Remove modules
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Binary file not shown.
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Before Width: | Height: | Size: 240 KiB |
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"""
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Module extending the bitoperator capabilites of numpy
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"""
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from numpy import (bitwise_and, bitwise_or,
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bitwise_not, binary_repr, # @UnusedImport
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bitwise_xor, where, arange) # @UnusedImport
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__all__ = ['bitwise_and', 'bitwise_or', 'bitwise_not', 'binary_repr',
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'bitwise_xor', 'getbit', 'setbit', 'getbits', 'setbits']
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def getbit(i, bit):
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"""
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Get bit at specified position
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Parameters
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----------
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i : array-like of uints, longs
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value to
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bit : array-like of ints or longs
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bit position between 0 and the number of bits in the uint class.
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Examples
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--------
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>>> import numpy as np
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>>> binary_repr(13)
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'1101'
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>>> getbit(13,np.arange(3,-1,-1))
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array([1, 1, 0, 1])
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>>> getbit(5, np.r_[0:4])
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array([1, 0, 1, 0])
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"""
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return bitwise_and(i, 1 << bit) >> bit
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def getbits(i, numbits=8):
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"""
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Returns bits of i in a list
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"""
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return getbit(i, arange(0, numbits))
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def setbit(i, bit, value=1):
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"""
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Set bit at specified position
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Parameters
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----------
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i : array-like of uints, longs
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value to
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bit : array-like of ints or longs
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bit position between 0 and the number of bits in the uint class.
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value : array-like of 0 or 1
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value to set the bit to.
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Examples
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--------
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Set bit fifth bit in the five bit binary binary representation of 9 (01001)
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yields 25 (11001)
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>>> setbit(9,4)
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array(25)
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"""
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val1 = 1 << bit
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val0 = bitwise_not(val1)
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return where((value == 0) & (i == i) & (bit == bit), bitwise_and(i, val0),
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bitwise_or(i, val1))
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def setbits(bitlist):
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"""
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Set bits of val to values in bitlist
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Example
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-------
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>>> setbits([1,1])
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3
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>>> setbits([1,0])
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1
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"""
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val = 0
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for i, j in enumerate(bitlist):
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val |= j << i
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return val
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if __name__ == '__main__':
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from wafo.testing import test_docstrings
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test_docstrings(__file__)
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# t = set(np.arange(8),1,1)
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# t=get(0x84,np.arange(0,8))
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# t=getbyte(0x84)
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# t=get(0x84,[0, 1, 2, 3, 4, 5, 6, 7])
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# t=get(0x20, 6)
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# bit = [0 for i in range(8)]
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# bit[7]=1
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# t = setbits(bit)
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# print(hex(t))
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# TODO Fix doctests in fig.py
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collect_ignore = ["fig.py", "MSO.py", "MSPPT.py", "powerpoint.py",
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"win32_utils.py"]
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@ -1,310 +0,0 @@
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"""
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WAFO defintions and numenclature
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crossings :
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cycle_pairs :
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turning_points :
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wave_amplitudes :
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wave_periods :
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waves :
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Examples
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--------
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In order to view the documentation do the following in an ipython window:
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import wafo.definitions as wd
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wd.crossings()
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or
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wd.crossings?
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"""
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def wave_amplitudes():
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r"""
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Wave amplitudes and heights definitions and nomenclature
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Definition of wave amplitudes and wave heights
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---------------------------------------------
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<----- Direction of wave propagation
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|..............c_..........|
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| /| \ |
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Hd | _/ | \ | Hu
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M | / | \ |
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/ \ | M / Ac | \_ | c_
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F \ | / \m/ | \ | / \
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------d----|---u------------------d---|---u----d------ level v
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\ | /| \ | / \L
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\_ | / | At \_|_/
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\|/..| t
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t
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Parameters
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----------
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Ac : crest amplitude
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At : trough amplitude
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Hd : wave height as defined for down crossing waves
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Hu : wave height as defined for up crossing waves
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See also
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--------
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waves, crossings, turning_points
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"""
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print(wave_amplitudes.__doc__)
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def crossings():
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r"""
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Level v crossing definitions and nomenclature
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Definition of level v crossings
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-------------------------------
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M
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. . M M
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. . . . . .
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F d . . L
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-----------------------u-------d-------o----------------- level v
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. . . . u
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. m
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m
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Let the letters 'm', 'M', 'F', 'L','d' and 'u' in the
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figure above denote local minimum, maximum, first value, last
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value, down- and up-crossing, respectively. The remaining
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sampled values are indicated with a '.'. Values that are identical
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with v, but do not cross the level is indicated with the letter 'o'.
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We have a level up-crossing at index, k, if
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x(k) < v and v < x(k+1)
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or if
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x(k) == v and v < x(k+1) and x(r) < v for some di < r <= k-1
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where di is the index to the previous downcrossing.
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Similarly there is a level down-crossing at index, k, if
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x(k) > v and v > x(k+1)
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or if
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x(k) == v and v > x(k+1) and x(r) > v for some ui < r <= k-1
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where ui is the index to the previous upcrossing.
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The first (F) value is a up crossing if x(1) = v and x(2) > v.
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Similarly, it is a down crossing if x(1) = v and x(2) < v.
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See also
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--------
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wave_periods, waves, turning_points, findcross, findtp
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"""
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print(crossings.__doc__)
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def cycle_pairs():
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r"""
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Cycle pairs definitions and numenclature
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Definition of Max2min and min2Max cycle pair
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--------------------------------------------
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A min2Max cycle pair (mM) is defined as the pair of a minimum
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and the following Maximum. Similarly a Max2min cycle pair (Mm)
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is defined as the pair of a Maximum and the following minimum.
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(all turning points possibly rainflowfiltered before pairing into cycles.)
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See also
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--------
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turning_points
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"""
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print(cycle_pairs.__doc__)
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def wave_periods():
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r"""
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Wave periods (lengths) definitions and nomenclature
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Definition of wave periods (lengths)
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------------------------------------
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<----- Direction of wave propagation
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<-------Tu--------->
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: :
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<---Tc-----> :
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: : : <------Tcc---->
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M : c : : : :
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/ \ : M / \_ : : c_ c
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F \ :/ \m/ \: :/ \ / \
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------d--------u----------d-------u----d--------u---d-------- level v
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\ / \ / :\_ _/: :\_ L
|
|
||||||
\_ / \_t_/ : \t_/ : : \m/
|
|
||||||
\t/ : : : :
|
|
||||||
: : <---Tt---> :
|
|
||||||
<--------Ttt-------> : :
|
|
||||||
<-----Td----->
|
|
||||||
Tu = Up crossing period
|
|
||||||
Td = Down crossing period
|
|
||||||
Tc = Crest period, i.e., period between up crossing and
|
|
||||||
the next down crossing
|
|
||||||
Tt = Trough period, i.e., period between down crossing and
|
|
||||||
the next up crossing
|
|
||||||
Ttt = Trough2trough period
|
|
||||||
Tcc = Crest2crest period
|
|
||||||
|
|
||||||
|
|
||||||
<----- Direction of wave propagation
|
|
||||||
|
|
||||||
<--Tcf-> Tuc
|
|
||||||
: : <-Tcb-> <->
|
|
||||||
M : c : : : :
|
|
||||||
/ \ : M / \_ c_ : : c
|
|
||||||
F \ :/ \m/ \ / \___: :/ \
|
|
||||||
------d---------u----------d---------u-------d--------u---d------ level v
|
|
||||||
:\_ / \ __/: \_ _/ \_ L
|
|
||||||
: \_ / \_t_/ : \t_/ \m/
|
|
||||||
: \t/ : :
|
|
||||||
: : : :
|
|
||||||
<-Ttf-> <-Ttb->
|
|
||||||
|
|
||||||
|
|
||||||
Tcf = Crest front period, i.e., period between up crossing and crest
|
|
||||||
Tcb = Crest back period, i.e., period between crest and down crossing
|
|
||||||
Ttf = Trough front period, i.e., period between down crossing and trough
|
|
||||||
Ttb = Trough back period, i.e., period between trough and up crossing
|
|
||||||
Also note that Tcf and Ttf can also be abbreviated by their crossing
|
|
||||||
marker, e.g. Tuc (u2c) and Tdt (d2t), respectively. Similar applies
|
|
||||||
to all the other wave periods and wave lengths.
|
|
||||||
|
|
||||||
(The nomenclature for wave length is similar, just substitute T and
|
|
||||||
period with L and length, respectively)
|
|
||||||
|
|
||||||
<----- Direction of wave propagation
|
|
||||||
|
|
||||||
<--TMm-->
|
|
||||||
<-TmM-> : :
|
|
||||||
M : : M :
|
|
||||||
/ \ : M /:\_ : M_ M
|
|
||||||
F \ : / \m/ : \ : /: \ / \
|
|
||||||
\ : / : \ : / : \ / \
|
|
||||||
\ : / : \ : / : \_ _/ \_ L
|
|
||||||
\_ : / : \_m_/ : \m_/ \m/
|
|
||||||
\m/ : : : :
|
|
||||||
<-----TMM-----> <----Tmm----->
|
|
||||||
|
|
||||||
|
|
||||||
TmM = Period between minimum and the following Maximum
|
|
||||||
TMm = Period between Maximum and the following minimum
|
|
||||||
TMM = Period between Maximum and the following Maximum
|
|
||||||
Tmm = Period between minimum and the following minimum
|
|
||||||
|
|
||||||
See also
|
|
||||||
--------
|
|
||||||
waves,
|
|
||||||
wave_amplitudes,
|
|
||||||
crossings,
|
|
||||||
turning_points
|
|
||||||
"""
|
|
||||||
print(wave_periods.__doc__)
|
|
||||||
|
|
||||||
|
|
||||||
def turning_points():
|
|
||||||
r"""
|
|
||||||
Turning points definitions and numenclature
|
|
||||||
|
|
||||||
Definition of turningpoints
|
|
||||||
---------------------------
|
|
||||||
<----- Direction of wave propagation
|
|
||||||
|
|
||||||
M M
|
|
||||||
/ \ .... M /:\_ M_ M
|
|
||||||
F \ | / \m/ : \ /: \ / \
|
|
||||||
\ h | / : \ / : \ / \
|
|
||||||
\ | / : \ / : \_ _/ \_ L
|
|
||||||
\_ | / : \_m_/ : \m_/ \m/
|
|
||||||
\m/ : : : :
|
|
||||||
<------Mw-----> <-----mw----->
|
|
||||||
|
|
||||||
Local minimum or maximum are indicated with the
|
|
||||||
letters 'm' or 'M'. Turning points in this connection are all
|
|
||||||
local max (M) and min (m) and the last (L) value and the
|
|
||||||
first (F) value if the first local extremum is a max.
|
|
||||||
|
|
||||||
(This choice is made in order to get the exact up-crossing intensity
|
|
||||||
from rfc by mm2lc(tp2mm(rfc)) )
|
|
||||||
|
|
||||||
|
|
||||||
See also
|
|
||||||
--------
|
|
||||||
waves,
|
|
||||||
crossings,
|
|
||||||
cycle_pairs
|
|
||||||
findtp
|
|
||||||
|
|
||||||
"""
|
|
||||||
print(turning_points.__doc__)
|
|
||||||
|
|
||||||
|
|
||||||
def waves():
|
|
||||||
r"""
|
|
||||||
Wave definitions and nomenclature
|
|
||||||
|
|
||||||
Definition of trough and crest
|
|
||||||
------------------------------
|
|
||||||
A trough (t) is defined as the global minimum between a
|
|
||||||
level v down-crossing (d) and the next up-crossing (u)
|
|
||||||
and a crest (c) is defined as the global maximum between a
|
|
||||||
level v up-crossing and the following down-crossing.
|
|
||||||
|
|
||||||
Definition of down- and up -crossing waves
|
|
||||||
------------------------------------------
|
|
||||||
A level v-down-crossing wave (dw) is a wave from a
|
|
||||||
down-crossing to the following down-crossing.
|
|
||||||
Similarly, a level v-up-crossing wave (uw) is a wave from an up-crossing
|
|
||||||
to the next up-crossing.
|
|
||||||
|
|
||||||
Definition of trough and crest waves
|
|
||||||
------------------------------------
|
|
||||||
A trough-to-trough wave (tw) is a wave from a trough (t) to the
|
|
||||||
following trough. The crest-to-crest wave (cw) is defined similarly.
|
|
||||||
|
|
||||||
|
|
||||||
Definition of min2min and Max2Max wave
|
|
||||||
--------------------------------------
|
|
||||||
A min2min wave (mw) is defined starting from a minimum (m) and
|
|
||||||
ending in the following minimum.
|
|
||||||
Similarly a Max2Max wave (Mw) is thus a wave from a maximum (M)
|
|
||||||
to the next maximum (all waves optionally rainflow filtered).
|
|
||||||
|
|
||||||
<----- Direction of wave propagation
|
|
||||||
|
|
||||||
|
|
||||||
<------Mw-----> <----mw---->
|
|
||||||
M : : c :
|
|
||||||
/ \ M : / \_ : c_ c
|
|
||||||
F \ / \m/ \ : /: \ /:\
|
|
||||||
------d--------u----------d-------u----d--------u---d------ level v
|
|
||||||
\ /: \ : /: : :\_ _/ : :\_ L
|
|
||||||
\_ / : \_t_/ : : : \t_/ : : \m/
|
|
||||||
\t/ <-------uw---------> : <-----dw----->
|
|
||||||
: : : :
|
|
||||||
<--------tw--------> <------cw----->
|
|
||||||
|
|
||||||
(F=first value and L=last value).
|
|
||||||
|
|
||||||
See also
|
|
||||||
--------
|
|
||||||
turning_points,
|
|
||||||
crossings,
|
|
||||||
wave_periods
|
|
||||||
findtc,
|
|
||||||
findcross
|
|
||||||
"""
|
|
||||||
print(waves.__doc__)
|
|
||||||
|
|
||||||
if __name__ == '__main__':
|
|
||||||
import doctest
|
|
||||||
doctest.testmod(optionflags=doctest.NORMALIZE_WHITESPACE)
|
|
||||||
@ -1,146 +0,0 @@
|
|||||||
"""
|
|
||||||
Created on 20. jan. 2011
|
|
||||||
|
|
||||||
@author: pab
|
|
||||||
"""
|
|
||||||
import numpy as np
|
|
||||||
from numpy import exp, meshgrid
|
|
||||||
__all__ = ['peaks', 'humps', 'magic']
|
|
||||||
|
|
||||||
|
|
||||||
def _magic_odd_order(n):
|
|
||||||
ix = np.arange(n) + 1
|
|
||||||
J, I = np.meshgrid(ix, ix)
|
|
||||||
A = np.mod(I + J - (n + 3) / 2, n)
|
|
||||||
B = np.mod(I + 2 * J - 2, n)
|
|
||||||
M = n * A + B + 1
|
|
||||||
return M
|
|
||||||
|
|
||||||
|
|
||||||
def _magic_doubly_even_order(n):
|
|
||||||
M = np.arange(1, n * n + 1).reshape(n, n)
|
|
||||||
ix = np.mod(np.arange(n) + 1, 4) // 2
|
|
||||||
J, I = np.meshgrid(ix, ix)
|
|
||||||
iz = np.flatnonzero(I == J)
|
|
||||||
M.put(iz, n * n + 1 - M.flat[iz])
|
|
||||||
return M
|
|
||||||
|
|
||||||
|
|
||||||
def _magic_even_order(n):
|
|
||||||
p = n // 2
|
|
||||||
M0 = magic(p)
|
|
||||||
M = np.hstack((np.vstack((M0, M0 + 3 * p * p)),
|
|
||||||
np.vstack((M0 + 2 * p * p, M0 + p * p))))
|
|
||||||
if n > 2:
|
|
||||||
k = (n - 2) // 4
|
|
||||||
jvec = np.hstack((np.arange(k), np.arange(n - k + 1, n)))
|
|
||||||
for i in range(p):
|
|
||||||
for j in jvec:
|
|
||||||
temp = M[i][j]
|
|
||||||
M[i][j] = M[i + p][j]
|
|
||||||
M[i + p][j] = temp
|
|
||||||
|
|
||||||
i = k
|
|
||||||
j = 0
|
|
||||||
temp = M[i][j]
|
|
||||||
M[i][j] = M[i + p][j]
|
|
||||||
M[i + p][j] = temp
|
|
||||||
j = i
|
|
||||||
temp = M[i + p][j]
|
|
||||||
M[i + p][j] = M[i][j]
|
|
||||||
M[i][j] = temp
|
|
||||||
return M
|
|
||||||
|
|
||||||
|
|
||||||
def magic(n):
|
|
||||||
"""
|
|
||||||
Return magic square for n of any orders > 2.
|
|
||||||
|
|
||||||
A magic square has the property that the sum of every row and column,
|
|
||||||
as well as both diagonals, is the same number.
|
|
||||||
|
|
||||||
Examples
|
|
||||||
--------
|
|
||||||
>>> np.allclose(magic(3),
|
|
||||||
... [[8, 1, 6],
|
|
||||||
... [3, 5, 7],
|
|
||||||
... [4, 9, 2]])
|
|
||||||
True
|
|
||||||
|
|
||||||
>>> np.allclose(magic(4),
|
|
||||||
... [[16, 2, 3, 13],
|
|
||||||
... [ 5, 11, 10, 8],
|
|
||||||
... [ 9, 7, 6, 12],
|
|
||||||
... [ 4, 14, 15, 1]])
|
|
||||||
True
|
|
||||||
|
|
||||||
>>> np.allclose(magic(6),
|
|
||||||
... [[35, 1, 6, 26, 19, 24],
|
|
||||||
... [ 3, 32, 7, 21, 23, 25],
|
|
||||||
... [31, 9, 2, 22, 27, 20],
|
|
||||||
... [ 8, 28, 33, 17, 10, 15],
|
|
||||||
... [30, 5, 34, 12, 14, 16],
|
|
||||||
... [ 4, 36, 29, 13, 18, 11]])
|
|
||||||
True
|
|
||||||
"""
|
|
||||||
if (n < 3):
|
|
||||||
raise ValueError('n must be greater than 2.')
|
|
||||||
|
|
||||||
if np.mod(n, 2) == 1:
|
|
||||||
return _magic_odd_order(n)
|
|
||||||
elif np.mod(n, 4) == 0:
|
|
||||||
return _magic_doubly_even_order(n)
|
|
||||||
return _magic_even_order(n)
|
|
||||||
|
|
||||||
|
|
||||||
def peaks(x=None, y=None, n=51):
|
|
||||||
"""
|
|
||||||
Return the "well" known MatLab (R) peaks function
|
|
||||||
evaluated in the [-3,3] x,y range
|
|
||||||
|
|
||||||
Example
|
|
||||||
-------
|
|
||||||
>>> import matplotlib.pyplot as plt
|
|
||||||
>>> x,y,z = peaks()
|
|
||||||
|
|
||||||
h = plt.contourf(x,y,z)
|
|
||||||
|
|
||||||
"""
|
|
||||||
if x is None:
|
|
||||||
x = np.linspace(-3, 3, n)
|
|
||||||
if y is None:
|
|
||||||
y = np.linspace(-3, 3, n)
|
|
||||||
|
|
||||||
[x1, y1] = meshgrid(x, y)
|
|
||||||
|
|
||||||
z = (3 * (1 - x1) ** 2 * exp(-(x1 ** 2) - (y1 + 1) ** 2) -
|
|
||||||
10 * (x1 / 5 - x1 ** 3 - y1 ** 5) * exp(-x1 ** 2 - y1 ** 2) -
|
|
||||||
1. / 3 * exp(-(x1 + 1) ** 2 - y1 ** 2))
|
|
||||||
|
|
||||||
return x1, y1, z
|
|
||||||
|
|
||||||
|
|
||||||
def humps(x=None):
|
|
||||||
"""
|
|
||||||
Computes a function that has three roots, and some humps.
