5f7702469e
- Removed v10 TradingView indicator (moneyline_v10_momentum_dots.pinescript) - Removed v10 penalty system from signal-quality.ts (-30/-25 point penalties) - Removed backtest result files (sweep_*.csv) - Updated copilot-instructions.md to remove v10 references - Simplified direction-specific quality thresholds (LONG 90+, SHORT 80+) Rationale: - 1,944 parameter combinations tested in backtest - All top results IDENTICAL (568 trades, $498 P&L, 61.09% WR) - Momentum parameters had ZERO impact on trade selection - Profit factor 1.027 too low (barely profitable after fees) - Max drawdown -$1,270 vs +$498 profit = terrible risk-reward - v10 penalties were blocking good trades (bug: applied to wrong positions) Keeping v9 as production system - simpler, proven, effective.
414 lines
9.0 KiB
Python
414 lines
9.0 KiB
Python
# -*- coding: utf-8 -*-
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"""
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=========
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Constants
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=========
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.. currentmodule:: numpy
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NumPy includes several constants:
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%(constant_list)s
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"""
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#
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# Note: the docstring is autogenerated.
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#
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import re
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import textwrap
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# Maintain same format as in numpy.add_newdocs
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constants = []
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def add_newdoc(module, name, doc):
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constants.append((name, doc))
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add_newdoc('numpy', 'pi',
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"""
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``pi = 3.1415926535897932384626433...``
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References
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----------
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https://en.wikipedia.org/wiki/Pi
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""")
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add_newdoc('numpy', 'e',
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"""
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Euler's constant, base of natural logarithms, Napier's constant.
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``e = 2.71828182845904523536028747135266249775724709369995...``
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See Also
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--------
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exp : Exponential function
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log : Natural logarithm
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References
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----------
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https://en.wikipedia.org/wiki/E_%28mathematical_constant%29
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""")
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add_newdoc('numpy', 'euler_gamma',
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"""
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``γ = 0.5772156649015328606065120900824024310421...``
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References
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----------
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https://en.wikipedia.org/wiki/Euler-Mascheroni_constant
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""")
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add_newdoc('numpy', 'inf',
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"""
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IEEE 754 floating point representation of (positive) infinity.
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Returns
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-------
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y : float
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A floating point representation of positive infinity.
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See Also
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--------
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isinf : Shows which elements are positive or negative infinity
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isposinf : Shows which elements are positive infinity
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isneginf : Shows which elements are negative infinity
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isnan : Shows which elements are Not a Number
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isfinite : Shows which elements are finite (not one of Not a Number,
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positive infinity and negative infinity)
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Notes
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-----
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NumPy uses the IEEE Standard for Binary Floating-Point for Arithmetic
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(IEEE 754). This means that Not a Number is not equivalent to infinity.
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Also that positive infinity is not equivalent to negative infinity. But
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infinity is equivalent to positive infinity.
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`Inf`, `Infinity`, `PINF` and `infty` are aliases for `inf`.
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Examples
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--------
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>>> np.inf
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inf
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>>> np.array([1]) / 0.
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array([ Inf])
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""")
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add_newdoc('numpy', 'nan',
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"""
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IEEE 754 floating point representation of Not a Number (NaN).
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Returns
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-------
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y : A floating point representation of Not a Number.
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See Also
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--------
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isnan : Shows which elements are Not a Number.
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isfinite : Shows which elements are finite (not one of
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Not a Number, positive infinity and negative infinity)
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Notes
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-----
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NumPy uses the IEEE Standard for Binary Floating-Point for Arithmetic
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(IEEE 754). This means that Not a Number is not equivalent to infinity.
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`NaN` and `NAN` are aliases of `nan`.
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Examples
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--------
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>>> np.nan
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nan
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>>> np.log(-1)
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nan
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>>> np.log([-1, 1, 2])
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array([ NaN, 0. , 0.69314718])
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""")
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add_newdoc('numpy', 'newaxis',
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"""
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A convenient alias for None, useful for indexing arrays.
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Examples
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--------
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>>> newaxis is None
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True
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>>> x = np.arange(3)
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>>> x
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array([0, 1, 2])
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>>> x[:, newaxis]
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array([[0],
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[1],
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[2]])
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>>> x[:, newaxis, newaxis]
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array([[[0]],
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[[1]],
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[[2]]])
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>>> x[:, newaxis] * x
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array([[0, 0, 0],
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[0, 1, 2],
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[0, 2, 4]])
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Outer product, same as ``outer(x, y)``:
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>>> y = np.arange(3, 6)
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>>> x[:, newaxis] * y
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array([[ 0, 0, 0],
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[ 3, 4, 5],
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[ 6, 8, 10]])
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``x[newaxis, :]`` is equivalent to ``x[newaxis]`` and ``x[None]``:
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>>> x[newaxis, :].shape
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(1, 3)
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>>> x[newaxis].shape
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(1, 3)
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>>> x[None].shape
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(1, 3)
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>>> x[:, newaxis].shape
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(3, 1)
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""")
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add_newdoc('numpy', 'NZERO',
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"""
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IEEE 754 floating point representation of negative zero.
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Returns
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-------
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y : float
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A floating point representation of negative zero.
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See Also
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--------
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PZERO : Defines positive zero.
