279 lines
39 KiB
Plaintext
279 lines
39 KiB
Plaintext
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H‹ ĆH‰Š I‹ ĆH‰Š H‹ ĂH‰Š I‹K8HŻ ĂH�ÂI‰K8H˙ŔI;Â|‘H‹\$H‹l$H‹t$ H‹|$(A^ĂĚĚĚĚĚĚĚĚĚĚ@USWAUAWHŤ¬$`ý˙˙H�ě H‹š{ H3ÄH‰…€ H‹ž ‹Úş˙˙˙˙Icř˙�¨ D‹ĂHŤL$0H‹ĐL‹čč~ü˙˙L‹|$8M…˙uI‹Ĺéz L‰¤$� …˙�B IŤG˙L‹çH;řŹ2 L‰´$� ˙ľ\ L‹t$XH‰D$ L;t$`Ťů L‹ťh E3ŔL‹T$@H‰´$Ř f�IŤ˙I‹đH…˙Ž) I‹ÚIŻÜf „ ňBI‹ÂHŻĆI‹ĘHŻĎňBf/ĐňB v f/Čv,f/Ęë
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{W MŤO˙D‹ÇHŤ [ H‹ ˙\W 3ŔL‹¤$� H‹Ť€ H3Ěčó; H�Ä A_A]_[]ĂĚĚĚĚ@USVWAUHŤ¬$Pý˙˙H�ě° H‹«u H3ÄH‰…� H‹"� ‹Úş˙˙˙˙Icř˙�¨ D‹ĂHŤL$@H‹ĐH‰D$(H‹đčŠö˙˙L‹l$HM…íuH‹Ćéq …˙�I IŤE˙H‰|$ H;řŹ7 L‰¤$č ˙ĐV L‹d$hH‰D$0L;d$pŤţ H‹˝x E3ÉH‹\$PL‰´$¨ L‰Ľ$ @ „ MŤu˙M‹ůM…öŽá L‹l$ H‹óIŻőH÷�H‹L‹ĂMŻÇL‹ËMŻÎI‹8I‹9H;ŃŽ H;Č}H;ĐŚ# H‰I‰ 9L‹ M‹ĎM‹ÖI‹ÁHŻĂHÇL9} �HĂI˙ÁL9|őI‹ĘHŻËHĎL;}H‹ĂH÷ŘHČI˙ĘL;|őM;Ę)I‹ĘI‹ŃHŻËHŻÓH‹9I˙ÁI˙ĘL‹:H‰:L‰9M;Ę~—M;ŐI‹ÁIMÇM;éL‹řMMÖM‹ňI;ÂŚ7˙˙˙L‹l$HE3ÉHcL$@H‰L$`H�ů˙Žˇ fff „ HŤÍ H‹„x L‹DxH˙ČL;Ŕ|RH‹DxIŻŔH+řH‰˝x L‰LxH‹L$`H‹˝x H˙ÉH‰L$`H�ů˙±ë6H;ČŽíţ˙˙H;ĐŽÝţ˙˙H‰I‰ 8éŘţ˙˙H|ÍxH‰˝x H˙DĚxH‹˝x L‹l$HH‹\$PL‹d$hI˙ÄL‰d$hL;d$pŚGţ˙˙H‹t$(H‹D$0L‹Ľ$ L‹´$¨ H‹Č˙ T L‹¤$č H‹Ćë H‹
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xT MŤM˙D‹ÇHŤX H‹ ˙YT 3ŔH‹Ť� H3Ěčř8 H�Ä° A]_^[]ĂĚĚĚĚĚĚĚĚĚĚ@USVWAUHŤ¬$Pý˙˙H�ě° H‹«r H3ÄH‰…� H‹"• ‹Úş˙˙˙˙Icř˙�¨ D‹ĂHŤL$@H‹ĐH‰D$(H‹đčŠó˙˙L‹l$HM…íuH‹Ćén …˙�F IŤE˙H‰|$ H;řŹ4 L‰¤$č ˙ĐS L‹d$hH‰D$0L;d$pŤű H‹˝x E3ÉH‹\$PL‰´$¨ L‰Ľ$ @ „ MŤu˙M‹ůM…öŽß L‹l$ H‹óIŻőH÷�‹L‹ĂMŻÇL‹ËMŻÎA‹8A‹9;ŃŽ! ;Č};ĐŚ% ‰A‰ 9D‹ M‹ĎM‹Ö D I‹ÁHŻĂHÇD9} �HĂI˙ÁD9|őI‹ĘHŻËHĎD;}H‹ĂH÷ŘHČI˙ĘD;|őM;Ę'I‹ŃI‹ĘHŻÓHŻËD‹:I˙ÁI˙Ę‹9‰:D‰9M;Ę~™M;ŐI‹ÁIMÇM;éL‹řMMÖM‹ňI;ÂŚ9˙˙˙L‹l$HE3ÉHcL$@H‰L$`H�ů˙Ž @ f „ HŤÍ H‹„x L‹DxH˙ČL;Ŕ|OH‹DxIŻŔH+řH‰˝x L‰LxH‹L$`H‹˝x H˙ÉH‰L$`H�ů˙±ë3;ČŽéţ˙˙;ĐŽŰţ˙˙‰A‰ 8éÖţ˙˙H|ÍxH‰˝x H˙DĚxH‹˝x L‹l$HH‹\$PL‹d$hI˙ÄL‰d$hL;d$pŚJţ˙˙H‹t$(H‹D$0L‹Ľ$ L‹´$¨ H‹Č˙ Q L‹¤$č H‹Ćë H‹
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{Q MŤM˙D‹ÇHŤ U H‹ ˙\Q 3ŔH‹Ť� H3Ěčű5 H�Ä° A]_^[]ĂĚĚĚĚĚĚĚĚĚĚĚĚĚH�ěHÇD$8 HŤíü˙˙H‰D$0LŤ