|
|
||||||
|
|
||||||
Example
|
|
||||||
-------
|
|
||||||
>>> import matplotlib.pyplot as plt
|
|
||||||
>>> x = np.linspace(0,1)
|
|
||||||
>>> y = humps(x)
|
|
||||||
|
|
||||||
h = plt.plot(x,y)
|
|
||||||
"""
|
|
||||||
if x is None:
|
|
||||||
y = np.linspace(0, 1)
|
|
||||||
else:
|
|
||||||
y = np.asarray(x)
|
|
||||||
|
|
||||||
return 1.0 / ((y - 0.3) ** 2 + 0.01) + 1.0 / ((y - 0.9) ** 2 + 0.04) + \
|
|
||||||
2 * y - 5.2
|
|
||||||
|
|
||||||
|
|
||||||
if __name__ == '__main__':
|
|
||||||
from wafo.testing import test_docstrings
|
|
||||||
test_docstrings(__file__)
|
|
||||||
@ -1,65 +0,0 @@
|
|||||||
# -*- coding: utf-8 -*-
|
|
||||||
"""
|
|
||||||
f2py c_library.pyf c_functions.c -c
|
|
||||||
|
|
||||||
See also http://www.scipy.org/Cookbook/CompilingExtensionsOnWindowsWithMinGW
|
|
||||||
"""
|
|
||||||
import os
|
|
||||||
import sys
|
|
||||||
|
|
||||||
|
|
||||||
def which(program):
|
|
||||||
"""
|
|
||||||
Return filepath to program if it exists
|
|
||||||
|
|
||||||
In order to test if a certain executable exists, it will search for the
|
|
||||||
program name in the environment variables.
|
|
||||||
If program is a full path to an executable, it will check it exists
|
|
||||||
|
|
||||||
Copied from:
|
|
||||||
http://stackoverflow.com/questions/377017/test-if-executable-exists-in-python/
|
|
||||||
It is supposed to mimic the UNIX command "which"
|
|
||||||
"""
|
|
||||||
|
|
||||||
def is_exe(fpath):
|
|
||||||
return os.path.exists(fpath) and os.access(fpath, os.X_OK)
|
|
||||||
|
|
||||||
fpath, unused_fname = os.path.split(program)
|
|
||||||
if fpath:
|
|
||||||
if is_exe(program):
|
|
||||||
return program
|
|
||||||
else:
|
|
||||||
for path in os.environ["PATH"].split(os.pathsep):
|
|
||||||
exe_file = os.path.join(path, program)
|
|
||||||
if is_exe(exe_file):
|
|
||||||
return exe_file
|
|
||||||
|
|
||||||
return None
|
|
||||||
|
|
||||||
|
|
||||||
def f2py_call_str():
|
|
||||||
'''Return which f2py callable is in the path regardless of platform'''
|
|
||||||
|
|
||||||
# define possible options:
|
|
||||||
# on Arch Linux, python and f2py are the calls corresponding to python 3
|
|
||||||
# and python2/f2py2 for python 2
|
|
||||||
# other Linux versions might still use python/f2py for python 2
|
|
||||||
|
|
||||||
if os.path.basename(sys.executable).endswith('2'):
|
|
||||||
options = ('f2py2', 'f2py2.6', 'f2py2.7',)
|
|
||||||
else: # on Windows and other Linux using python/f2py
|
|
||||||
options = ('f2py.exe', 'f2py.bat', 'f2py', 'f2py2.6', 'f2py2.7',
|
|
||||||
'f2py.py',)
|
|
||||||
for k in options:
|
|
||||||
if which(k):
|
|
||||||
# Found the f2py path, no need to look further
|
|
||||||
f2py_call = k
|
|
||||||
f2py_path = which(k)
|
|
||||||
break
|
|
||||||
|
|
||||||
try:
|
|
||||||
print('found f2py in:', f2py_path)
|
|
||||||
return f2py_call
|
|
||||||
except NameError:
|
|
||||||
raise UserWarning('Couldn\'t locate f2py. '
|
|
||||||
'Should be part of NumPy installation.')
|
|
||||||
@ -1,908 +0,0 @@
|
|||||||
# /usr/bin/env python
|
|
||||||
'''
|
|
||||||
Module FIG
|
|
||||||
------------
|
|
||||||
Module for manipulating windows/figures created using
|
|
||||||
pylab or enthought.mayavi.mlab on the windows platform.
|
|
||||||
|
|
||||||
Figure manipulation involves
|
|
||||||
maximization, minimization, hiding, closing, stacking or tiling.
|
|
||||||
|
|
||||||
It is assumed that the figures are uniquely numbered in the following way:
|
|
||||||
Figure 1
|
|
||||||
Figure 2
|
|
||||||
....
|
|
||||||
or
|
|
||||||
TVTK scene 1
|
|
||||||
TVTK scene 2
|
|
||||||
TVTK scene 3
|
|
||||||
...
|
|
||||||
|
|
||||||
Example
|
|
||||||
-------
|
|
||||||
>>> import pylab as p
|
|
||||||
>>> import wafo.fig as fig
|
|
||||||
>>> for ix in range(6):
|
|
||||||
... f = p.figure(ix)
|
|
||||||
>>> fig.stack('all')
|
|
||||||
>>> fig.stack(1,2)
|
|
||||||
>>> fig.hide(1)
|
|
||||||
>>> fig.restore(1)
|
|
||||||
>>> fig.tile()
|
|
||||||
>>> fig.pile()
|
|
||||||
>>> fig.maximize(4)
|
|
||||||
>>> fig.close('all')
|
|
||||||
'''
|
|
||||||
|
|
||||||
from __future__ import absolute_import, division, print_function
|
|
||||||
# import win32api
|
|
||||||
import win32gui
|
|
||||||
import win32con
|
|
||||||
import wx
|
|
||||||
import numpy
|
|
||||||
|
|
||||||
from win32gui import (EnumWindows, MoveWindow, GetWindowRect, FindWindow,
|
|
||||||
ShowWindow, BringWindowToTop)
|
|
||||||
|
|
||||||
__all__ = ['close', 'cycle', 'hide', 'keep', 'maximize', 'minimize', 'pile',
|
|
||||||
'restore', 'stack', 'tile', 'find_all_figure_numbers', 'set_size']
|
|
||||||
|
|
||||||
# Figure format strings to recognize in window title
|
|
||||||
FIGURE_TITLE_FORMATS = ('Figure', 'TVTK Scene', 'Chaco Plot Window: Figure')
|
|
||||||
_SCREENSIZE = None
|
|
||||||
|
|
||||||
|
|
||||||
class CycleDialog(wx.Dialog):
|
|
||||||
|
|
||||||
def _get_buttons(self):
|
|
||||||
hbox = wx.BoxSizer(wx.HORIZONTAL)
|
|
||||||
buttons = ['Forward', 'Back', 'Cancel']
|
|
||||||
callbacks = [self.on_forward, self.on_backward, self.on_cancel]
|
|
||||||
for button, callback in zip(buttons, callbacks):
|
|
||||||
button = wx.Button(self, -1, button, size=(70, 30))
|
|
||||||
self.Bind(wx.EVT_BUTTON, callback, button)
|
|
||||||
hbox.Add(button, 1, wx.ALIGN_CENTER)
|
|
||||||
return hbox
|
|
||||||
|
|
||||||
def _get_message(self):
|
|
||||||
label = ('Press back or forward to display previous or next figure(s),'
|
|
||||||
' respectively. Press cancel to quit.')
|
|
||||||
message = wx.StaticText(self, label=label, size=(240, 25))
|
|
||||||
return message
|
|
||||||
|
|
||||||
def __init__(self, parent, interval=None, title='Cycle dialog'):
|
|
||||||
super(CycleDialog, self).__init__(parent, title=title, size=(260, 130))
|
|
||||||
if isinstance(interval, (float, int)):
|
|
||||||
self.interval_milli_sec = interval * 1000
|
|
||||||
else:
|
|
||||||
self.interval_milli_sec = 30
|
|
||||||
|
|
||||||
self.timer = wx.Timer(self)
|
|
||||||
self.Bind(wx.EVT_TIMER, self.on_forward, self.timer)
|
|
||||||
|
|
||||||
vbox = wx.BoxSizer(wx.VERTICAL)
|
|
||||||
vbox.Add(self._get_message(), 0, wx.ALIGN_CENTER | wx.TOP, 20)
|
|
||||||
vbox.Add(self._get_buttons(), 1, wx.ALIGN_CENTER | wx.TOP | wx.BOTTOM, 10)
|
|
||||||
self.SetSizer(vbox)
|
|
||||||
|
|
||||||
def ShowModal(self, *args, **kwargs):
|
|
||||||
self.timer.Start(self.interval_milli_sec, oneShot=True)
|
|
||||||
return super(CycleDialog, self).ShowModal(*args, **kwargs)
|
|
||||||
|
|
||||||
def on_forward(self, evt):
|
|
||||||
self.EndModal(wx.ID_FORWARD)
|
|
||||||
|
|
||||||
def on_backward(self, evt):
|
|
||||||
self.EndModal(wx.ID_BACKWARD)
|
|
||||||
|
|
||||||
def on_cancel(self, evt):
|
|
||||||
self.EndModal(wx.ID_CANCEL)
|
|
||||||
|
|
||||||
|
|
||||||
def _get_cycle_dialog(parent=None, interval=None):
|
|
||||||
app = wx.GetApp()
|
|
||||||
if not app:
|
|
||||||
app = wx.App(redirect=False)
|
|
||||||
frame = wx.Frame(None)
|
|
||||||
app.SetTopWindow(frame)
|
|
||||||
dlg = CycleDialog(parent, interval)
|
|
||||||
return dlg
|
|
||||||
|
|
||||||
|
|
||||||
def get_window_position_and_size(window_handle):
|
|
||||||
pos = GetWindowRect(window_handle)
|
|
||||||
return pos[0], pos[1], pos[2] - pos[0], pos[3] - pos[1]
|
|
||||||
|
|
||||||
|
|
||||||
def get_screen_position_and_size(window_handles):
|
|
||||||
"""Return screen position; X, Y and size; width, height.
|
|
||||||
|
|
||||||
Parameters
|
|
||||||
----------
|
|
||||||
window_handles: list of handles to open window figures
|
|
||||||
(Note: only needed the first time)
|
|
||||||
|
|
||||||
Returns
|
|
||||||
--------
|
|
||||||
X : coordinate of the left side of the screen.
|
|
||||||
Y : coordinate of the top of the screen.
|
|
||||||
width : screen horizontal size
|
|
||||||
height : screen vertical size
|
|
||||||
|
|
||||||
"""
|
|
||||||
# pylint: disable=global-statement
|
|
||||||
global _SCREENSIZE
|
|
||||||
if _SCREENSIZE is None:
|
|
||||||
window_handle = window_handles[0]
|
|
||||||
pos = get_window_position_and_size(window_handle)
|
|
||||||
_show_windows((window_handle,), win32con.SW_SHOWMAXIMIZED)
|
|
||||||
_SCREENSIZE = get_window_position_and_size(window_handle)
|
|
||||||
MoveWindow(window_handle, pos[0], pos[1], pos[2], pos[3], 1)
|
|
||||||
return _SCREENSIZE
|
|
||||||
|
|
||||||
|
|
||||||
def _get_screen_size(wnds):
|
|
||||||
screen_width, screen_height = get_screen_position_and_size(wnds)[2:4]
|
|
||||||
return screen_width, screen_height
|
|
||||||
|
|
||||||
|
|
||||||
def _windowEnumerationHandler(handle, result_list):
|
|
||||||
"""Pass to win32gui.EnumWindows() to generate list of window handle, window
|
|
||||||
text tuples."""
|
|
||||||
# pylint: disable=no-member
|
|
||||||
if win32gui.IsWindowVisible(handle):
|
|
||||||
result_list.append((handle, win32gui.GetWindowText(handle)))
|
|
||||||
|
|
||||||
|
|
||||||
def _find_window_handles_and_titles(wantedTitle=None):
|
|
||||||
"""Return list of window handle and window title tuples.
|
|
||||||
|
|
||||||
Parameter
|
|
||||||
---------
|
|
||||||
wantedTitle:
|
|
||||||
|
|
||||||
"""
|
|
||||||
handles_n_titles = []
|
|
||||||
EnumWindows(_windowEnumerationHandler, handles_n_titles)
|
|
||||||
if wantedTitle is None:
|
|
||||||
return handles_n_titles
|
|
||||||
else:
|
|
||||||
return [(handle, title)
|
|
||||||
for handle, title in handles_n_titles
|
|
||||||
if title.startswith(wantedTitle)]
|
|
||||||
|
|
||||||
|
|
||||||
def find_figure_handles(*figure_numbers):
|
|
||||||
"""Find figure handles from figure numbers."""
|
|
||||||
wnd_handles = []
|
|
||||||
for figure_number in _parse_figure_numbers(*figure_numbers):
|
|
||||||
for format_ in FIGURE_TITLE_FORMATS:
|
|
||||||
winTitle = format_ + ' %d' % figure_number
|
|
||||||
handle = FindWindow(None, winTitle)
|
|
||||||
if not handle == 0:
|
|
||||||
wnd_handles.append(handle)
|
|
||||||
return wnd_handles
|
|
||||||
|
|
||||||
|
|
||||||
def find_all_figure_numbers():
|
|
||||||
"""Return list of all figure numbers.
|
|
||||||
|
|
||||||
Example
|
|
||||||
-------
|
|
||||||
>>> import fig
|
|
||||||
>>> import pylab as p
|
|
||||||
>>> for ix in range(5):
|
|
||||||
... f = p.figure(ix)
|
|
||||||
... p.draw()
|
|
||||||
|
|
||||||
fig.find_all_figure_numbers()
|
|
||||||
[0, 1, 2, 3, 4]
|
|
||||||
|
|
||||||
>>> fig.close()
|
|
||||||
|
|
||||||
"""
|
|
||||||
figure_numbers = []
|
|
||||||
for wantedTitle in FIGURE_TITLE_FORMATS:
|
|
||||||
handles_n_titles = _find_window_handles_and_titles(wantedTitle)
|
|
||||||
for _handle, title in handles_n_titles:
|
|
||||||
try:
|
|
||||||
number = int(title.split()[-1])
|
|
||||||
figure_numbers.append(number)
|
|
||||||
except (TypeError, ValueError):
|
|
||||||
pass
|
|
||||||
# pylint: disable=no-member
|
|
||||||
return numpy.unique(figure_numbers).tolist()
|
|
||||||
|
|
||||||
|
|
||||||
def _parse_figure_numbers(*args):
|
|
||||||
figure_numbers = []
|
|
||||||
for arg in args:
|
|
||||||
if isinstance(arg, (list, tuple, set)):
|
|
||||||
for val in arg:
|
|
||||||
figure_numbers.append(int(val))
|
|
||||||
elif isinstance(arg, int):
|
|
||||||
figure_numbers.append(arg)
|
|
||||||
elif arg == 'all':
|
|
||||||
figure_numbers = find_all_figure_numbers()
|
|
||||||
break
|
|
||||||
else:
|
|
||||||
raise TypeError('Only integers arguments accepted!')
|
|
||||||
|
|
||||||
if len(figure_numbers) == 0:
|
|
||||||
figure_numbers = find_all_figure_numbers()
|
|
||||||
return figure_numbers
|
|
||||||
|
|
||||||
|
|
||||||
def _show_figure(figure_numbers, command):
|
|
||||||
"""Sets the specified figure's show state.
|
|
||||||
|
|
||||||
Parameters
|
|
||||||
----------
|
|
||||||
figure_numbers: list of figure numbers
|
|
||||||
command: one of following commands:
|
|
||||||
SW_FORCEMINIMIZE:
|
|
||||||
Minimizes a window, even if the thread that owns the window is not
|
|
||||||
responding. This flag should only be used when minimizing windows
|
|
||||||
from a different thread.
|
|
||||||
SW_HIDE:
|
|
||||||
Hides the window and activates another window.
|
|
||||||
SW_MAXIMIZE:
|
|
||||||
Maximizes the specified window.
|
|
||||||
SW_MINIMIZE:
|
|
||||||
Minimizes the specified window and activates the next top-level window
|
|
||||||
in the Z order.
|
|
||||||
SW_RESTORE:
|
|
||||||
Activates and displays the window. If the window is minimized or
|
|
||||||
maximized, the system restores it to its original size and position.
|
|
||||||
An application should specify this flag when restoring a minimized
|
|
||||||
window.
|
|
||||||
SW_SHOW:
|
|
||||||
Activates the window and displays it in its current size and position.
|
|
||||||
SW_SHOWDEFAULT:
|
|
||||||
Sets the show state based on the SW_ value specified in the STARTUPINFO
|
|
||||||
structure passed to the CreateProcess function by the program that
|
|
||||||
started the application.
|
|
||||||
SW_SHOWMAXIMIZED:
|
|
||||||
Activates the window and displays it as a maximized window.
|
|
||||||
SW_SHOWMINIMIZED:
|
|
||||||
Activates the window and displays it as a minimized window.
|
|
||||||
SW_SHOWMINNOACTIVE:
|
|
||||||
Displays the window as a minimized window. This value is similar to
|
|
||||||
SW_SHOWMINIMIZED, except the window is not activated.
|
|
||||||
SW_SHOWNA:
|
|
||||||
Displays the window in its current size and position. This value is
|
|
||||||
similar to SW_SHOW, except the window is not activated.
|
|
||||||
SW_SHOWNOACTIVATE:
|
|
||||||
Displays a window in its most recent size and position. This value is
|
|
||||||
similar to SW_SHOWNORMAL, except the window is not actived.
|
|
||||||
SW_SHOWNORMAL:
|
|
||||||
Activates and displays a window. If the window is minimized or
|
|
||||||
maximized, the system restores it to its original size and position.
|
|
||||||
An application should specify this flag when displaying the window for
|
|
||||||
the first time.
|
|
||||||
|
|
||||||
"""
|
|
||||||
for number in _parse_figure_numbers(*figure_numbers):
|
|
||||||
for format_ in FIGURE_TITLE_FORMATS:
|
|
||||||
title = format_ + ' %d' % number
|
|
||||||
handle = FindWindow(None, title)
|
|
||||||
if not handle == 0:
|
|
||||||
BringWindowToTop(handle)
|
|
||||||
ShowWindow(handle, command)
|
|
||||||
|
|
||||||
|
|
||||||
def _show_windows(handles, command, redraw_now=False):
|
|
||||||
"""Sets the specified window's show state.
|
|
||||||
|
|
||||||
Parameters
|
|
||||||
----------
|
|
||||||
handles: list of window handles
|
|
||||||
command: one of following commands:
|
|
||||||
SW_FORCEMINIMIZE:
|
|
||||||
Minimizes a window, even if the thread that owns the window is not
|
|
||||||
responding. This flag should only be used when minimizing windows
|
|
||||||
from a different thread.