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isinf : Shows which elements are positive or negative infinity.
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isposinf : Shows which elements are positive infinity.
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isneginf : Shows which elements are negative infinity.
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isnan : Shows which elements are Not a Number.
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isfinite : Shows which elements are finite - not one of
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Not a Number, positive infinity and negative infinity.
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Notes
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-----
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NumPy uses the IEEE Standard for Binary Floating-Point for Arithmetic
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(IEEE 754). Negative zero is considered to be a finite number.
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Examples
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--------
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>>> np.NZERO
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-0.0
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>>> np.PZERO
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0.0
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>>> np.isfinite([np.NZERO])
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array([ True])
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>>> np.isnan([np.NZERO])
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array([False])
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>>> np.isinf([np.NZERO])
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array([False])
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""")
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add_newdoc('numpy', 'PZERO',
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"""
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IEEE 754 floating point representation of positive zero.
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Returns
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-------
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y : float
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A floating point representation of positive zero.
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See Also
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--------
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NZERO : Defines negative zero.
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isinf : Shows which elements are positive or negative infinity.
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isposinf : Shows which elements are positive infinity.
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isneginf : Shows which elements are negative infinity.
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isnan : Shows which elements are Not a Number.
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isfinite : Shows which elements are finite - not one of
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Not a Number, positive infinity and negative infinity.
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Notes
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-----
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NumPy uses the IEEE Standard for Binary Floating-Point for Arithmetic
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(IEEE 754). Positive zero is considered to be a finite number.
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Examples
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--------
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>>> np.PZERO
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0.0
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>>> np.NZERO
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-0.0
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>>> np.isfinite([np.PZERO])
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array([ True])
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>>> np.isnan([np.PZERO])
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array([False])
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>>> np.isinf([np.PZERO])
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array([False])
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""")
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add_newdoc('numpy', 'NAN',
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"""
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IEEE 754 floating point representation of Not a Number (NaN).
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`NaN` and `NAN` are equivalent definitions of `nan`. Please use
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`nan` instead of `NAN`.
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See Also
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--------
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nan
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""")
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add_newdoc('numpy', 'NaN',
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"""
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IEEE 754 floating point representation of Not a Number (NaN).
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`NaN` and `NAN` are equivalent definitions of `nan`. Please use
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`nan` instead of `NaN`.
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See Also
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--------
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nan
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""")
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add_newdoc('numpy', 'NINF',
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"""
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IEEE 754 floating point representation of negative infinity.
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Returns
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-------
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y : float
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A floating point representation of negative infinity.
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See Also
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--------
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isinf : Shows which elements are positive or negative infinity
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isposinf : Shows which elements are positive infinity
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isneginf : Shows which elements are negative infinity
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isnan : Shows which elements are Not a Number
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isfinite : Shows which elements are finite (not one of Not a Number,
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positive infinity and negative infinity)
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Notes
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-----
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NumPy uses the IEEE Standard for Binary Floating-Point for Arithmetic
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(IEEE 754). This means that Not a Number is not equivalent to infinity.
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Also that positive infinity is not equivalent to negative infinity. But
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infinity is equivalent to positive infinity.
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Examples
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--------
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>>> np.NINF
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-inf
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>>> np.log(0)
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-inf
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""")
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add_newdoc('numpy', 'PINF',
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"""
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IEEE 754 floating point representation of (positive) infinity.
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Use `inf` because `Inf`, `Infinity`, `PINF` and `infty` are aliases for
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`inf`. For more details, see `inf`.
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See Also
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--------
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inf
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""")
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add_newdoc('numpy', 'infty',
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"""
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IEEE 754 floating point representation of (positive) infinity.
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Use `inf` because `Inf`, `Infinity`, `PINF` and `infty` are aliases for
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`inf`. For more details, see `inf`.
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See Also
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--------
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inf
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""")
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add_newdoc('numpy', 'Inf',
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"""
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IEEE 754 floating point representation of (positive) infinity.
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Use `inf` because `Inf`, `Infinity`, `PINF` and `infty` are aliases for
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`inf`. For more details, see `inf`.
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See Also
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--------
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inf
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""")
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add_newdoc('numpy', 'Infinity',
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"""
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IEEE 754 floating point representation of (positive) infinity.
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Use `inf` because `Inf`, `Infinity`, `PINF` and `infty` are aliases for
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`inf`. For more details, see `inf`.
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See Also
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--------
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inf
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""")
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if __doc__:
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constants_str = []
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constants.sort()
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for name, doc in constants:
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s = textwrap.dedent(doc).replace("\n", "\n ")
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# Replace sections by rubrics
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lines = s.split("\n")
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new_lines = []
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for line in lines:
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m = re.match(r'^(\s+)[-=]+\s*$', line)
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if m and new_lines:
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prev = textwrap.dedent(new_lines.pop())
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new_lines.append('%s.. rubric:: %s' % (m.group(1), prev))
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new_lines.append('')
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else:
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new_lines.append(line)
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s = "\n".join(new_lines)
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# Done.
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constants_str.append(""".. data:: %s\n %s""" % (name, s))
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constants_str = "\n".join(constants_str)
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__doc__ = __doc__ % dict(constant_list=constants_str)
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del constants_str, name, doc
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del line, lines, new_lines, m, s, prev
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del constants, add_newdoc
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