_list[(i*len(self.tif_list)//cpu_count) : ((1+i)*len(self.tif_list)//cpu_count)]
temp_list.insert(0, i)
trunc_list.append(temp_list)
# print(self.check_lists(self.tif_list, trunc_list, cpu_count))
process_list = []
x = range(0,len(self.tif_list))
y = []
q = multiprocessing.Queue()
#a = multiprocessing.Array()
l = multiprocessing.Lock()
#p = multiprocessing.Process(a.x_and_y_vals, args=(l,))
for i in range(0, cpu_count):
process_list.append(multiprocessing.Process(target=a.x_and_y_vals, args=(l, q, trunc_list[i])))
for process in process_list:
process.start()
for process in process_list:
y.append(q.get())
process.join()
# for i in range(0,cpu_count):
# y.append(q.get())
y = self.selectionSort(y)
flattened_y = [val for sublist in y for val in sublist]
assert len(flattened_y) == len(self.tif_list)
plt.scatter(x, flattened_y)
plt.xlabel("file num")
plt.ylabel(self.selection)
# plt.xscale()
plt.show()
| bsd-2-clause |
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