|
|
||||||
SW_HIDE:
|
|
||||||
Hides the window and activates another window.
|
|
||||||
SW_MAXIMIZE:
|
|
||||||
Maximizes the specified window.
|
|
||||||
SW_MINIMIZE:
|
|
||||||
Minimizes the specified window and activates the next top-level window
|
|
||||||
in the Z order.
|
|
||||||
SW_RESTORE:
|
|
||||||
Activates and displays the window. If the window is minimized or
|
|
||||||
maximized, the system restores it to its original size and position.
|
|
||||||
An application should specify this flag when restoring a minimized
|
|
||||||
window.
|
|
||||||
SW_SHOW:
|
|
||||||
Activates the window and displays it in its current size and position.
|
|
||||||
SW_SHOWDEFAULT:
|
|
||||||
Sets the show state based on the SW_ value specified in the STARTUPINFO
|
|
||||||
structure passed to the CreateProcess function by the program that
|
|
||||||
started the application.
|
|
||||||
SW_SHOWMAXIMIZED:
|
|
||||||
Activates the window and displays it as a maximized window.
|
|
||||||
SW_SHOWMINIMIZED:
|
|
||||||
Activates the window and displays it as a minimized window.
|
|
||||||
SW_SHOWMINNOACTIVE:
|
|
||||||
Displays the window as a minimized window. This value is similar to
|
|
||||||
SW_SHOWMINIMIZED, except the window is not activated.
|
|
||||||
SW_SHOWNA:
|
|
||||||
Displays the window in its current size and position. This value is
|
|
||||||
similar to SW_SHOW, except the window is not activated.
|
|
||||||
SW_SHOWNOACTIVATE:
|
|
||||||
Displays a window in its most recent size and position. This value is
|
|
||||||
similar to SW_SHOWNORMAL, except the window is not actived.
|
|
||||||
SW_SHOWNORMAL:
|
|
||||||
Activates and displays a window. If the window is minimized or
|
|
||||||
maximized, the system restores it to its original size and position.
|
|
||||||
An application should specify this flag when displaying the window for
|
|
||||||
the first time.
|
|
||||||
|
|
||||||
redraw_now :
|
|
||||||
|
|
||||||
"""
|
|
||||||
# pylint: disable=no-member
|
|
||||||
for handle in handles:
|
|
||||||
if not handle == 0:
|
|
||||||
BringWindowToTop(handle)
|
|
||||||
ShowWindow(handle, command)
|
|
||||||
if redraw_now:
|
|
||||||
rect = GetWindowRect(handle)
|
|
||||||
win32gui.RedrawWindow(handle, rect, None, win32con.RDW_UPDATENOW)
|
|
||||||
|
|
||||||
|
|
||||||
def keep(*figure_numbers):
|
|
||||||
"""Keeps figure windows of your choice and closes the rest.
|
|
||||||
|
|
||||||
Parameters
|
|
||||||
----------
|
|
||||||
figure_numbers : list of integers specifying which figures to keep.
|
|
||||||
|
|
||||||
Example:
|
|
||||||
--------
|
|
||||||
# keep only figures 1,2,3,5 and 7
|
|
||||||
>>> import pylab as p
|
|
||||||
>>> import wafo.fig as fig
|
|
||||||
>>> for ix in range(10):
|
|
||||||
... f = p.figure(ix)
|
|
||||||
>>> fig.keep( range(1,4), 5, 7)
|
|
||||||
|
|
||||||
or
|
|
||||||
fig.keep([range(1,4), 5, 7])
|
|
||||||
>>> fig.close()
|
|
||||||
|
|
||||||
See also
|
|
||||||
--------
|
|
||||||
fig.close
|
|
||||||
|
|
||||||
"""
|
|
||||||
figs2keep = []
|
|
||||||
for fig in figure_numbers:
|
|
||||||
if isinstance(fig, (list, tuple, set)):
|
|
||||||
for val in fig:
|
|
||||||
figs2keep.append(int(val))
|
|
||||||
elif isinstance(fig, int):
|
|
||||||
figs2keep.append(fig)
|
|
||||||
else:
|
|
||||||
raise TypeError('Only integers arguments accepted!')
|
|
||||||
|
|
||||||
if len(figs2keep) > 0:
|
|
||||||
allfigs = set(find_all_figure_numbers())
|
|
||||||
figs2delete = allfigs.difference(figs2keep)
|
|
||||||
close(figs2delete)
|
|
||||||
|
|
||||||
|
|
||||||
def close(*figure_numbers):
|
|
||||||
""" Close figure window(s)
|
|
||||||
|
|
||||||
Parameters
|
|
||||||
----------
|
|
||||||
figure_numbers : list of integers or string
|
|
||||||
specifying which figures to close (default 'all').
|
|
||||||
|
|
||||||
Examples
|
|
||||||
--------
|
|
||||||
>>> import pylab as p
|
|
||||||
>>> import wafo.fig as fig
|
|
||||||
>>> for ix in range(5):
|
|
||||||
... f = p.figure(ix)
|
|
||||||
>>> fig.close(3,4) # close figure 3 and 4
|
|
||||||
>>> fig.close('all') # close all remaining figures
|
|
||||||
|
|
||||||
or even simpler
|
|
||||||
fig.close() # close all remaining figures
|
|
||||||
|
|
||||||
See also
|
|
||||||
--------
|
|
||||||
fig.keep
|
|
||||||
|
|
||||||
"""
|
|
||||||
# pylint: disable=no-member
|
|
||||||
for handle in find_figure_handles(*figure_numbers):
|
|
||||||
if win32gui.SendMessage(handle, win32con.WM_CLOSE, 0, 0):
|
|
||||||
win32gui.SendMessage(handle, win32con.WM_DESTROY, 0, 0)
|
|
||||||
|
|
||||||
|
|
||||||
def restore(*figure_numbers):
|
|
||||||
"""Restore figures window size and position to its default value.
|
|
||||||
|
|
||||||
Parameters
|
|
||||||
----------
|
|
||||||
figure_numbers : list of integers or string
|
|
||||||
specifying which figures to restor (default 'all').
|
|
||||||
|
|
||||||
Description
|
|
||||||
-----------
|
|
||||||
RESTORE Activates and displays the window. If the window is minimized
|
|
||||||
or maximized, the system restores it to its original size and position.
|
|
||||||
|
|
||||||
Examples
|
|
||||||
---------
|
|
||||||
>>> import pylab as p
|
|
||||||
>>> import wafo.fig as fig
|
|
||||||
>>> for ix in range(5):
|
|
||||||
... f = p.figure(ix)
|
|
||||||
>>> fig.restore('all') #Restores all figures
|
|
||||||
>>> fig.restore() #same as restore('all')
|
|
||||||
>>> fig.restore(p.gcf().number) #Restores the current figure
|
|
||||||
>>> fig.restore(3) #Restores figure 3
|
|
||||||
>>> fig.restore([2, 4]) #Restores figures 2 and 4
|
|
||||||
|
|
||||||
or alternatively
|
|
||||||
fig.restore(2, 4)
|
|
||||||
>>> fig.close()
|
|
||||||
|
|
||||||
See also
|
|
||||||
--------
|
|
||||||
fig.close,
|
|
||||||
fig.keep
|
|
||||||
|
|
||||||
"""
|
|
||||||
SW_RESTORE = win32con.SW_RESTORE
|
|
||||||
# SW_RESTORE = win32con.SW_SHOWDEFAULT
|
|
||||||
# SW_RESTORE = win32con.SW_SHOWNORMAL
|
|
||||||
_show_figure(figure_numbers, SW_RESTORE)
|
|
||||||
|
|
||||||
|
|
||||||
def hide(*figure_numbers):
|
|
||||||
"""hide figure(s) window.
|
|
||||||
|
|
||||||
Parameters
|
|
||||||
----------
|
|
||||||
figure_numbers : list of integers or string
|
|
||||||
specifying which figures to hide (default 'all').
|
|
||||||
|
|
||||||
Examples:
|
|
||||||
--------
|
|
||||||
>>> import wafo.fig as fig
|
|
||||||
>>> import pylab as p
|
|
||||||
>>> for ix in range(5):
|
|
||||||
... f = p.figure(ix)
|
|
||||||
>>> fig.hide('all') #hides all unhidden figures
|
|
||||||
>>> fig.hide() #same as hide('all')
|
|
||||||
>>> fig.hide(p.gcf().number) #hides the current figure
|
|
||||||
>>> fig.hide(3) #hides figure 3
|
|
||||||
>>> fig.hide([2, 4]) #hides figures 2 and 4
|
|
||||||
|
|
||||||
or alternatively
|
|
||||||
fig.hide(2, 4)
|
|
||||||
>>> fig.restore(list(range(5)))
|
|
||||||
>>> fig.close()
|
|
||||||
|
|
||||||
See also
|
|
||||||
--------
|
|
||||||
fig.cycle,
|
|
||||||
fig.keep,
|
|
||||||
fig.restore
|
|
||||||
|
|
||||||
"""
|
|
||||||
_show_figure(figure_numbers, win32con.SW_HIDE)
|
|
||||||
|
|
||||||
|
|
||||||
def minimize(*figure_numbers):
|
|
||||||
"""Minimize figure(s) window size.
|
|
||||||
|
|
||||||
Parameters
|
|
||||||
----------
|
|
||||||
figure_numbers : list of integers or string
|
|
||||||
specifying which figures to minimize (default 'all').
|
|
||||||
|
|
||||||
Examples:
|
|
||||||
---------
|
|
||||||
>>> import wafo.fig as fig
|
|
||||||
>>> import pylab as p
|
|
||||||
>>> for ix in range(5):
|
|
||||||
... f = p.figure(ix)
|
|
||||||
>>> fig.minimize('all') #Minimizes all unhidden figures
|
|
||||||
>>> fig.minimize() #same as minimize('all')
|
|
||||||
>>> fig.minimize(p.gcf().number) #Minimizes the current figure
|
|
||||||
>>> fig.minimize(3) #Minimizes figure 3
|
|
||||||
>>> fig.minimize([2, 4]) #Minimizes figures 2 and 4
|
|
||||||
|
|
||||||
or alternatively
|
|
||||||
fig.minimize(2, 4)
|
|
||||||
>>> fig.close()
|
|
||||||
|
|
||||||
See also
|
|
||||||
--------
|
|
||||||
fig.cycle,
|
|
||||||
fig.keep,
|
|
||||||
fig.restore
|
|
||||||
|
|
||||||
"""
|
|
||||||
_show_figure(figure_numbers, win32con.SW_SHOWMINIMIZED)
|
|
||||||
|
|
||||||
|
|
||||||
def maximize(*figure_numbers):
|
|
||||||
"""Maximize figure(s) window size.
|
|
||||||
|
|
||||||
Parameters
|
|
||||||
----------
|
|
||||||
figure_numbers : list of integers or string
|
|
||||||
specifying which figures to maximize (default 'all').
|
|
||||||
|
|
||||||
Examples:
|
|
||||||
---------
|
|
||||||
>>> import pylab as p
|
|
||||||
>>> import wafo.fig as fig
|
|
||||||
>>> for ix in range(5):
|
|
||||||
... f = p.figure(ix)
|
|
||||||
>>> fig.maximize('all') #Maximizes all unhidden figures
|
|
||||||
>>> fig.maximize() #same as maximize('all')
|
|
||||||
>>> fig.maximize(p.gcf().number) #Maximizes the current figure
|
|
||||||
>>> fig.maximize(3) #Maximizes figure 3
|
|
||||||
>>> fig.maximize([2, 4]) #Maximizes figures 2 and 4
|
|
||||||
|
|
||||||
or alternatively
|
|
||||||
fig.maximize(2, 4)
|
|
||||||
>>> fig.close()
|
|
||||||
|
|
||||||
See also
|
|
||||||
--------
|
|
||||||
fig.cycle,
|
|
||||||
fig.keep,
|
|
||||||
fig.restore
|
|
||||||
|
|
||||||
"""
|
|
||||||
_show_figure(figure_numbers, win32con.SW_SHOWMAXIMIZED)
|
|
||||||
|
|
||||||
|
|
||||||
def pile(*figure_numbers, **kwds):
|
|
||||||
"""Pile figure windows.
|
|
||||||
|
|
||||||
Parameters
|
|
||||||
----------
|
|
||||||
figure_numbers : list of integers or string
|
|
||||||
specifying which figures to pile (default 'all').
|
|
||||||
kwds : dict with the following keys
|
|
||||||
position :
|
|
||||||
width :
|
|
||||||
height :
|
|
||||||
|
|
||||||
Description
|
|
||||||
-------------
|
|
||||||
PILE piles all open figure windows on top of eachother
|
|
||||||
with complete overlap. PILE(FIGS) can be used to specify which
|
|
||||||
figures that should be piled. Figures are not sorted when specified.
|
|
||||||
|
|
||||||
Example:
|
|
||||||
--------
|
|
||||||
>>> import pylab as p
|
|
||||||
>>> import wafo.fig as fig
|
|
||||||
>>> for ix in range(7):
|
|
||||||
... f = p.figure(ix)
|
|
||||||
>>> fig.pile() # pile all open figures
|
|
||||||
>>> fig.pile(range(1,4), 5, 7) # pile figure 1,2,3,5 and 7
|
|
||||||
>>> fig.close()
|
|
||||||
|
|
||||||
See also
|
|
||||||
--------
|
|
||||||
fig.cycle, fig.keep, fig.maximize, fig.restore,
|
|
||||||
fig.stack, fig.tile
|
|
||||||
|
|
||||||
"""
|
|
||||||
wnds = find_figure_handles(*figure_numbers)
|
|
||||||
numfigs = len(wnds)
|
|
||||||
if numfigs > 0:
|
|
||||||
screen_width, screen_height = _get_screen_size(wnds)
|
|
||||||
pos = kwds.get(
|
|
||||||
'position', (int(screen_width / 5), int(screen_height / 4)))
|
|
||||||
width = kwds.get('width', int(screen_width / 2.5))
|
|
||||||
height = kwds.get('height', int(screen_height / 2))
|
|
||||||
|
|
||||||
for wnd in wnds:
|
|
||||||
MoveWindow(wnd, pos[0], pos[1], width, height, 1)
|
|
||||||
BringWindowToTop(wnd)
|
|
||||||
|
|
||||||
|
|
||||||
def set_size(*figure_numbers, **kwds):
|
|
||||||
"""Set size for figure windows.
|
|
||||||
|
|
||||||
Parameters
|
|
||||||
----------
|
|
||||||
figure_numbers : list of integers or string
|
|
||||||
specifying which figures to pile (default 'all').
|
|
||||||
kwds : dict with the following keys
|
|
||||||
width :
|
|
||||||
height :
|
|
||||||
|
|
||||||
Description
|
|
||||||
-------------
|
|
||||||
Set size sets the size of all open figure windows. SET_SIZE(FIGS)
|
|
||||||
can be used to specify which figures that should be resized.
|
|
||||||
Figures are not sorted when specified.
|
|
||||||
|
|
||||||
Example:
|
|
||||||
--------
|
|
||||||
>>> import pylab as p
|
|
||||||
>>> import fig
|
|
||||||
>>> for ix in range(7):
|
|
||||||
... f = p.figure(ix)
|
|
||||||
>>> fig.set_size(7, width=150, height=100)
|
|
||||||
>>> fig.set_size(range(1,4), 5,width=250, height=170)
|
|
||||||
>>> fig.close()
|
|
||||||
|
|
||||||
See also
|
|
||||||
--------
|
|
||||||
fig.cycle, fig.keep, fig.maximize, fig.restore,
|
|
||||||
fig.stack, fig.tile
|
|
||||||
|
|
||||||
"""
|
|
||||||
handles = find_figure_handles(*figure_numbers)
|
|
||||||
numfigs = len(handles)
|
|
||||||
if numfigs > 0:
|
|
||||||
screen_width, screen_height = _get_screen_size(handles)
|
|
||||||
width = kwds.get('width', int(screen_width / 2.5))
|
|
||||||
height = kwds.get('height', int(screen_height / 2))
|
|
||||||
new_pos = kwds.get('position', None)
|
|
||||||
pos = new_pos
|
|
||||||
for handle in handles:
|
|
||||||
if not new_pos:
|
|
||||||
pos = get_window_position_and_size(handle)
|
|
||||||
MoveWindow(handle, pos[0], pos[1], width, height, 1)
|
|
||||||
BringWindowToTop(handle)
|
|
||||||
|
|
||||||
|
|
||||||
def stack(*figure_numbers, **kwds):
|
|
||||||
"""Stack figure windows.
|
|
||||||
|
|
||||||
Parameters
|
|
||||||
----------
|
|
||||||
figure_numbers : list of integers or string
|
|
||||||
specifying which figures to stack (default 'all').
|
|
||||||
kwds : dict with the following keys
|
|
||||||
figs_per_stack :
|
|
||||||
number of figures per stack (default depends on screenheight)
|
|
||||||
|
|
||||||
Description
|
|
||||||
-----------
|
|
||||||
STACK stacks all open figure windows on top of eachother
|
|
||||||
with maximum overlap. STACK(FIGS) can be used to specify which
|
|
||||||
figures that should be stacked. Figures are not sorted when specified.
|
|
||||||
|
|
||||||
Example:
|
|
||||||
--------
|
|
||||||
>>> import pylab as p
|
|
||||||
>>> import wafo.fig as fig
|
|
||||||
>>> for ix in range(7):
|
|
||||||
... f = p.figure(ix)
|
|
||||||
>>> fig.stack() # stack all open figures
|
|
||||||
>>> fig.stack(range(1,4), 5, 7) # stack figure 1,2,3,5 and 7
|
|
||||||
>>> fig.close()
|
|
||||||
|
|
||||||
See also
|
|
||||||
--------
|
|
||||||
fig.cycle, fig.keep, fig.maximize, fig.restore,
|
|
||||||
fig.pile, fig.tile
|
|
||||||
|
|
||||||
"""
|
|
||||||
wnds = find_figure_handles(*figure_numbers)
|
|
||||||
numfigs = len(wnds)
|
|
||||||
if numfigs > 0:
|
|
||||||
screenpos = get_screen_position_and_size(wnds)
|
|
||||||
y_step = 25
|
|
||||||
x_step = border = 5
|
|
||||||
|
|
||||||
figs_per_stack = kwds.get(
|
|
||||||
'figs_per_stack',
|
|
||||||
int(numpy.fix(0.7 * (screenpos[3] - border) / y_step)))
|
|
||||||
|
|
||||||
for iy in range(numfigs):
|
|
||||||
pos = get_window_position_and_size(wnds[iy])
|
|
||||||
# print('[x, y, w, h] = ', pos)
|
|
||||||
ix = iy % figs_per_stack
|
|
||||||
ypos = int(screenpos[1] + ix * y_step + border)
|
|
||||||
xpos = int(screenpos[0] + ix * x_step + border)
|
|
||||||
MoveWindow(wnds[iy], xpos, ypos, pos[2], pos[3], 1)
|
|
||||||
BringWindowToTop(wnds[iy])
|
|
||||||
|
|
||||||
|
|
||||||
def tile(*figure_numbers, **kwds):
|
|
||||||
"""Tile figure windows.
|
|
||||||
|
|
||||||
Parameters
|
|
||||||
----------
|
|
||||||
figure_numbers : list of integers or string
|
|
||||||
specifying which figures to tile (default 'all').
|
|
||||||
kwds : dict with key pairs
|
|
||||||
specifying how many pairs of figures that are tiled at a time
|
|
||||||
|
|
||||||
Description
|
|
||||||
-----------
|
|
||||||
TILE places all open figure windows around on the screen with no
|
|
||||||
overlap. TILE(FIGS) can be used to specify which figures that
|
|
||||||
should be tiled. Figures are not sorted when specified.
|
|
||||||
|
|
||||||
Example:
|
|
||||||
--------
|
|
||||||
>>> import pylab as p
|
|
||||||
>>> import wafo.fig as fig
|
|
||||||
>>> for ix in range(7):
|
|
||||||
... f = p.figure(ix)
|
|
||||||
>>> fig.tile() # tile all open figures
|
|
||||||
>>> fig.tile( range(1,4), 5, 7) # tile figure 1,2,3,5 and 7
|
|
||||||
>>> fig.tile(range(1,11), pairs=2) # tile figure 1 to 10 two at a time
|
|
||||||
>>> fig.tile(range(1,11), pairs=3) # tile figure 1 to 10 three at a time
|
|
||||||
>>> fig.close()
|
|
||||||
|
|
||||||
See also
|
|
||||||
--------
|
|
||||||
fig.cycle, fig.keep, fig.maximize, fig.minimize
|
|
||||||
fig.restore, fig.pile, fig.stack
|
|
||||||
|
|
||||||
"""
|
|
||||||
wnds = find_figure_handles(*figure_numbers)
|
|
||||||
|
|
||||||
nfigs = len(wnds)
|
|
||||||
# Number of windows.
|
|
||||||
|
|
||||||
if nfigs > 0:
|
|
||||||
nfigspertile = kwds.get('pairs', nfigs)
|
|
||||||
|
|
||||||
ceil = numpy.ceil
|
|
||||||
sqrt = numpy.sqrt
|
|
||||||
maximum = numpy.maximum
|
|
||||||
|
|
||||||
nlayers = int(ceil(nfigs / nfigspertile))
|
|
||||||
|
|
||||||
# Number of figures horisontally.
|
|
||||||
nh = maximum(int(ceil(sqrt(nfigspertile))), 2)
|
|
||||||
# Number of figures vertically.
|
|
||||||
nv = maximum(int(ceil(nfigspertile / nh)), 2)
|
|
||||||
|
|
||||||
screenpos = get_screen_position_and_size(wnds)
|
|
||||||
screen_width, screen_heigth = screenpos[2:4]
|
|
||||||
|
|
||||||
hspc = 10 # Horisontal space.
|
|
||||||
topspc = 20 # Space above top figure.
|
|
||||||
medspc = 10 # Space between figures.
|
|
||||||
botspc = 20 # Space below bottom figure.
|
|
||||||
|
|
||||||
figwid = (screen_width - (nh + 1) * hspc) / nh
|
|
||||||
fighgt = (screen_heigth - (topspc + botspc) - (nv - 1) * medspc) / nv
|
|
||||||
|
|
||||||
figwid = int(numpy.round(figwid))
|
|
||||||
fighgt = int(numpy.round(fighgt))
|
|
||||||
|
|
||||||
idx = 0
|
|
||||||
for unused_ix in range(nlayers):
|
|
||||||
for row in range(nv):
|
|
||||||
figtop = int(screenpos[1] + topspc + row * (fighgt + medspc))
|
|
||||||
for col in range(nh):
|
|
||||||
if (row) * nh + col < nfigspertile:
|
|
||||||
if idx < nfigs:
|
|
||||||
figlft = int(
|
|
||||||
screenpos[0] + (col + 1) * hspc + col * figwid)
|
|
||||||
fighnd = wnds[idx]
|
|
||||||
MoveWindow(fighnd, figlft, figtop, figwid, fighgt,
|
|
||||||
1)
|
|
||||||
# Set position.
|
|
||||||
BringWindowToTop(fighnd)
|
|
||||||
idx += 1
|
|
||||||
|
|
||||||
|
|
||||||
class _CycleGenerator(object):
|
|
||||||
|
|
||||||
"""Cycle through figure windows.
|
|
||||||
|
|
||||||
Parameters
|
|
||||||
----------
|
|
||||||
figure_numbers : list of integers or string
|
|
||||||
specifying which figures to cycle through (default 'all').
|
|
||||||
kwds : dict with the following keys
|
|
||||||
pairs : number of figures to cycle in pairs (default 1)
|
|
||||||
maximize: If True maximize figure when viewing (default False)
|
|
||||||
interval : pause interval in seconds
|
|
||||||
|
|
||||||
Description
|
|
||||||
-----------
|
|
||||||
CYCLE brings up all open figure in ascending order and pauses after
|
|
||||||
each figure. Press escape to quit cycling, backspace to display previous
|
|
||||||
figure(s) and press any other key to display next figure(s)
|
|
||||||
When done, the figures are sorted in ascending order.
|
|
||||||
|
|
||||||
CYCLE(maximize=True) does the same thing, except figures are maximized.
|
|
||||||
CYCLE(pairs=2) cycle through all figures in pairs of 2.
|
|
||||||
|
|
||||||
Examples:
|
|
||||||
>>> import pylab as p
|
|
||||||
>>> import wafo.fig as fig
|
|
||||||
>>> for ix in range(4):
|
|
||||||
... f = p.figure(ix)
|
|
||||||
|
|
||||||
fig.cycle(range(3), interval=1) # Cycle trough figure 0 to 2
|
|
||||||
|
|
||||||
# Cycle trough figure 0 to 2 with figures maximized
|
|
||||||
fig.cycle(range(3), maximize=True, interval=1)
|
|
||||||
fig.cycle(interval=1) # Cycle through all figures one at a time
|
|
||||||
fig.tile(pairs=2, interval=1)
|
|
||||||
fig.cycle(pairs=2, interval=2) # Cycle through all figures two at a time
|
|
||||||
|
|
||||||
fig.cycle(pairs=2) # Manually cycle through all figures two at a time
|
|
||||||
>>> fig.close()
|
|
||||||
|
|
||||||
See also
|
|
||||||
--------
|
|
||||||
fig.keep, fig.maximize, fig.restore, fig.pile,
|
|
||||||
fig.stack, fig.tile
|
|
||||||
|
|
||||||
"""
|
|
||||||
escape_key = chr(27)
|
|
||||||
backspace_key = chr(8)
|
|
||||||
|
|
||||||
def __init__(self, **kwds):
|
|
||||||
self.dialog = None
|
|
||||||
maximize = kwds.get('maximize', False)
|
|
||||||
pairs = kwds.get('pairs', 1)
|
|
||||||
self.interval = kwds.get('interval', 'user_defined')
|
|
||||||
self.nfigspercycle = 1
|
|
||||||
if maximize:
|
|
||||||
self.command = win32con.SW_SHOWMAXIMIZED
|
|
||||||
else:
|
|
||||||
self.command = win32con.SW_SHOWNORMAL
|
|
||||||
if pairs is not None:
|
|
||||||
self.nfigspercycle = pairs
|
|
||||||
|
|
||||||
def _set_options(self, kwds):
|
|
||||||
self.__init__(**kwds)
|
|
||||||
|
|
||||||
def _iterate(self, handles):
|
|
||||||
i = 0
|
|
||||||
numfigs = len(handles)
|
|
||||||
self.dialog = _get_cycle_dialog(parent=None, interval=self.interval)
|
|
||||||
while 0 <= i and i < numfigs:
|
|
||||||
iu = min(i + self.nfigspercycle, numfigs)
|
|
||||||
yield handles[i:iu]
|
|
||||||
i = self.next_index(i)
|
|
||||||
self.dialog.Destroy()
|
|
||||||
raise StopIteration
|
|
||||||
|
|
||||||
def next_index(self, i):
|
|
||||||
result = self.dialog.ShowModal()
|
|
||||||
if result == wx.ID_FORWARD:
|
|
||||||
i += self.nfigspercycle
|
|
||||||
elif result == wx.ID_BACKWARD:
|
|
||||||
i -= self.nfigspercycle
|
|
||||||
else:
|
|
||||||
i = -1
|
|
||||||
return i
|
|
||||||
|
|
||||||
def __call__(self, *figure_numbers, **kwds):
|
|
||||||
handles = find_figure_handles(*figure_numbers)
|
|
||||||
numfigs = len(handles)
|
|
||||||
if numfigs > 0:
|
|
||||||
self._set_options(kwds)
|
|
||||||
for handle in self._iterate(handles):
|
|
||||||
_show_windows(handle, self.command, redraw_now=True)
|
|
||||||
|
|
||||||
_show_windows(handles, win32con.SW_SHOWNORMAL)
|
|
||||||
|
|
||||||
cycle = _CycleGenerator()
|
|
||||||
|
|
||||||
|
|
||||||
if __name__ == '__main__':
|
|
||||||
from wafo.testing import test_docstrings
|
|
||||||
import matplotlib
|
|
||||||
matplotlib.interactive(True)
|
|
||||||
test_docstrings(__file__)
|
|
||||||
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
@ -1,538 +0,0 @@
|
|||||||
"""
|
|
||||||
Created on 20. aug. 2015
|
|
||||||
|
|
||||||
@author: pab
|
|
||||||
"""
|
|
||||||
from __future__ import division
|
|
||||||
from collections import namedtuple
|
|
||||||
import warnings
|
|
||||||
import numdifftools as nd
|
|
||||||
import numdifftools.nd_algopy as nda
|
|
||||||
from numdifftools.extrapolation import dea3
|
|
||||||
from numdifftools.limits import Limit
|
|
||||||
import numpy as np
|
|
||||||
from numpy import linalg
|
|
||||||
from numpy.polynomial.chebyshev import chebval, Chebyshev
|
|
||||||
from numpy.polynomial import polynomial
|
|
||||||
from wafo.misc import piecewise, findcross, ecross
|
|
||||||
|
|
||||||
_FINFO = np.finfo(float)
|
|
||||||
EPS = _FINFO.eps
|
|
||||||
_EPS = EPS
|
|
||||||
_TINY = _FINFO.tiny
|
|
||||||
_HUGE = _FINFO.max
|
|
||||||
|
|
||||||
|
|
||||||
def _assert(cond, msg):
|
|
||||||
if not cond:
|
|
||||||
raise ValueError(msg)
|
|
||||||
|
|
||||||
|
|
||||||
def _assert_warn(cond, msg):
|
|
||||||
if not cond:
|
|
||||||
warnings.warn(msg)
|
|
||||||
|
|
||||||
|
|
||||||
class PolyBasis(object):
|
|
||||||
@staticmethod
|
|
||||||
def _derivative(c, m):
|
|
||||||
return polynomial.polyder(c, m)
|
|
||||||
|
|
||||||
@staticmethod
|
|
||||||
def eval(t, c):
|
|
||||||
return polynomial.polyval(t, c)
|
|
||||||
|
|
||||||
@staticmethod
|
|
||||||
def _coefficients(k):
|
|
||||||
c = np.zeros(k + 1)
|
|
||||||
c[k] = 1
|
|
||||||
return c
|
|
||||||
|
|
||||||
def derivative(self, t, k, n=1):
|
|
||||||
c = self._coefficients(k)
|
|
||||||
d_c = self._derivative(c, m=n)
|
|
||||||
return self.eval(t, d_c)
|
|
||||||
|
|
||||||
def __call__(self, t, k):
|
|
||||||
return t**k
|
|
||||||
|
|
||||||
|
|
||||||
poly_basis = PolyBasis()
|
|
||||||
|
|
||||||
|
|
||||||
class ChebyshevBasis(PolyBasis):
|
|
||||||
|
|
||||||
@staticmethod
|
|
||||||
def _derivative(c, m):
|
|
||||||
cheb = Chebyshev(c)
|
|
||||||
dcheb = cheb.deriv(m=m)
|
|
||||||
return dcheb.coef
|
|
||||||
|
|
||||||
@staticmethod
|
|
||||||
def eval(t, c):
|
|
||||||
return chebval(t, c)
|
|
||||||
|
|
||||||
def __call__(self, t, k):
|
|
||||||
c = self._coefficients(k)
|
|
||||||
return self.eval(t, c)
|
|
||||||
|
|
||||||
|
|
||||||
chebyshev_basis = ChebyshevBasis()
|
|
||||||
|
|
||||||
|
|
||||||
def richardson(q_val, k):
|
|
||||||
# license BSD
|
|
||||||
# Richardson extrapolation with parameter estimation
|
|
||||||
c = np.real((q_val[k - 1] - q_val[k - 2]) / (q_val[k] - q_val[k - 1])) - 1.
|
|
||||||
# The lower bound 0.07 admits the singularity x.^-0.9
|
|
||||||
c = max(c, 0.07)
|
|
||||||
return q_val[k] + (q_val[k] - q_val[k - 1]) / c
|
|
||||||
|
|
||||||
|
|
||||||
def evans_webster_weights(omega, g, d_g, x, basis, *args, **kwds):
|
|
||||||
|
|
||||||
def psi(t, k):
|
|
||||||
return d_g(t, *args, **kwds) * basis(t, k)
|
|
||||||
|
|
||||||
j_w = 1j * omega
|
|
||||||
n = len(x)
|
|
||||||
a_matrix = np.zeros((n, n), dtype=complex)
|
|
||||||
rhs = np.zeros((n,), dtype=complex)
|
|
||||||
|
|
||||||
dbasis = basis.derivative
|
|
||||||
lim_g = Limit(g)
|
|
||||||
b_1 = np.exp(j_w * lim_g(1, *args, **kwds))
|
|
||||||
if np.isnan(b_1):
|
|
||||||
b_1 = 0.0
|
|
||||||
a_1 = np.exp(j_w * lim_g(-1, *args, **kwds))
|
|
||||||
if np.isnan(a_1):
|
|
||||||
a_1 = 0.0
|
|
||||||
|
|
||||||
lim_psi = Limit(psi)
|
|
||||||
for k in range(n):
|
|
||||||
rhs[k] = basis(1, k) * b_1 - basis(-1, k) * a_1
|
|
||||||
a_matrix[k] = (dbasis(x, k, n=1) + j_w * lim_psi(x, k))
|
|
||||||
|
|
||||||
solution = linalg.lstsq(a_matrix, rhs)
|
|
||||||
return solution[0]
|
|
||||||
|
|
||||||
|
|
||||||
def osc_weights(omega, g, d_g, x, basis, a_b, *args, **kwds):
|
|
||||||
def _g(t):
|
|
||||||
return g(scale * t + offset, *args, **kwds)
|
|
||||||
|
|
||||||
def _d_g(t):
|
|
||||||
return scale * d_g(scale * t + offset, *args, **kwds)
|
|
||||||
|
|
||||||
w = []
|
|
||||||
|
|
||||||
for a, b in zip(a_b[::2], a_b[1::2]):
|
|
||||||
scale = (b - a) / 2
|
|
||||||
offset = (a + b) / 2
|
|
||||||
|
|
||||||
w.append(evans_webster_weights(omega, _g, _d_g, x, basis))
|
|
||||||
|
|
||||||
return np.asarray(w).ravel()
|
|
||||||
|
|
||||||
|
|
||||||
class _Integrator(object):
|
|
||||||
info = namedtuple('info', ['error_estimate', 'n'])
|
|
||||||
|
|
||||||
def __init__(self, f, g, dg=None, a=-1, b=1, basis=chebyshev_basis, s=1,
|
|
||||||
precision=10, endpoints=True, full_output=False):
|
|
||||||
self.f = f
|
|
||||||
self.g = g
|
|
||||||
self.dg = nd.Derivative(g) if dg is None else dg
|
|
||||||
self.basis = basis
|
|
||||||
self.a = a
|
|
||||||
self.b = b
|
|
||||||
self.s = s
|
|
||||||
self.endpoints = endpoints
|
|
||||||
self.precision = precision
|
|
||||||
self.full_output = full_output
|
|
||||||
|
|
||||||
|
|
||||||
class QuadOsc(_Integrator):
|
|
||||||
def __init__(self, f, g, dg=None, a=-1, b=1, basis=chebyshev_basis, s=15,
|
|
||||||
precision=10, endpoints=False, full_output=False, maxiter=17):
|
|
||||||
self.maxiter = maxiter
|
|
||||||
super(QuadOsc, self).__init__(f, g, dg=dg, a=a, b=b, basis=basis, s=s,
|
|
||||||
precision=precision, endpoints=endpoints,
|
|
||||||
full_output=full_output)
|
|
||||||
|
|
||||||
@staticmethod
|
|
||||||
def _change_interval_to_0_1(f, g, d_g, a, _b):
|
|
||||||
def f_01(t, *args, **kwds):
|
|
||||||
den = 1 - t
|
|
||||||
return f(a + t / den, *args, **kwds) / den ** 2
|
|
||||||
|
|
||||||
def g_01(t, *args, **kwds):
|
|
||||||
return g(a + t / (1 - t), *args, **kwds)
|
|
||||||
|
|
||||||
def d_g_01(t, *args, **kwds):
|
|
||||||
den = 1 - t
|
|
||||||
return d_g(a + t / den, *args, **kwds) / den ** 2
|
|
||||||
return f_01, g_01, d_g_01, 0., 1.
|
|
||||||
|
|
||||||
@staticmethod
|
|
||||||
def _change_interval_to_m1_0(f, g, d_g, _a, b):
|
|
||||||
def f_m10(t, *args, **kwds):
|
|
||||||
den = 1 + t
|
|
||||||
return f(b + t / den, *args, **kwds) / den ** 2
|
|
||||||
|
|
||||||
def g_m10(t, *args, **kwds):
|
|
||||||
return g(b + t / (1 + t), *args, **kwds)
|
|
||||||
|
|
||||||
def d_g_m10(t, *args, **kwds):
|
|
||||||
den = 1 + t
|
|
||||||
return d_g(b + t / den, *args, **kwds) / den ** 2
|
|
||||||
return f_m10, g_m10, d_g_m10, -1.0, 0.0
|
|
||||||
|
|
||||||
@staticmethod
|
|
||||||
def _change_interval_to_m1_1(f, g, d_g, _a, _b):
|
|
||||||
def f_m11(t, *args, **kwds):
|
|
||||||
den = (1 - t**2)
|
|
||||||
return f(t / den, *args, **kwds) * (1 + t**2) / den ** 2
|
|
||||||
|
|
||||||
def g_m11(t, *args, **kwds):
|
|
||||||
den = (1 - t**2)
|
|
||||||
return g(t / den, *args, **kwds)
|
|
||||||
|
|
||||||
def d_g_m11(t, *args, **kwds):
|
|
||||||
den = (1 - t**2)
|
|
||||||
return d_g(t / den, *args, **kwds) * (1 + t**2) / den ** 2
|
|
||||||
return f_m11, g_m11, d_g_m11, -1., 1.
|
|
||||||
|
|
||||||
def _get_functions(self):
|
|
||||||
a, b = self.a, self.b
|
|
||||||
reverse = np.real(a) > np.real(b)
|
|
||||||
if reverse:
|
|
||||||
a, b = b, a
|
|
||||||
f, g, dg = self.f, self.g, self.dg
|
|
||||||
|
|
||||||
if a == b:
|
|
||||||
pass
|
|
||||||
elif np.isinf(a) | np.isinf(b):
|
|
||||||
# Check real limits
|
|
||||||
if ~np.isreal(a) | ~np.isreal(b) | np.isnan(a) | np.isnan(b):
|
|
||||||
raise ValueError('Infinite intervals must be real.')
|
|
||||||
# Change of variable
|
|
||||||
if np.isfinite(a) & np.isinf(b):
|
|
||||||
f, g, dg, a, b = self._change_interval_to_0_1(f, g, dg, a, b)
|
|
||||||
elif np.isinf(a) & np.isfinite(b):
|
|
||||||
f, g, dg, a, b = self._change_interval_to_m1_0(f, g, dg, a, b)
|
|
||||||
else: # -inf to inf
|
|
||||||
f, g, dg, a, b = self._change_interval_to_m1_1(f, g, dg, a, b)
|
|
||||||
|
|
||||||
return f, g, dg, a, b, reverse
|
|
||||||
|
|
||||||
def __call__(self, omega, *args, **kwds):
|
|
||||||
f, g, dg, a, b, reverse = self._get_functions()
|
|
||||||
|
|
||||||
val, err = self._quad_osc(f, g, dg, a, b, omega, *args, **kwds)
|
|
||||||
|
|
||||||
if reverse:
|
|
||||||
val = -val
|
|
||||||
if self.full_output:
|
|
||||||
return val, err
|
|
||||||
return val
|
|
||||||
|
|
||||||
@staticmethod
|
|
||||||
def _get_best_estimate(k, q_0, q_1, q_2):
|
|
||||||
if k >= 5:
|
|
||||||
q_v = np.hstack((q_0[k], q_1[k], q_2[k]))
|
|
||||||
q_w = np.hstack((q_0[k - 1], q_1[k - 1], q_2[k - 1]))
|
|
||||||
elif k >= 3:
|
|
||||||
q_v = np.hstack((q_0[k], q_1[k]))
|
|
||||||
q_w = np.hstack((q_0[k - 1], q_1[k - 1]))
|
|
||||||
else:
|
|
||||||
q_v = np.atleast_1d(q_0[k])
|
|
||||||
q_w = q_0[k - 1]
|
|
||||||
errors = np.atleast_1d(abs(q_v - q_w))
|
|
||||||
j = np.nanargmin(errors)
|
|
||||||
return q_v[j], errors[j]
|
|
||||||
|
|
||||||
def _extrapolate(self, k, q_0, q_1, q_2):
|
|
||||||
if k >= 4:
|
|
||||||
q_1[k] = dea3(q_0[k - 2], q_0[k - 1], q_0[k])[0]
|
|
||||||
q_2[k] = dea3(q_1[k - 2], q_1[k - 1], q_1[k])[0]
|
|
||||||
elif k >= 2:
|
|
||||||
q_1[k] = dea3(q_0[k - 2], q_0[k - 1], q_0[k])[0]
|
|
||||||
# # Richardson extrapolation
|
|
||||||
# if k >= 4:
|
|
||||||
# q_1[k] = richardson(q_0, k)
|
|
||||||
# q_2[k] = richardson(q_1, k)
|
|
||||||
# elif k >= 2:
|
|
||||||
# q_1[k] = richardson(q_0, k)
|
|
||||||
q, err = self._get_best_estimate(k, q_0, q_1, q_2)
|
|
||||||
return q, err
|
|
||||||
|
|
||||||
def _quad_osc(self, f, g, dg, a, b, omega, *args, **kwds):
|
|
||||||
if a == b:
|
|
||||||
q_val = b - a
|
|
||||||
err = np.abs(b - a)
|
|
||||||
return q_val, err
|
|
||||||
|
|
||||||
abseps = 10**-self.precision
|
|
||||||
max_iter = self.maxiter
|
|
||||||
basis = self.basis
|
|
||||||
if self.endpoints:
|
|
||||||
x_n = chebyshev_extrema(self.s)
|
|
||||||
else:
|
|
||||||
x_n = chebyshev_roots(self.s)
|
|
||||||
# x_n = tanh_sinh_open_nodes(self.s)
|
|
||||||
|
|
||||||
# One interval
|
|
||||||
hh = (b - a) / 2
|
|
||||||
x = (a + b) / 2 + hh * x_n # Nodes
|
|
||||||
|
|
||||||
dtype = complex
|
|
||||||
val0 = np.zeros((max_iter, 1), dtype=dtype) # Quadrature
|
|
||||||
val1 = np.zeros((max_iter, 1), dtype=dtype) # First extrapolation
|
|
||||||
val2 = np.zeros((max_iter, 1), dtype=dtype) # Second extrapolation
|
|
||||||
|
|
||||||
lim_f = Limit(f)
|
|
||||||
a_b = np.hstack([a, b])
|
|
||||||
wq = osc_weights(omega, g, dg, x_n, basis, a_b, *args, **kwds)
|
|
||||||
val0[0] = hh * np.sum(wq * lim_f(x, *args, **kwds))
|
|
||||||
|
|
||||||
# Successive bisection of intervals
|
|
||||||
nq = len(x_n)
|
|
||||||
n = nq
|
|
||||||
for k in range(1, max_iter):
|
|
||||||
n += nq * 2**k
|
|
||||||
|
|
||||||
hh = hh / 2
|
|
||||||
x = np.hstack([x + a, x + b]) / 2
|
|
||||||
a_b = np.hstack([a_b + a, a_b + b]) / 2
|
|
||||||
wq = osc_weights(omega, g, dg, x_n, basis, a_b, *args, **kwds)
|
|
||||||
|
|
||||||
val0[k] = hh * np.sum(wq * lim_f(x, *args, **kwds))
|
|
||||||
|
|
||||||
q_val, err = self._extrapolate(k, val0, val1, val2)
|
|
||||||
|
|
||||||
converged = (err <= abseps) | ~np.isfinite(q_val)
|
|
||||||
if converged:
|
|
||||||
break
|
|
||||||
_assert_warn(converged, 'Max number of iterations reached '
|
|
||||||
'without convergence.')
|
|
||||||
_assert_warn(np.isfinite(q_val),
|
|
||||||
'Integral approximation is Infinite or NaN.')
|
|
||||||
|
|
||||||
# The error estimate should not be zero
|
|
||||||
err += 2 * np.finfo(q_val).eps
|
|
||||||
return q_val, self.info(err, n)
|
|
||||||
|
|
||||||
|
|
||||||
def adaptive_levin_points(m, delta):
|
|
||||||
m_1 = m - 1
|
|
||||||
prm = 0.5
|
|
||||||
while prm * m_1 / delta >= 1:
|
|
||||||
delta = 2 * delta
|
|
||||||
k = np.arange(m)
|
|
||||||
x = piecewise([k < prm * m_1, k == np.ceil(prm * m_1)],
|
|
||||||
[-1 + k / delta, 0 * k, 1 - (m_1 - k) / delta])
|
|
||||||
return x
|
|
||||||
|
|
||||||
|
|
||||||
def open_levin_points(m, delta):
|
|
||||||
return adaptive_levin_points(m + 2, delta)[1:-1]
|
|
||||||
|
|
||||||
|
|
||||||
def chebyshev_extrema(m, delta=None):
|
|
||||||
k = np.arange(m)
|
|
||||||
x = np.cos(k * np.pi / (m - 1))
|
|
||||||
return x
|
|
||||||
|
|
||||||
|
|
||||||
def tanh_sinh_nodes(m, delta=None, tol=_EPS):
|
|
||||||
tmax = np.arcsinh(np.arctanh(1 - _EPS) * 2 / np.pi)
|
|
||||||
# tmax = 3.18
|
|
||||||
m_1 = int(np.floor(-np.log2(tmax / max(m - 1, 1)))) - 1
|
|
||||||
h = 2.0**-m_1
|
|
||||||
t = np.arange((m + 1) // 2 + 1) * h
|
|
||||||
x = np.tanh(np.pi / 2 * np.sinh(t))
|
|
||||||
k = np.flatnonzero(np.abs(x - 1) <= 10 * tol)
|
|
||||||
y = x[:k[0] + 1] if len(k) else x
|
|
||||||
return np.hstack((-y[:0:-1], y))
|
|
||||||
|
|
||||||
|
|
||||||
def tanh_sinh_open_nodes(m, delta=None, tol=_EPS):
|
|
||||||
return tanh_sinh_nodes(m + 1, delta, tol)[1:-1]
|
|
||||||
|
|
||||||
|
|
||||||
def chebyshev_roots(m, delta=None):
|
|
||||||
k = np.arange(1, 2 * m, 2) * 0.5
|
|
||||||
x = np.cos(k * np.pi / m)
|
|
||||||
return x
|
|
||||||
|
|
||||||
|
|
||||||
class AdaptiveLevin(_Integrator):
|
|
||||||
"""Return integral for the Levin-type and adaptive Levin-type methods"""
|
|
||||||
|
|
||||||
@staticmethod
|
|
||||||
def _a_levin(omega, f, g, d_g, x, s, basis, *args, **kwds):
|
|
||||||
|
|
||||||
def psi(t, k):
|
|
||||||
return d_g(t, *args, **kwds) * basis(t, k)
|
|
||||||
|
|
||||||
j_w = 1j * omega
|
|
||||||
nu = np.ones((len(x),), dtype=int)
|
|
||||||
nu[0] = nu[-1] = s
|
|
||||||
S = np.cumsum(np.hstack((nu, 0)))
|
|
||||||
S[-1] = 0
|
|
||||||
n = int(S[-2])
|
|
||||||
a_matrix = np.zeros((n, n), dtype=complex)
|
|
||||||
rhs = np.zeros((n,))
|
|
||||||
dff = Limit(nda.Derivative(f))
|
|
||||||
d_psi = Limit(nda.Derivative(psi))
|
|
||||||
dbasis = basis.derivative
|
|
||||||
for r, t in enumerate(x):
|
|
||||||
for j in range(S[r - 1], S[r]):
|
|
||||||
order = ((j - S[r - 1]) % nu[r]) # derivative order
|
|
||||||
dff.fun.n = order
|
|
||||||
rhs[j] = dff(t, *args, **kwds)
|
|
||||||
d_psi.fun.n = order
|
|
||||||
for k in range(n):
|
|
||||||
a_matrix[j, k] = (dbasis(t, k, n=order + 1) +
|
|
||||||
j_w * d_psi(t, k))
|
|
||||||
k1 = np.flatnonzero(1 - np.isfinite(rhs))
|
|
||||||
if k1.size > 0: # Remove singularities
|
|
||||||
warnings.warn('Singularities detected! ')
|
|
||||||
a_matrix[k1] = 0
|
|
||||||
rhs[k1] = 0
|
|
||||||
solution = linalg.lstsq(a_matrix, rhs)
|
|
||||||
v = basis.eval([-1, 1], solution[0])
|
|
||||||
|
|
||||||
lim_g = Limit(g)
|
|
||||||
g_b = np.exp(j_w * lim_g(1, *args, **kwds))
|
|
||||||
if np.isnan(g_b):
|
|
||||||
g_b = 0
|
|
||||||
g_a = np.exp(j_w * lim_g(-1, *args, **kwds))
|
|
||||||
if np.isnan(g_a):
|
|
||||||
g_a = 0
|
|
||||||
return v[1] * g_b - v[0] * g_a
|
|
||||||
|
|
||||||
def _get_integration_limits(self, omega, args, kwds):
|
|
||||||
a, b = self.a, self.b
|
|
||||||
M = 30
|
|
||||||
ab = [a]
|
|
||||||
scale = (b - a) / 2
|
|
||||||
n = 30
|
|
||||||
x = np.linspace(a, b, n + 1)
|
|
||||||
dg_x = np.asarray([scale * omega * self.dg(xi, *args, **kwds)
|
|
||||||
for xi in x])
|
|
||||||
i10 = findcross(dg_x, M)
|
|
||||||
i1 = findcross(dg_x, 1)
|
|
||||||
i0 = findcross(dg_x, 0)
|
|
||||||
im1 = findcross(dg_x, -1)
|
|
||||||
im10 = findcross(dg_x, -M)
|
|
||||||
x10 = ecross(x, dg_x, i10, M) if len(i10) else ()
|
|
||||||
x1 = ecross(x, dg_x, i1, 1) if len(i1) else ()
|
|
||||||
x0 = ecross(x, dg_x, i0, 0) if len(i0) else ()
|
|
||||||
xm1 = ecross(x, dg_x, im1, -1) if len(im1) else ()
|
|
||||||
xm10 = ecross(x, dg_x, im10, -M) if len(im10) else ()
|
|
||||||
|
|
||||||
for i in np.unique(np.hstack((x10, x1, x0, xm1, xm10))):
|
|
||||||
if x[0] < i < x[n]:
|
|
||||||
ab.append(i)
|
|
||||||
ab.append(b)
|
|
||||||
return ab
|
|
||||||
|
|
||||||
def __call__(self, omega, *args, **kwds):
|
|
||||||
ab = self._get_integration_limits(omega, args, kwds)
|
|
||||||
s = self.s
|
|
||||||
val = 0
|
|
||||||
n = 0
|
|
||||||
err = 0
|
|
||||||
for ai, bi in zip(ab[:-1], ab[1:]):
|
|
||||||
vali, infoi = self._QaL(s, ai, bi, omega, *args, **kwds)
|
|
||||||
val += vali
|
|
||||||
err += infoi.error_estimate
|
|
||||||
n += infoi.n
|
|
||||||
if self.full_output:
|
|
||||||
info = self.info(err, n)
|
|
||||||
return val, info
|
|
||||||
return val
|
|
||||||
|
|
||||||
@staticmethod
|
|
||||||
def _get_num_points(s, prec, betam):
|
|
||||||
return 1 if s > 1 else int(prec / max(np.log10(betam + 1), 1) + 1)
|
|
||||||
|
|
||||||
def _QaL(self, s, a, b, omega, *args, **kwds):
|
|
||||||
"""if s>1,the integral is computed by Q_s^L"""
|
|
||||||
scale = (b - a) / 2
|
|
||||||
offset = (a + b) / 2
|
|
||||||
prec = self.precision # desired precision
|
|
||||||
|
|
||||||
def ff(t, *args, **kwds):
|
|
||||||
return scale * self.f(scale * t + offset, *args, **kwds)
|
|
||||||
|
|
||||||
def gg(t, *args, **kwds):
|
|
||||||
return self.g(scale * t + offset, *args, **kwds)
|
|
||||||
|
|
||||||
def dgg(t, *args, **kwds):
|
|
||||||
return scale * self.dg(scale * t + offset, *args, **kwds)
|
|
||||||
dg_a = abs(omega * dgg(-1, *args, **kwds))
|
|
||||||
dg_b = abs(omega * dgg(1, *args, **kwds))
|
|
||||||
g_a = abs(omega * gg(-1, *args, **kwds))
|
|
||||||
g_b = abs(omega * gg(1, *args, **kwds))
|
|
||||||
delta, alpha = min(dg_a, dg_b), min(g_a, g_b)
|
|
||||||
|
|
||||||
betam = delta # * scale
|
|
||||||
if self.endpoints:
|
|
||||||
if delta < 10 or alpha <= 10 or s > 1:
|
|
||||||
points = chebyshev_extrema
|
|
||||||
else:
|
|
||||||
points = adaptive_levin_points
|
|
||||||
elif delta < 10 or alpha <= 10 or s > 1:
|
|
||||||
points = chebyshev_roots
|
|
||||||
else:
|
|
||||||
points = open_levin_points # tanh_sinh_open_nodes
|
|
||||||
|
|
||||||
m = self._get_num_points(s, prec, betam)
|
|
||||||
abseps = 10 * 10.0**-prec
|
|
||||||
num_collocation_point_list = m * 2**np.arange(1, 5) + 1
|
|
||||||
basis = self.basis
|
|
||||||
|
|
||||||
q_val = 1e+300
|
|
||||||
num_function_evaluations = 0
|
|
||||||
n = 0
|
|
||||||
for num_collocation_points in num_collocation_point_list:
|
|
||||||
n_old = n
|
|
||||||
q_old = q_val
|
|
||||||
x = points(num_collocation_points, betam)
|
|
||||||
n = len(x)
|
|
||||||
if n > n_old:
|
|
||||||
q_val = self._a_levin(omega, ff, gg, dgg, x, s, basis, *args,
|
|
||||||
**kwds)
|
|
||||||
num_function_evaluations += n
|
|
||||||
err = np.abs(q_val - q_old)
|
|
||||||
if err <= abseps:
|
|
||||||
break
|
|
||||||
info = self.info(err, num_function_evaluations)
|
|
||||||
return q_val, info
|
|
||||||
|
|
||||||
|
|
||||||
class EvansWebster(AdaptiveLevin):
|
|
||||||
"""Return integral for the Evans Webster method"""
|
|
||||||
|
|
||||||
def __init__(self, f, g, dg=None, a=-1, b=1, basis=chebyshev_basis, s=8,
|
|
||||||
precision=10, endpoints=False, full_output=False):
|
|
||||||
super(EvansWebster,
|
|
||||||
self).__init__(f, g, dg=dg, a=a, b=b, basis=basis, s=s,
|
|
||||||
precision=precision, endpoints=endpoints,
|
|
||||||
full_output=full_output)
|
|
||||||
|
|
||||||
def _a_levin(self, omega, ff, gg, dgg, x, s, basis, *args, **kwds):
|
|
||||||
w = evans_webster_weights(omega, gg, dgg, x, basis, *args, **kwds)
|
|
||||||
|
|
||||||
f = Limit(ff)(x, *args, **kwds)
|
|
||||||
return np.sum(f * w)
|
|
||||||
|
|
||||||
def _get_num_points(self, s, prec, betam):
|
|
||||||
return 8 if s > 1 else int(prec / max(np.log10(betam + 1), 1) + 1)
|
|
||||||
|
|
||||||
|
|
||||||
if __name__ == '__main__':
|
|
||||||
tanh_sinh_nodes(16)
|
|
||||||
@ -1,532 +0,0 @@
|
|||||||
'''
|
|
||||||
|
|
||||||
All the software contained in this library is protected by copyright.
|
|
||||||
Permission to use, copy, modify, and distribute this software for any
|
|
||||||
purpose without fee is hereby granted, provided that this entire notice
|
|
||||||
is included in all copies of any software which is or includes a copy
|
|
||||||
or modification of this software and in all copies of the supporting
|
|
||||||
documentation for such software.
|
|
||||||
|
|
||||||
THIS SOFTWARE IS BEING PROVIDED "AS IS", WITHOUT ANY EXPRESS OR IMPLIED
|
|
||||||
WARRANTY. IN NO EVENT, NEITHER THE AUTHORS, NOR THE PUBLISHER, NOR ANY
|
|
||||||
MEMBER OF THE EDITORIAL BOARD OF THE JOURNAL "NUMERICAL ALGORITHMS",
|
|
||||||
NOR ITS EDITOR-IN-CHIEF, BE LIABLE FOR ANY ERROR IN THE SOFTWARE, ANY
|
|
||||||
MISUSE OF IT OR ANY DAMAGE ARISING OUT OF ITS USE. THE ENTIRE RISK OF
|
|
||||||
USING THE SOFTWARE LIES WITH THE PARTY DOING SO.
|
|
||||||
|
|
||||||
ANY USE OF THE SOFTWARE CONSTITUTES ACCEPTANCE OF THE TERMS OF THE
|
|
||||||
ABOVE STATEMENT.
|
|
||||||
|
|
||||||
|
|
||||||
AUTHORS:
|
|
||||||
Per A Brodtkorb
|
|
||||||
Python code Based on matlab code written by:
|
|
||||||
|
|
||||||
Marco Caliari
|
|
||||||
University of Verona, Italy
|
|
||||||
E-mail: marco.caliari@univr.it
|
|
||||||
|
|
||||||
Stefano de Marchi, Alvise Sommariva, Marco Vianello
|
|
||||||
University of Padua, Italy
|
|
||||||
E-mail: demarchi@math.unipd.it, alvise@math.unipd.it,
|
|
||||||
marcov@math.unipd.it
|
|
||||||
|
|
||||||
Reference
|
|
||||||
---------
|
|
||||||
Padua2DM: fast interpolation and cubature at the Padua points in Matlab/Octave
|
|
||||||
NUMERICAL ALGORITHMS, 56 (2011), PP. 45-60
|
|
||||||
|
|
||||||
|
|
||||||
Padua module
|
|
||||||
------------
|
|
||||||
In polynomial interpolation of two variables, the Padua points are the first
|
|
||||||
known example (and up to now the only one) of a unisolvent point set
|
|
||||||
(that is, the interpolating polynomial is unique) with minimal growth of their
|
|
||||||
Lebesgue constant, proven to be O(log2 n).
|
|
||||||
This module provides all the functions needed to perform interpolation and
|
|
||||||
cubature at the Padua points, together with the functions and the demos used
|
|
||||||
in the paper.
|
|
||||||
|
|
||||||
pdint.m : main function for interpolation at the Padua points
|
|
||||||
pdcub.m : main function for cubature at the Padua points
|
|
||||||
pdpts.m : function for the computation of the Padua points
|
|
||||||
padua_fit.m : function for the computation of the interpolation
|
|
||||||
coefficients by FFT (recommended)
|
|
||||||
pdcfsMM.m : function for the computation of the interpolation
|
|
||||||
coefficients by matrix multiplications
|
|
||||||
pdval.m : function for the evaluation of the interpolation
|
|
||||||
polynomial
|
|
||||||
pdwtsFFT.m : function for the computation of the cubature
|
|
||||||
weights by FFT
|
|
||||||
pdwtsMM.m : function for the computation of the cubature
|
|
||||||
weights by matrix multiplications (recommended)
|
|
||||||
funct.m : function containing some test functions
|
|
||||||
demo_pdint.m : demo script for pdint
|
|
||||||
demo_cputime_pdint.m : demo script for the computation of CPU time for
|
|
||||||
interpolation
|
|
||||||
demo_errors_pdint.m : demo script for the comparison of interpolation with
|
|
||||||
coefficients computed by FFT or by matrix
|
|
||||||
multiplications
|
|
||||||
demo_pdcub : demo script for pdcub
|
|
||||||
demo_cputime_pdcub.m : demo script for the computation of CPU time for
|
|
||||||
cubature
|
|
||||||
demo_errors_pdcub.m : demo script for the comparison of cubature with
|
|
||||||
weights computed by FFT or by matrix multiplications
|
|
||||||
demo_errors_pdcub_gl.m : demo script for the comparison of different cubature
|
|
||||||
formulas
|
|
||||||
cubature_square.m : function for the computation of some cubature
|
|
||||||
formulas for the square
|
|
||||||
omelyan_solovyan_rule.m : function for the computation of Omelyan-Solovyan
|
|
||||||
cubature points and weights
|
|
||||||
Contents.m : Contents file for Matlab
|
|
||||||
|
|
||||||
|
|
||||||
'''
|
|
||||||
from __future__ import absolute_import, division
|
|
||||||
import numpy as np
|
|
||||||
from numpy.fft import fft
|
|
||||||
from wafo.dctpack import dct
|
|
||||||
from wafo.polynomial import map_from_interval, map_to_interval
|
|
||||||
# from scipy.fftpack.realtransforms import dct
|
|
||||||
|
|
||||||
|
|
||||||
class _ExampleFunctions(object):
|
|
||||||
'''
|
|
||||||
Computes test function in the points (x, y)
|
|
||||||
|
|
||||||
Parameters
|
|
||||||
----------
|
|
||||||
x,y : array-like
|
|
||||||
evaluate the function in the points (x,y)
|
|
||||||
i : scalar int (default 0)
|
|
||||||
defining which test function to use. Options are
|
|
||||||
0: franke
|
|
||||||
1: half_sphere
|
|
||||||
2: poly_degree20
|
|
||||||
3: exp_fun1
|
|
||||||
4: exp_fun100
|
|
||||||
5: cos30
|
|
||||||
6: constant
|
|
||||||
7: exp_xy
|
|
||||||
8: runge
|
|
||||||
9: abs_cubed
|
|
||||||
10: gauss
|
|
||||||
11: exp_inv
|
|
||||||
|
|
||||||
Returns
|
|
||||||
-------
|
|
||||||
z : array-like
|
|
||||||
value of the function in the points (x,y)
|
|
||||||
'''
|
|
||||||
@staticmethod
|
|
||||||
def franke(x, y):
|
|
||||||
'''Franke function.
|
|
||||||
|
|
||||||
The value of the definite integral on the square [-1,1] x [-1,1],
|
|
||||||
obtained using a Padua Points cubature formula of degree 500, is
|
|
||||||
2.1547794245591083e+000 with an estimated absolute error of 8.88e-016.
|
|
||||||
|
|
||||||
The value of the definite integral on the square [0,1] x [0,1],
|
|
||||||
obtained using a Padua Points cubature formula of degree 500, is
|
|
||||||
4.06969589491556e-01 with an estimated absolute error of 8.88e-016.
|
|
||||||
|
|
||||||
Maple: 0.40696958949155611906
|
|
||||||
'''
|
|
||||||
def _exp(x, y, loc, scale, p2=2):
|
|
||||||
return np.exp(- (x - loc[0])**2 / scale[0] - (y - loc[1])**p2 / scale[1])
|
|
||||||
# exp = np.exp
|
|
||||||
x9, y9 = 9. * x, 9. * y
|
|
||||||
return (3. / 4 * _exp(x9, y9, [2, 2], [4, 4]) +
|
|
||||||
3. / 4 * _exp(x9, y9, [-1, -1], [49, 10], p2=1) +
|
|
||||||
1. / 2 * _exp(x9, y9, [7, 3], [4, 4]) -
|
|
||||||
1. / 5 * _exp(x9, y9, [4, 7], [1, 1]))
|
|
||||||
|
|
||||||
@staticmethod
|
|
||||||
def half_sphere(x, y):
|
|
||||||
'''The value of the definite integral on the square [-1,1] x [-1,1],
|
|
||||||
obtained using a Padua Points cubature formula of degree 2000, is
|
|
||||||
3.9129044444568244e+000 with an estimated absolute error of 3.22e-010.
|
|
||||||
'''
|
|
||||||
return ((x - 0.5)**2 + (y - 0.5)**2)**(1. / 2)
|
|
||||||
|
|
||||||
@staticmethod
|
|
||||||
def poly_degree20(x, y):
|
|
||||||
''''Bivariate polynomial having moderate degree.
|
|
||||||
The value of the definite integral on the square [-1,1] x
|
|
||||||
[-1,1], up to machine precision, is 18157.16017316017 (see ref. 6).
|
|
||||||
The value of the definite integral on the square [-1,1] x [-1,1],
|
|
||||||
obtained using a Padua Points cubature formula of degree 500,
|
|
||||||
is 1.8157160173160162e+004.
|
|
||||||
|
|
||||||
2D modification of an example by L.N.Trefethen (see ref. 7), f(x)=x^20.
|
|
||||||
'''
|
|
||||||
return (x + y)**20
|
|
||||||
|
|
||||||
@staticmethod
|
|
||||||
def exp_fun1(x, y):
|
|
||||||
''' The value of the definite integral on the square [-1,1] x [-1,1],
|
|
||||||
obtained using a Padua Points cubature formula of degree 2000,
|
|
||||||
is 2.1234596326670683e+001 with an estimated absolute error of
|
|
||||||
7.11e-015.
|
|
||||||
'''
|
|
||||||
return np.exp((x - 0.5)**2 + (y - 0.5)**2)
|
|
||||||
|
|
||||||
@staticmethod
|
|
||||||
def exp_fun100(x, y):
|
|
||||||
'''The value of the definite integral on the square [-1,1] x [-1,1],
|
|
||||||
obtained using a Padua Points cubature formula of degree 2000,
|
|
||||||
is 3.1415926535849605e-002 with an estimated absolute error of
|
|
||||||
3.47e-017.
|
|
||||||
'''
|
|
||||||
return np.exp(-100 * ((x - 0.5)**2 + (y - 0.5)**2))
|
|
||||||
|
|
||||||
@staticmethod
|
|
||||||
def cos30(x, y):
|
|
||||||
''' The value of the definite integral on the square [-1,1] x [-1,1],
|
|
||||||
obtained using a Padua Points cubature formula of degree 500,
|
|
||||||
is 4.3386955120336568e-003 with an estimated absolute error of
|
|
||||||
2.95e-017.
|
|
||||||
'''
|
|
||||||
return np.cos(30 * (x + y))
|
|
||||||
|
|
||||||
@staticmethod
|
|
||||||
def constant(x, y):
|
|
||||||
'''Constant.
|
|
||||||
To test interpolation and cubature at degree 0.
|
|
||||||
The value of the definite integral on the square [-1,1] x [-1,1]
|
|
||||||
is 4.
|
|
||||||
'''
|
|
||||||
return np.ones(np.shape(x + y))
|
|
||||||
|
|
||||||
@staticmethod
|
|
||||||
def exp_xy(x, y):
|
|
||||||
'''The value of the definite integral on the square [-1,1] x [-1,1]
|
|
||||||
is up to machine precision is 5.524391382167263 (see ref. 6).
|
|
||||||
2. The value of the definite integral on the square [-1,1] x [-1,1],
|
|
||||||
obtained using a Padua Points cubature formula of degree 500,
|
|
||||||
is 5.5243913821672628e+000 with an estimated absolute error of
|
|
||||||
0.00e+000.
|
|
||||||
2D modification of an example by L.N.Trefethen (see ref. 7),
|
|
||||||
f(x)=exp(x).
|
|
||||||
'''
|
|
||||||
return np.exp(x + y)
|
|
||||||
|
|
||||||
@staticmethod
|
|
||||||
def runge(x, y):
|
|
||||||
''' Bivariate Runge function: as 1D complex function is analytic
|
|
||||||
in a neighborhood of [-1; 1] but not throughout the complex plane.
|
|
||||||
|
|
||||||
The value of the definite integral on the square [-1,1] x [-1,1],
|
|
||||||
up to machine precision, is 0.597388947274307 (see ref. 6).
|
|
||||||
The value of the definite integral on the square [-1,1] x [-1,1],
|
|
||||||
obtained using a Padua Points cubature formula of degree 500,
|
|
||||||
is 5.9738894727430725e-001 with an estimated absolute error of
|
|
||||||
0.00e+000.
|
|
||||||
|
|
||||||
2D modification of an example by L.N.Trefethen (see ref. 7),
|
|
||||||
f(x)=1/(1+16*x^2).
|
|
||||||
'''
|
|
||||||
return 1. / (1 + 16 * (x**2 + y**2))
|
|
||||||
|
|
||||||
@staticmethod
|
|
||||||
def abs_cubed(x, y):
|
|
||||||
'''Low regular function.
|
|
||||||
The value of the definite integral on the square [-1,1] x [-1,1],
|
|
||||||
up to machine precision, is 2.508723139534059 (see ref. 6).
|
|
||||||
The value of the definite integral on the square [-1,1] x [-1,1],
|
|
||||||
obtained using a Padua Points cubature formula of degree 500,
|
|
||||||
is 2.5087231395340579e+000 with an estimated absolute error of
|
|
||||||
0.00e+000.
|
|
||||||
|
|
||||||
2D modification of an example by L.N.Trefethen (see ref. 7),
|
|
||||||
f(x)=abs(x)^3.
|
|
||||||
'''
|
|
||||||
return (x**2 + y**2)**(3 / 2)
|
|
||||||
|
|
||||||
@staticmethod
|
|
||||||
def gauss(x, y):
|
|
||||||
'''Bivariate gaussian: smooth function.
|
|
||||||
The value of the definite integral on the square [-1,1] x [-1,1],
|
|
||||||
up to machine precision, is 2.230985141404135 (see ref. 6).
|
|
||||||
The value of the definite integral on the square [-1,1] x [-1,1],
|
|
||||||
obtained using a Padua Points cubature formula of degree 500,
|
|
||||||
is 2.2309851414041333e+000 with an estimated absolute error of
|
|
||||||
2.66e-015.
|
|
||||||
|
|
||||||
2D modification of an example by L.N.Trefethen (see ref. 7),
|
|
||||||
f(x)=exp(-x^2).
|
|
||||||
'''
|
|
||||||
return np.exp(-x**2 - y**2)
|
|
||||||
|
|
||||||
@staticmethod
|
|
||||||
def exp_inv(x, y):
|
|
||||||
'''Bivariate example stemming from a 1D C-infinity function.
|
|
||||||
The value of the definite integral on the square [-1,1] x [-1,1],
|
|
||||||
up to machine precision, is 0.853358758654305 (see ref. 6).
|
|
||||||
The value of the definite integral on the square [-1,1] x [-1,1],
|
|
||||||
obtained using a Padua Points cubature formula of degree 2000,
|
|
||||||
is 8.5335875865430544e-001 with an estimated absolute error of
|
|
||||||
3.11e-015.
|
|
||||||
|
|
||||||
2D modification of an example by L.N.Trefethen (see ref. 7),
|
|
||||||
f(x)=exp(-1/x^2).
|
|
||||||
'''
|
|
||||||
arg_z = (x**2 + y**2)
|
|
||||||
# Avoid cases in which "arg_z=0", setting only in those instances
|
|
||||||
# "arg_z=eps".
|
|
||||||
arg_z = arg_z + (1 - np.abs(np.sign(arg_z))) * 1.e-100
|
|
||||||
arg_z = 1. / arg_z
|
|
||||||
return np.exp(-arg_z)
|
|
||||||
|
|
||||||
def __call__(self, x, y, i=0):
|
|
||||||
s = self
|
|
||||||
test_function = [s.franke, s.half_sphere, s.poly_degree20, s.exp_fun1,
|
|
||||||
s.exp_fun100, s.cos30, s.constant, s.exp_xy, s.runge,
|
|
||||||
s.abs_cubed, s.gauss, s.exp_inv]
|
|
||||||
return test_function[i](x, y)
|
|
||||||
|
|
||||||
|
|
||||||
example_functions = _ExampleFunctions()
|
|
||||||
|
|
||||||
|
|
||||||
def _find_m(n):
|
|
||||||
ix = np.r_[1:(n + 1) * (n + 2):2]
|
|
||||||
if np.mod(n, 2) == 0:
|
|
||||||
n2 = n // 2
|
|
||||||
offset = np.array([[0, 1] * n2 + [0, ]] * (n2 + 1))
|
|
||||||
ix = ix - offset.ravel(order='F')
|
|
||||||
return ix
|
|
||||||
|
|
||||||
|
|
||||||
def padua_points(n, domain=(-1, 1, -1, 1)):
|
|
||||||
''' Return Padua points
|
|
||||||
|
|
||||||
Parameters
|
|
||||||
----------
|
|
||||||
n : scalar integer
|
|
||||||
interpolation degree
|
|
||||||
domain : vector [a,b,c,d]
|
|
||||||
defining the rectangle [a,b] x [c,d]. (default domain = (-1,1,-1,1))
|
|
||||||
|
|
||||||
Returns
|
|
||||||
-------
|
|
||||||
pad : array of shape (2 x (n+1)*(n+2)/2) such that
|
|
||||||
(pad[0,:], pad[1,: ]) defines the Padua points in the domain
|
|
||||||
rectangle [a,b] x [c,d].
|
|
||||||
or
|
|
||||||
X1,Y1,X2,Y2 : arrays
|
|
||||||
Two subgrids X1,Y1 and X2,Y2 defining the Padua points
|
|
||||||
-------------------------------------------------------------------------------
|
|
||||||
'''
|
|
||||||
a, b, c, d = domain
|
|
||||||
t0 = [np.pi] if n == 0 else np.linspace(0, np.pi, n + 1)
|
|
||||||
t1 = np.linspace(0, np.pi, n + 2)
|
|
||||||
zn = map_to_interval(np.cos(t0), a, b)
|
|
||||||
zn1 = map_to_interval(np.cos(t1), c, d)
|
|
||||||
|
|
||||||
Pad1, Pad2 = np.meshgrid(zn, zn1)
|
|
||||||
ix = _find_m(n)
|
|
||||||
return np.vstack((Pad1.ravel(order='F')[ix],
|
|
||||||
Pad2.ravel(order='F')[ix]))
|
|
||||||
|
|
||||||
|
|
||||||
def error_estimate(C0f):
|
|
||||||
''' Return interpolation error estimate from Padua coefficients
|
|
||||||
'''
|
|
||||||
n = C0f.shape[1]
|
|
||||||
C0f2 = np.fliplr(C0f)
|
|
||||||
errest = sum(np.abs(np.diag(C0f2)))
|
|
||||||
if (n >= 1):
|
|
||||||
errest = errest + sum(np.abs(np.diag(C0f2, -1)))
|
|
||||||
if (n >= 2):
|
|
||||||
errest = errest + sum(np.abs(np.diag(C0f2, -2)))
|
|
||||||
return 2 * errest
|
|
||||||
|
|
||||||
|
|
||||||
def padua_fit(Pad, fun, *args):
|
|
||||||
'''
|
|
||||||
Computes the Chebyshevs coefficients
|
|
||||||
|
|
||||||
so that f(x, y) can be approximated by:
|
|
||||||
|
|
||||||
f(x, y) = sum cjk*Tjk(x, y)
|
|
||||||
|
|
||||||
Parameters
|
|
||||||
----------
|
|
||||||
Pad : array-like
|
|
||||||
Padua points, as computed with padua_points function.
|
|
||||||
fun : function to be interpolated in the form
|
|
||||||
fun(x, y, *args), where *args are optional arguments for fun.
|
|
||||||
|
|
||||||
Returns
|
|
||||||
-------
|
|
||||||
coefficents: coefficient matrix
|
|
||||||
abs_err : interpolation error estimate
|
|
||||||
|
|
||||||
Example
|
|
||||||
------
|
|
||||||
>>> import numpy as np
|
|
||||||
>>> import wafo.padua as wp
|
|
||||||
>>> domain = [0, 1, 0, 1]
|
|
||||||
>>> a, b, c, d = domain
|
|
||||||
>>> points = wp.padua_points(21, domain)
|
|
||||||
>>> C0f, abs_error = wp.padua_fit(points, wp.example_functions.franke)
|
|
||||||
>>> x1 = np.linspace(a, b, 100)
|
|
||||||
>>> x2 = np.linspace(c, d, 101)
|
|
||||||
>>> val = wp.padua_val(x1, x2, C0f, domain, use_meshgrid=True)
|
|
||||||
>>> X1, X2 = np.meshgrid(x1, x2)
|
|
||||||
>>> true_val = wp.example_functions.franke(X1, X2)
|
|
||||||
|
|
||||||
>>> np.allclose(val, true_val, atol=10*abs_error)
|
|
||||||
True
|
|
||||||
>>> np.allclose(np.abs(val-true_val).max(), 0.0073174614275738296)
|
|
||||||
True
|
|
||||||
>>> np.allclose(abs_error, 0.0022486904061664046)
|
|
||||||
True
|
|
||||||
|
|
||||||
import matplotlib.pyplot as plt
|
|
||||||
plt.contour(x1, x2, val)
|
|
||||||
|
|
||||||
'''
|
|
||||||
|
|
||||||
N = np.shape(Pad)[1]
|
|
||||||
# recover the degree n from N = (n+1)(n+2)/2
|
|
||||||
n = int(round(-3 + np.sqrt(1 + 8 * N)) / 2)
|
|
||||||
C0f = fun(Pad[0], Pad[1], *args)
|
|
||||||
if (n > 0):
|
|
||||||
ix = _find_m(n)
|
|
||||||
GfT = np.zeros((n + 2) * (n + 1))
|
|
||||||
GfT[ix] = C0f * 2 / (n * (n + 1))
|
|
||||||
GfT = GfT.reshape(n + 1, n + 2)
|
|
||||||
GfT = GfT.T
|
|
||||||
GfT[0] = GfT[0] / 2
|
|
||||||
GfT[n + 1] = GfT[n + 1] / 2
|
|
||||||
GfT[:, 0] = GfT[:, 0] / 2
|
|
||||||
GfT[:, n] = GfT[:, n] / 2
|
|
||||||
Gf = GfT.T
|
|
||||||
# compute the interpolation coefficient matrix C0f by FFT
|
|
||||||
Gfhat = np.real(fft(Gf, 2 * n, axis=0))
|
|
||||||
Gfhathat = np.real(fft(Gfhat[:n + 1, :], 2 * (n + 1), axis=1))
|
|
||||||
C0f = 2 * Gfhathat[:, 0:n + 1]
|
|
||||||
C0f[0] = C0f[0, :] / np.sqrt(2)
|
|
||||||
C0f[:, 0] = C0f[:, 0] / np.sqrt(2)
|
|
||||||
C0f = np.fliplr(np.triu(np.fliplr(C0f)))
|
|
||||||
C0f[n] = C0f[n] / 2
|
|
||||||
|
|
||||||
return C0f, error_estimate(C0f)
|
|
||||||
|
|
||||||
|
|
||||||
def paduavals2coefs(f):
|
|
||||||
m = len(f)
|
|
||||||
n = int(round(-1.5 + np.sqrt(.25 + 2 * m)))
|
|
||||||
x = padua_points(n)
|
|
||||||
idx = _find_m(n)
|
|
||||||
w = 0 * x[0] + 1. / (n * (n + 1))
|
|
||||||
idx1 = np.all(np.abs(x) == 1, axis=0)
|
|
||||||
w[idx1] = .5 * w[idx1]
|
|
||||||
idx2 = np.all(np.abs(x) != 1, axis=0)
|
|
||||||
w[idx2] = 2 * w[idx2]
|
|
||||||
|
|
||||||
G = np.zeros(idx.max() + 1)
|
|
||||||
G[idx] = 4 * w * f
|
|
||||||
|
|
||||||
use_dct = 100 < n
|
|
||||||
if use_dct:
|
|
||||||
C = np.rot90(dct(dct(G.T).T)) # , axis=1)
|
|
||||||
else:
|
|
||||||
t1 = np.r_[0:n + 1].reshape(-1, 1)
|
|
||||||
Tn1 = np.cos(t1 * t1.T * np.pi / n)
|
|
||||||
t2 = np.r_[0:n + 2].reshape(-1, 1)
|
|
||||||
Tn2 = np.cos(t2 * t2.T * np.pi / (n + 1))
|
|
||||||
C = np.dot(Tn2, np.dot(G, Tn1))
|
|
||||||
|
|
||||||
C[0] = .5 * C[0]
|
|
||||||
C[:, 1] = .5 * C[:, 1]
|
|
||||||
C[0, -1] = .5 * C[0, -1]
|
|
||||||
del C[-1]
|
|
||||||
|
|
||||||
# Take upper-left triangular part:
|
|
||||||
return np.fliplr(np.triu(np.fliplr(C)))
|
|
||||||
# C = triu(C(:,end:-1:1));
|
|
||||||
# C = C(:,end:-1:1);
|
|
||||||
|
|
||||||
|
|
||||||
# TODO: padua_fit2 does not work correctly yet.
|
|
||||||
def padua_fit2(Pad, fun, *args):
|
|
||||||
# N = np.shape(Pad)[1]
|
|
||||||
# recover the degree n from N = (n+1)(n+2)/2
|
|
||||||
# _n = int(round(-3 + np.sqrt(1 + 8 * N)) / 2)
|
|
||||||
C0f = fun(Pad[0], Pad[1], *args)
|
|
||||||
return paduavals2coefs(C0f)
|
|
||||||
|
|
||||||
|
|
||||||
def _compute_moments(n):
|
|
||||||
k = np.r_[0:n:2]
|
|
||||||
mom = 2 * np.sqrt(2) / (1 - k ** 2)
|
|
||||||
mom[0] = 2
|
|
||||||
return mom
|
|
||||||
|
|
||||||
|
|
||||||
def padua_cubature(coefficients, domain=(-1, 1, -1, 1)):
|
|
||||||
'''
|
|
||||||
Compute the integral through the coefficient matrix.
|
|
||||||
'''
|
|
||||||
n = coefficients.shape[1]
|
|
||||||
mom = _compute_moments(n)
|
|
||||||
M1, M2 = np.meshgrid(mom, mom)
|
|
||||||
M = M1 * M2
|
|
||||||
C0fM = coefficients[0:n:2, 0:n:2] * M
|
|
||||||
a, b, c, d = domain
|
|
||||||
integral = (b - a) * (d - c) * C0fM.sum() / 4
|
|
||||||
return integral
|
|
||||||
|
|
||||||
|
|
||||||
def padua_val(X, Y, coefficients, domain=(-1, 1, -1, 1), use_meshgrid=False):
|
|
||||||
'''
|
|
||||||
Evaluate polynomial in padua form at X, Y.
|
|
||||||
|
|
||||||
Evaluate the interpolation polynomial defined through its coefficient
|
|
||||||
matrix coefficients at the target points X(:,1),X(:,2) or at the
|
|
||||||
meshgrid(X1,X2)
|
|
||||||
|
|
||||||
Parameters
|
|
||||||
----------
|
|
||||||
X, Y: array-like
|
|
||||||
evaluation points.
|
|
||||||
coefficients : array-like
|
|
||||||
coefficient matrix
|
|
||||||
domain : a vector [a,b,c,d]
|
|
||||||
defining the rectangle [a,b] x [c,d]
|
|
||||||
use_meshgrid: bool
|
|
||||||
If True interpolate at the points meshgrid(X, Y)
|
|
||||||
|
|
||||||
Returns
|
|
||||||
-------
|
|
||||||
fxy : array-like
|
|
||||||
evaluation of the interpolation polynomial at the target points
|
|
||||||
'''
|
|
||||||
def transform(tn, x, a, b):
|
|
||||||
xn = map_from_interval(x, a, b).clip(min=-1, max=1).reshape(1, -1)
|
|
||||||
tx = np.cos(tn * np.arccos(xn)) * np.sqrt(2)
|
|
||||||
tx[0] = 1
|
|
||||||
return tx
|
|
||||||
|
|
||||||
X, Y = np.atleast_1d(X, Y)
|
|
||||||
original_shape = X.shape
|
|
||||||
a, b, c, d = domain
|
|
||||||
n = np.shape(coefficients)[1]
|
|
||||||
|
|
||||||
tn = np.r_[0:n][:, None]
|
|
||||||
tx1 = transform(tn, X.ravel(), a, b)
|
|
||||||
tx2 = transform(tn, Y.ravel(), c, d)
|
|
||||||
|
|
||||||
if use_meshgrid: # eval on meshgrid points
|
|
||||||
return np.dot(tx1.T, np.dot(coefficients, tx2)).T
|
|
||||||
# scattered points
|
|
||||||
val = np.sum(np.dot(tx1.T, coefficients) * tx2.T, axis=1)
|
|
||||||
return val.reshape(original_shape)
|
|
||||||
|
|
||||||
|
|
||||||
if __name__ == '__main__':
|
|
||||||
from wafo.testing import test_docstrings
|
|
||||||
test_docstrings(__file__)
|
|
||||||
@ -1,18 +0,0 @@
|
|||||||
# -*- coding: utf-8 -*-
|
|
||||||
"""
|
|
||||||
Created on Sun Oct 25 14:55:34 2015
|
|
||||||
|
|
||||||
@author: dave
|
|
||||||
"""
|
|
||||||
|
|
||||||
|
|
||||||
def configuration(parent_package='', top_path=None):
|
|
||||||
from numpy.distutils.misc_util import Configuration
|
|
||||||
config = Configuration('wafo', parent_package, top_path)
|
|
||||||
config.add_subpackage('source')
|
|
||||||
config.make_config_py()
|
|
||||||
return config
|
|
||||||
|
|
||||||
if __name__ == "__main__":
|
|
||||||
from numpy.distutils.core import setup
|
|
||||||
setup(**configuration(top_path='').todict())
|
|
||||||
@ -1,268 +0,0 @@
|
|||||||
'''
|
|
||||||
Created on 13. mar. 2018
|
|
||||||
|
|
||||||
@author: pab
|
|
||||||
'''
|
|
||||||
import numpy as np
|
|
||||||
from numpy import pi, sqrt
|
|
||||||
import wafo.transform.estimation as te
|
|
||||||
import wafo.transform as wt
|
|
||||||
from wafo.containers import PlotData
|
|
||||||
from wafo.kdetools.kernels import qlevels
|
|
||||||
from wafo.misc import tranproc
|
|
||||||
import warnings
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
def _set_default_t_h_g(t, h, g, m0, m2):
|
|
||||||
if g is None:
|
|
||||||
y = np.linspace(-5, 5)
|
|
||||||
x = sqrt(m0) * y + 0
|
|
||||||
g = wt.TrData(y, x)
|
|
||||||
if t is None:
|
|
||||||
tt1 = 2 * pi * sqrt(m0 / m2)
|
|
||||||
t = np.linspace(0, 1.7 * tt1, 51)
|
|
||||||
if h is None:
|
|
||||||
px = g.gauss2dat([0, 4.])
|
|
||||||
px = abs(px[1] - px[0])
|
|
||||||
h = np.linspace(0, 1.3 * px, 41)
|
|
||||||
return h, t, g
|
|
||||||
|
|
||||||
def lh83pdf(t=None, h=None, mom=None, g=None):
|
|
||||||
"""
|
|
||||||
LH83PDF Longuet-Higgins (1983) approximation of the density (Tc,Ac)
|
|
||||||
in a stationary Gaussian transform process X(t) where
|
|
||||||
Y(t) = g(X(t)) (Y zero-mean Gaussian, X non-Gaussian).
|
|
||||||
|
|
||||||
CALL: f = lh83pdf(t,h,[m0,m1,m2],g);
|
|
||||||
|
|
||||||
f = density of wave characteristics of half-wavelength
|
|
||||||
in a stationary Gaussian transformed process X(t),
|
|
||||||
where Y(t) = g(X(t)) (Y zero-mean Gaussian)
|
|
||||||
t,h = vectors of periods and amplitudes, respectively.
|
|
||||||
default depending on the spectral moments
|
|
||||||
m0,m1,m2 = the 0'th,1'st and 2'nd moment of the spectral density
|
|
||||||
with angular frequency.
|
|
||||||
g = space transformation, Y(t)=g(X(t)), default: g is identity
|
|
||||||
transformation, i.e. X(t) = Y(t) is Gaussian,
|
|
||||||
The transformation, g, can be estimated using lc2tr
|
|
||||||
or dat2tr or given apriori by ochi.
|
|
||||||
|
|
||||||
Example
|
|
||||||
-------
|
|
||||||
>>> import wafo.spectrum.models as sm
|
|
||||||
>>> Sj = sm.Jonswap()
|
|
||||||
>>> w = np.linspace(0,4,256)
|
|
||||||
>>> S = Sj.tospecdata(w) #Make spectrum object from numerical values
|
|
||||||
>>> S = sm.SpecData1D(Sj(w),w) # Alternatively do it manually
|
|
||||||
>>> mom, mom_txt = S.moment(nr=2, even=False)
|
|
||||||
>>> f = lh83pdf(mom=mom)
|
|
||||||
>>> f.plot()
|
|
||||||
|
|
||||||
See also
|
|
||||||
--------
|
|
||||||
cav76pdf, lc2tr, dat2tr
|
|
||||||
|
|
||||||
References
|
|
||||||
----------
|
|
||||||
Longuet-Higgins, M.S. (1983)
|
|
||||||
"On the joint distribution wave periods and amplitudes in a
|
|
||||||
random wave field", Proc. R. Soc. A389, pp 24--258
|
|
||||||
|
|
||||||
Longuet-Higgins, M.S. (1975)
|
|
||||||
"On the joint distribution wave periods and amplitudes of sea waves",
|
|
||||||
J. geophys. Res. 80, pp 2688--2694
|
|
||||||
"""
|
|
||||||
|
|
||||||
# tested on: matlab 5.3
|
|
||||||
# History:
|
|
||||||
# Revised pab 01.04.2001
|
|
||||||
# - Added example
|
|
||||||
# - Better automatic scaling for h,t
|
|
||||||
# revised by IR 18.06.2000, fixing transformation and transposing t and h to fit simpson req.
|
|
||||||
# revised by pab 28.09.1999
|
|
||||||
# made more efficient calculation of f
|
|
||||||
# by Igor Rychlik
|
|
||||||
|
|
||||||
m0, m1, m2 = mom
|
|
||||||
h, t, g = _set_default_t_h_g(t, h, g, m0, m2)
|
|
||||||
|
|
||||||
L0 = m0
|
|
||||||
L1 = m1 / (2 * pi)
|
|
||||||
L2 = m2 / (2 * pi)**2
|
|
||||||
eps2 = sqrt((L2 * L0) / (L1**2) - 1)
|
|
||||||
|
|
||||||
if np.any(~np.isreal(eps2)):
|
|
||||||
raise ValueError('input moments are not correct')
|
|
||||||
|
|
||||||
const = 4 / sqrt(pi) / eps2 / (1 + 1 / sqrt(1 + eps2**2))
|
|
||||||
|
|
||||||
a = len(h)
|
|
||||||
b = len(t)
|
|
||||||
der = np.ones((a, 1))
|
|
||||||
|
|
||||||
h_lh = g.dat2gauss(h.ravel(), der.ravel())
|
|
||||||
|
|
||||||
der = abs(h_lh[1]) # abs(h_lh[:, 1])
|
|
||||||
h_lh = h_lh[0]
|
|
||||||
|
|
||||||
# Normalization + transformation of t and h ???????
|
|
||||||
# Without any transformation
|
|
||||||
|
|
||||||
t_lh = t / (L0 / L1)
|
|
||||||
#h_lh = h_lh/sqrt(2*L0)
|
|
||||||
h_lh = h_lh / sqrt(2)
|
|
||||||
t_lh = 2 * t_lh
|
|
||||||
|
|
||||||
# Computation of the distribution
|
|
||||||
T, H = np.meshgrid(t_lh[1:b], h_lh)
|
|
||||||
f_th = np.zeros((a, b))
|
|
||||||
tmp = const * der[:, None] * (H / T)**2 * np.exp(-H**2. *
|
|
||||||
(1 + ((1 - 1. / T) / eps2)**2)) / ((L0 / L1) * sqrt(2) / 2)
|
|
||||||
f_th[:, 1:b] = tmp
|
|
||||||
|
|
||||||
f = PlotData(f_th, (t, h),
|
|
||||||
xlab='Tc', ylab='Ac',
|
|
||||||
title='Joint density of (Tc,Ac) - Longuet-Higgins (1983)',
|
|
||||||
plot_kwds=dict(plotflag=1))
|
|
||||||
|
|
||||||
return _add_contour_levels(f)
|
|
||||||
|
|
||||||
|
|
||||||
def cav76pdf(t=None, h=None, mom=None, g=None):
|
|
||||||
"""
|
|
||||||
CAV76PDF Cavanie et al. (1976) approximation of the density (Tc,Ac)
|
|
||||||
in a stationary Gaussian transform process X(t) where
|
|
||||||
Y(t) = g(X(t)) (Y zero-mean Gaussian, X non-Gaussian).
|
|
||||||
|
|
||||||
CALL: f = cav76pdf(t,h,[m0,m2,m4],g);
|
|
||||||
|
|
||||||
f = density of wave characteristics of half-wavelength
|
|
||||||
in a stationary Gaussian transformed process X(t),
|
|
||||||
where Y(t) = g(X(t)) (Y zero-mean Gaussian)
|
|
||||||
t,h = vectors of periods and amplitudes, respectively.
|
|
||||||
default depending on the spectral moments
|
|
||||||
m0,m2,m4 = the 0'th, 2'nd and 4'th moment of the spectral density
|
|
||||||
with angular frequency.
|
|
||||||
g = space transformation, Y(t)=g(X(t)), default: g is identity
|
|
||||||
transformation, i.e. X(t) = Y(t) is Gaussian,
|
|
||||||
The transformation, g, can be estimated using lc2tr
|
|
||||||
or dat2tr or given a priori by ochi.
|
|
||||||
[] = default values are used.
|
|
||||||
|
|
||||||
Example
|
|
||||||
-------
|
|
||||||
>>> import wafo.spectrum.models as sm
|
|
||||||
>>> Sj = sm.Jonswap()
|
|
||||||
>>> w = np.linspace(0,4,256)
|
|
||||||
>>> S = Sj.tospecdata(w) #Make spectrum object from numerical values
|
|
||||||
>>> S = sm.SpecData1D(Sj(w),w) # Alternatively do it manually
|
|
||||||
>>> mom, mom_txt = S.moment(nr=4, even=True)
|
|
||||||
>>> f = cav76pdf(mom=mom)
|
|
||||||
>>> f.plot()
|
|
||||||
|
|
||||||
See also
|
|
||||||
--------
|
|
||||||
lh83pdf, lc2tr, dat2tr
|
|
||||||
|
|
||||||
References
|
|
||||||
----------
|
|
||||||
Cavanie, A., Arhan, M. and Ezraty, R. (1976)
|
|
||||||
"A statistical relationship between individual heights and periods of
|
|
||||||
storm waves".
|
|
||||||
In Proceedings Conference on Behaviour of Offshore Structures,
|
|
||||||
Trondheim, pp. 354--360
|
|
||||||
Norwegian Institute of Technology, Trondheim, Norway
|
|
||||||
|
|
||||||
Lindgren, G. and Rychlik, I. (1982)
|
|
||||||
Wave Characteristics Distributions for Gaussian Waves --
|
|
||||||
Wave-lenght, Amplitude and Steepness, Ocean Engng vol 9, pp. 411-432.
|
|
||||||
"""
|
|
||||||
# tested on: matlab 5.3 NB! note
|
|
||||||
# History:
|
|
||||||
# revised pab 04.11.2000
|
|
||||||
# - fixed xlabels i.e. f.labx={'Tc','Ac'}
|
|
||||||
# revised by IR 4 X 2000. fixed transform and normalisation
|
|
||||||
# using Lindgren & Rychlik (1982) paper.
|
|
||||||
# At the end of the function there is a text with derivation of the density.
|
|
||||||
#
|
|
||||||
# revised by jr 21.02.2000
|
|
||||||
# - Introduced cell array for f.x for use with pdfplot
|
|
||||||
# by pab 28.09.1999
|
|
||||||
|
|
||||||
m0, m2, m4 = mom
|
|
||||||
h, t, g = _set_default_t_h_g(t, h, g, m0, m2)
|
|
||||||
|
|
||||||
eps4 = 1.0 - m2**2 / (m0 * m4)
|
|
||||||
alfa = m2 / sqrt(m0 * m4)
|
|
||||||
if np.any(~np.isreal(eps4)):
|
|
||||||
raise ValueError('input moments are not correct')
|
|
||||||
|
|
||||||
a = len(h)
|
|
||||||
b = len(t)
|
|
||||||
der = np.ones((a, 1))
|
|
||||||
|
|
||||||
h_lh = g.dat2gauss(h.ravel(), der.ravel())
|
|
||||||
der = abs(h_lh[1])
|
|
||||||
h_lh = h_lh[0]
|
|
||||||
|
|
||||||
# Normalization + transformation of t and h
|
|
||||||
|
|
||||||
pos = 2 / (1 + alfa) # inverse of a fraction of positive maxima
|
|
||||||
cons = 2 * pi**4 * pos / sqrt(2 * pi) / m4 / sqrt((1 - alfa**2))
|
|
||||||
# Tm=2*pi*sqrt(m0/m2)/alpha; #mean period between positive maxima
|
|
||||||
|
|
||||||
t_lh = t
|
|
||||||
h_lh = sqrt(m0) * h_lh
|
|
||||||
|
|
||||||
# Computation of the distribution
|
|
||||||
T, H = np.meshgrid(t_lh[1:b], h_lh)
|
|
||||||
f_th = np.zeros((a, b))
|
|
||||||
|
|
||||||
f_th[:, 1:b] = cons * der[:, None] * (H**2 / (T**5)) * np.exp(-0.5 * (
|
|
||||||
H / T**2)**2. * ((T**2 - pi**2 * m2 / m4)**2 / (m0 * (1 - alfa**2)) + pi**4 / m4))
|
|
||||||
f = PlotData(f_th, (t, h),
|
|
||||||
xlab='Tc', ylab='Ac',
|
|
||||||
title='Joint density of (Tc,Ac) - Cavanie et al. (1976)',
|
|
||||||
plot_kwds=dict(plotflag=1))
|
|
||||||
return _add_contour_levels(f)
|
|
||||||
|
|
||||||
def _add_contour_levels(f):
|
|
||||||
p_levels = np.r_[10:90:20, 95, 99, 99.9]
|
|
||||||
try:
|
|
||||||
c_levels = qlevels(f.data, p=p_levels)
|
|
||||||
f.clevels = c_levels
|
|
||||||
f.plevels = p_levels
|
|
||||||
except ValueError as e:
|
|
||||||
msg = "Could not calculate contour levels!. ({})".format(str(e))
|
|
||||||
warnings.warn(msg)
|
|
||||||
return f
|
|
||||||
|
|
||||||
# Let U,Z be the height and second derivative (curvature) at a local maximum in a Gaussian proces
|
|
||||||
# with spectral moments m0,m2,m4. The conditional density ($U>0$) has the following form
|
|
||||||
#$$
|
|
||||||
# f(z,u)=c \frac{1}{\sqrt{2\pi}}\frac{1}{\sqrt{m0(1-\alpha^2)}}\exp(-0.5\left(\frac{u-z(m2/m4)}
|
|
||||||
# {\sqrt{m0(1-\alpha^2)}}\right)^2)\frac{|z|}{m4}\exp(-0.5z^2/m4), \quad z<0,
|
|
||||||
#$$
|
|
||||||
# where $c=2/(1+\alpha)$, $\alpha=m2/\sqrt{m0\cdot m4}$.
|
|
||||||
#
|
|
||||||
# The cavanie approximation is based on the model $X(t)=U \cos(\pi t/T)$, consequently
|
|
||||||
# we have $U=H$ and by twice differentiation $Z=-U(\pi^2/T)^2\cos(0)$. The variable change has Jacobian
|
|
||||||
# $2\pi^2 H/T^3$ giving the final formula for the density of $T,H$
|
|
||||||
#$$
|
|
||||||
# f(t,h)=c \frac{2\pi^4}{\sqrt{2\pi}}\frac{1}{m4\sqrt{m0(1-\alpha^2)}}\frac{h^2}{t^5}
|
|
||||||
# \exp(-0.5\frac{h^2}{t^4}\left(\left(\frac{t^2-\pi^2(m2/m4)}
|
|
||||||
# {\sqrt{m0(1-\alpha^2)}}\right)^2+\frac{\pi^4}{m4}\right)).
|
|
||||||
#$$
|
|
||||||
#
|
|
||||||
#
|
|
||||||
|
|
||||||
|
|
||||||
def test_docstrings():
|
|
||||||
import doctest
|
|
||||||
print('Testing docstrings in %s' % __file__)
|
|
||||||
doctest.testmod(optionflags=doctest.NORMALIZE_WHITESPACE)
|
|
||||||
|
|
||||||
|
|
||||||
if __name__ == '__main__':
|
|
||||||
test_docstrings()
|
|
||||||
@ -1,258 +0,0 @@
|
|||||||
from __future__ import division
|
|
||||||
from numpy import round
|
|
||||||
from threading import Thread
|
|
||||||
from time import sleep
|
|
||||||
from win32gui import (InitCommonControls, CallWindowProc, CreateWindowEx,
|
|
||||||
CreateWindow, SetWindowLong, SendMessage, ShowWindow,
|
|
||||||
PumpWaitingMessages, PostQuitMessage, DestroyWindow,
|
|
||||||
MessageBox, EnumWindows, GetClassName)
|
|
||||||
from win32api import GetModuleHandle, GetSystemMetrics # @UnresolvedImport
|
|
||||||
from win32api import SetWindowLong as api_SetWindowLong # @UnresolvedImport
|
|
||||||
from commctrl import (TOOLTIPS_CLASS, TTM_GETDELAYTIME, TTM_SETDELAYTIME,
|
|
||||||
TTDT_INITIAL, TTDT_AUTOPOP)
|
|
||||||
import win32con
|
|
||||||
|
|
||||||
WM_USER = win32con.WM_USER
|
|
||||||
PBM_SETRANGE = (WM_USER + 1)
|
|
||||||
PBM_SETPOS = (WM_USER + 2)
|
|
||||||
PBM_DELTAPOS = (WM_USER + 3)
|
|
||||||
PBM_SETSTEP = (WM_USER + 4)
|
|
||||||
PBM_STEPIT = (WM_USER + 5)
|
|
||||||
PBM_SETRANGE32 = (WM_USER + 6)
|
|
||||||
PBM_GETRANGE = (WM_USER + 7)
|
|
||||||
PBM_GETPOS = (WM_USER + 8)
|
|
||||||
PBM_SETBARCOLOR = (WM_USER + 9)
|
|
||||||
PBM_SETMARQUEE = (WM_USER + 10)
|
|
||||||
PBM_GETSTEP = (WM_USER + 13)
|
|
||||||
PBM_GETBKCOLOR = (WM_USER + 14)
|
|
||||||
PBM_GETBARCOLOR = (WM_USER + 15)
|
|
||||||
PBM_SETSTATE = (WM_USER + 16)
|
|
||||||
PBM_GETSTATE = (WM_USER + 17)
|
|
||||||
PBS_SMOOTH = 0x01
|
|
||||||
PBS_VERTICAL = 0x04
|
|
||||||
PBS_MARQUEE = 0x08
|
|
||||||
PBS_SMOOTHREVERSE = 0x10
|
|
||||||
PBST_NORMAL = 1
|
|
||||||
PBST_ERROR = 2
|
|
||||||
PBST_PAUSED = 3
|
|
||||||
WC_DIALOG = 32770
|
|
||||||
WM_SETTEXT = win32con.WM_SETTEXT
|
|
||||||
|
|
||||||
|
|
||||||
def MAKELPARAM(a, b):
|
|
||||||
return (a & 0xffff) | ((b & 0xffff) << 16)
|
|
||||||
|
|
||||||
|
|
||||||
def _get_tooltip_handles(hwnd, resultList):
|
|
||||||
'''
|
|
||||||
Adds a window handle to resultList if its class-name is 'tooltips_class32',
|
|
||||||
i.e. the window is a tooltip.
|
|
||||||
'''
|
|
||||||
if GetClassName(hwnd) == TOOLTIPS_CLASS:
|
|
||||||
resultList.append(hwnd)
|
|
||||||
|
|
||||||
|
|
||||||
def set_max_pop_delay_on_tooltip(tooltip):
|
|
||||||
'''
|
|
||||||
Sets maximum auto-pop delay (delay before hiding) on an instance of
|
|
||||||
wx.ToolTip.
|
|
||||||
NOTE: The tooltip's SetDelay method is used just to identify the correct
|
|
||||||
tooltip.
|
|
||||||
'''
|
|
||||||
test_value = 12345
|
|
||||||
# Set initial delay just to identify tooltip.
|
|
||||||
tooltip.SetDelay(test_value)
|
|
||||||
handles = []
|
|
||||||
EnumWindows(_get_tooltip_handles, handles)
|
|
||||||
for hwnd in handles:
|
|
||||||
if SendMessage(hwnd, TTM_GETDELAYTIME, TTDT_INITIAL) == test_value:
|
|
||||||
SendMessage(hwnd, TTM_SETDELAYTIME, TTDT_AUTOPOP, 32767)
|
|
||||||
tooltip.SetDelay(500) # Restore default value
|
|
||||||
|
|
||||||
|
|
||||||
class Waitbar(Thread):
|
|
||||||
|
|
||||||
def __init__(self, title='Waitbar', can_abort=True, max_val=100):
|
|
||||||
Thread.__init__(self) # Initialize thread
|
|
||||||
self.title = title
|
|
||||||
self.can_abort = can_abort
|
|
||||||
self.max_val = max_val
|
|
||||||
InitCommonControls()
|
|
||||||
self.hinst = GetModuleHandle(None)
|
|
||||||
self.started = False
|
|
||||||
self.position = 0
|
|
||||||
self.do_update = False
|
|
||||||
self.start() # Run the thread
|
|
||||||
while not self.started:
|
|
||||||
sleep(0.1) # Wait until the dialog is ready
|
|
||||||
|
|
||||||
def DlgProc(self, hwnd, uMsg, wParam, lParam):
|
|
||||||
if uMsg == win32con.WM_DESTROY:
|
|
||||||
api_SetWindowLong(self.dialog,
|
|
||||||
win32con.GWL_WNDPROC,
|
|
||||||
self.oldWndProc)
|
|
||||||
if uMsg == win32con.WM_CLOSE:
|
|
||||||
self.started = False
|
|
||||||
if uMsg == win32con.WM_COMMAND and self.can_abort:
|
|
||||||
self.started = False
|
|
||||||
return CallWindowProc(self.oldWndProc, hwnd, uMsg, wParam, lParam)
|
|
||||||
|
|
||||||
def BuildWindow(self):
|
|
||||||
width = 400
|
|
||||||
height = 100
|
|
||||||
self.dialog = CreateWindowEx(
|
|
||||||
win32con.WS_EX_TOPMOST,
|
|
||||||
WC_DIALOG,
|
|
||||||
self.title + ' (0%)',
|
|
||||||
win32con.WS_VISIBLE | win32con.WS_OVERLAPPEDWINDOW,
|
|
||||||
int(round(
|
|
||||||
GetSystemMetrics(win32con.SM_CXSCREEN) * .5 - width * .5)),
|
|
||||||
int(round(
|
|
||||||
GetSystemMetrics(win32con.SM_CYSCREEN) * .5 - height * .5)),
|
|
||||||
width,
|
|
||||||
height,
|
|
||||||
0,
|
|
||||||
0,
|
|
||||||
self.hinst,
|
|
||||||
None)
|
|
||||||
self.progbar = CreateWindow(
|
|
||||||
# win32con.WS_EX_DLGMODALFRAME,
|
|
||||||
'msctls_progress32',
|
|
||||||
'',
|
|
||||||
win32con.WS_VISIBLE | win32con.WS_CHILD,
|
|
||||||
10,
|
|
||||||
10,
|
|
||||||
width - 30,
|
|
||||||
20,
|
|
||||||
self.dialog,
|
|
||||||
0,
|
|
||||||
0,
|
|
||||||
None)
|
|
||||||
if self.can_abort:
|
|
||||||
self.button = CreateWindow(
|
|
||||||
# win32con.WS_EX_DLGMODALFRAME,
|
|
||||||
'BUTTON',
|
|
||||||
'Cancel',
|
|
||||||
win32con.WS_VISIBLE | win32con.WS_CHILD | win32con.BS_PUSHBUTTON, # @IgnorePep8
|
|
||||||
int(width / 2.75),
|
|
||||||
40,
|
|
||||||
100,
|
|
||||||
20,
|
|
||||||
self.dialog,
|
|
||||||
0,
|
|
||||||
0,
|
|
||||||
None)
|
|
||||||
self.oldWndProc = SetWindowLong(
|
|
||||||
self.dialog,
|
|
||||||
win32con.GWL_WNDPROC,
|
|
||||||
self.DlgProc)
|
|
||||||
SendMessage(self.progbar, PBM_SETRANGE, 0, MAKELPARAM(0, self.max_val))
|
|
||||||
# win32gui.SendMessage(self.progbar, PBM_SETSTEP, 0, 10)
|
|
||||||
# win32gui.SendMessage(self.progbar, PBM_SETMARQUEE, 0, 0)
|
|
||||||
ShowWindow(self.progbar, win32con.SW_SHOW)
|
|
||||||
|
|
||||||
def run(self):
|
|
||||||
self.BuildWindow()
|
|
||||||
self.started = True
|
|
||||||
while self.started:
|
|
||||||
PumpWaitingMessages()
|
|
||||||
if self.do_update:
|
|
||||||
SendMessage(self.progbar, PBM_SETPOS,
|
|
||||||
int(self.position % self.max_val), 0)
|
|
||||||
percentage = int(round(100.0 * self.position / self.max_val))
|
|
||||||
SendMessage(self.dialog, WM_SETTEXT, 0,
|
|
||||||
self.title + ' (%d%%)' % percentage)
|
|
||||||
# SendMessage(self.progbar, PBM_STEPIT, 0, 0)
|
|
||||||
self.do_update = False
|
|
||||||
sleep(0.1)
|
|
||||||
PostQuitMessage(0)
|
|
||||||
DestroyWindow(self.dialog)
|
|
||||||
|
|
||||||
def update(self, pos):
|
|
||||||
if self.started:
|
|
||||||
if not self.do_update:
|
|
||||||
self.position = pos
|
|
||||||
self.do_update = True
|
|
||||||
return True
|
|
||||||
return False
|
|
||||||
|
|
||||||
def close(self):
|
|
||||||
self.started = False
|
|
||||||
|
|
||||||
|
|
||||||
# class Waitbar2(Dialog, Thread):
|
|
||||||
# def __init__(self, title='Waitbar'):
|
|
||||||
# template = [[title, (0, 0, 215, 36),
|
|
||||||
# (win32con.DS_MODALFRAME | win32con.WS_POPUP |
|
|
||||||
# win32con.WS_VISIBLE | win32con.WS_CAPTION |
|
|
||||||
# win32con.WS_SYSMENU | win32con.DS_SETFONT |
|
|
||||||
# win32con.WS_GROUP | win32con.WS_EX_TOPMOST),
|
|
||||||
# | win32con.DS_SYSMODAL),
|
|
||||||
# None, (8, "MS Sans Serif")], ]
|
|
||||||
# Dialog.__init__(self, id=template)
|
|
||||||
# Thread.__init__(self) # Initialize thread
|
|
||||||
# self.started = False
|
|
||||||
# self.start() # Run the thread
|
|
||||||
# while not self.started:
|
|
||||||
# sleep(0.1) # Wait until the dialog is ready
|
|
||||||
#
|
|
||||||
# def OnInitDialog(self):
|
|
||||||
# rc = Dialog.OnInitDialog(self)
|
|
||||||
# self.pbar = CreateProgressCtrl()
|
|
||||||
# self.pbar.CreateWindow (win32con.WS_CHILD | win32con.WS_VISIBLE,
|
|
||||||
# (10, 10, 310, 24), self, 1001)
|
|
||||||
# self.started = True
|
|
||||||
# return rc
|
|
||||||
#
|
|
||||||
# def run(self):
|
|
||||||
# self.DoModal()
|
|
||||||
#
|
|
||||||
# def update(self, pos):
|
|
||||||
# self.pbar.SetPos(int(pos))
|
|
||||||
#
|
|
||||||
# def close(self):
|
|
||||||
# self.OnCancel()
|
|
||||||
|
|
||||||
|
|
||||||
class WarnDlg(Thread):
|
|
||||||
|
|
||||||
def __init__(self, message='', title='Warning!'):
|
|
||||||
Thread.__init__(self) # Initialize thread
|
|
||||||
self.title = title
|
|
||||||
self.message = message
|
|
||||||
self.start() # Run the thread
|
|
||||||
|
|
||||||
def run(self):
|
|
||||||
# MessageBox(self.message, self.title, win32con.MB_ICONWARNING)
|
|
||||||
MessageBox(0, self.message, self.title,
|
|
||||||
win32con.MB_ICONWARNING | win32con.MB_SYSTEMMODAL)
|
|
||||||
|
|
||||||
|
|
||||||
class ErrorDlg(Thread):
|
|
||||||
|
|
||||||
def __init__(self, message='', title='Error!', blocking=False):
|
|
||||||
Thread.__init__(self) # Initialize thread
|
|
||||||
self.title = title
|
|
||||||
self.message = message
|
|
||||||
if blocking:
|
|
||||||
self.run() # Run without threading
|
|
||||||
else:
|
|
||||||
self.start() # Run in thread
|
|
||||||
|
|
||||||
def run(self):
|
|
||||||
# MessageBox(self.message, self.title, win32con.MB_ICONERROR)
|
|
||||||
MessageBox(0, self.message, self.title,
|
|
||||||
win32con.MB_ICONERROR | win32con.MB_SYSTEMMODAL)
|
|
||||||
|
|
||||||
|
|
||||||
if __name__ == '__main__':
|
|
||||||
WarnDlg('This is an example of a warning', 'Warning!')
|
|
||||||
ErrorDlg('This is an example of an error message')
|
|
||||||
wb = Waitbar('Waitbar example')
|
|
||||||
# wb2 = Waitbar2('Waitbar example')
|
|
||||||
for i in range(20):
|
|
||||||
print(wb.update(i * 5))
|
|
||||||
# wb2.update(i)
|
|
||||||
sleep(0.1)
|
|
||||||
wb.close()
|
|
||||||
# wb2.close()
|
|
||||||
Loading…
Reference in New Issue