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ćT HŤ×ö˙˙H‰D$ čť- H�ÄHĂĚĚĚĚĚĚĚĚ@USVWAUAVAWHŤ¬$pü˙˙H�ě� H‹Wo H3ÄH‰…€ H‹Y L‹ń‹y‹ňH‹Â‘ ą Mcř˙�h E3ÉH‹ÓL‹Ŕ‹ĎH‹˘‘ ˙�Ŕ D‹ÎHŤL$0L‹ŔH‰D$ I‹ÖH‹Řčń˙˙L‹l$8M…íuH‹Ăé× E…˙�® IŤE˙I‹÷L;řŹž L‰¤$Ř ˙XP JŤ í H‰D$(˙EO L‹d$XL‹ŘL;d$`ŤI H‹ťp E3ÉH‹|$H I‹ÉM…í~2L‹…h H‹Ó @ ff „ I‹ LD$@I‰ËH‰
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H˙ÁH×I;Í|ćMŤu˙M‹ůM…öŽ/ f D ňCűňA óf/ŃňCóv(f/ÁvUL‹ÇLŻĆf/ÂH‹Ďv%ňAóňC űIŻĎë#f/Čv-L‹ÇLŻĆf/ĐëÖňAóňC óIŻÎH‹I‹I‰H‰ňA óM‹ĎM‹Ö�fC/ Ëv „ I˙ÁfC/ ËwőňCÓf/Ávf „ ňCDÓřI˙Ęf/ÁwđM;ĘAK‹ÓI‹ĘňCËL‹ÇK‰ËňCÓI˙ĘMŻÁHŻĎI‹I˙ÁH‹I‰H‰M;ĘŽz˙˙˙L;ÖI‹ÁIMÇI;ńL‹řMMÖM‹ňI;ÂŚÚţ˙˙E3ÉHcL$0H‰L$PH�ů˙Ž’ H‹„Íh H‹TĚhH˙ČH;ĐH‹DÍh|DHŻÂH)…h H‹„Íh HŻDĚhH+ŘH‰ťp L‰LĚhH‹L$PH‹ťp H˙ÉH‰L$PH�ů˙¤ë"H…h HśÍh H‰ťp H˙DĚhH‹ťp L‹l$8H‹|$HL‹d$XI˙ÄL‰d$XL;d$`ŚÎý˙˙H‹\$ I‹Ë˙čL H‹L$(˙=M L‹¤$Ř H‹Ăë H‹
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™M MŤM˙E‹ÇHŤ;Q H‹ ˙zM 3ŔH‹Ť€ H3Ěč2 H�Ä� A_A^A]_^[]ĂĚĚĚĚĚĚĚ@USVWAUAVAWHŤ¬$pü˙˙H�ě� H‹Çk H3ÄH‰…€ H‹Y L‹ń‹y‹ňH‹2Ž ą Mcř˙�h E3ÉH‹ÓL‹Ŕ‹ĎH‹Ž ˙�Ŕ D‹ÎHŤL$0L‹ŔH‰D$ I‹ÖH‹Řč�í˙˙L‹l$8M…íuH‹Ăé× E…˙�® IŤE˙I‹÷L;řŹž L‰¤$Ř ˙ČL JŤ H‰D$(˙µK L‹d$XL‹ŘL;d$`ŤI H‹ťp E3ÉH‹|$H I‹ÉM…í~2L‹…h H‹Ó @ ff „ A‹ LD$@A‰‹H‰
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H˙ÁH×I;Í|ćMŤu˙M‹ůM…öŽ. f D óC»óA ł/ŃóCłv&/ÁvRL‹ÇLŻĆ/ÂH‹Ďv#óAłóC »IŻĎë!/Čv,L‹ÇLŻĆ/ĐëŘóAłóC łIŻÎH‹I‹I‰H‰óA łM‹ĎM‹Öf D C/ ‹vf „ I˙ÁC/ ‹wöóC“/Ávfff „ óCD“üI˙Ę/ÁwńM;ĘAC‹“I‹ĘóC‹L‹ÇC‰‹óC“I˙ĘMŻÁHŻĎI‹I˙ÁH‹I‰H‰M;ĘŽ{˙˙˙L;ÖI‹ÁIMÇI;ńL‹řMMÖM‹ňI;ÂŚŰţ˙˙E3ÉHcL$0H‰L$PH�ů˙Ž“ @ H‹„Íh H‹TĚhH˙ČH;ĐH‹DÍh|DHŻÂH)…h H‹„Íh HŻDĚhH+ŘH‰ťp L‰LĚhH‹L$PH‹ťp H˙ÉH‰L$PH�ů˙¤ë"H…h HśÍh H‰ťp H˙DĚhH‹ťp L‹l$8H‹|$HL‹d$XI˙ÄL‰d$XL;d$`ŚÎý˙˙H‹\$ I‹Ë˙XI H‹L$(˙I L‹¤$Ř H‹Ăë H‹
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J MŤM˙E‹ÇHŤ«M H‹ ˙ęI 3ŔH‹Ť€ H3Ěč‰. H�Ä� A_A^A]_^[]ĂĚĚĚĚĚĚĚ@USVWAUAVAWHŤ¬$pü˙˙H�ě� H‹7h H3ÄH‰…€ H‹Y L‹ń‹y‹ňH‹˘Š ą Mcř˙�h E3ÉH‹ÓL‹Ŕ‹ĎH‹‚Š ˙�Ŕ D‹ÎHŤL$0L‹ŔH‰D$ I‹ÖH‹Řčńé˙˙L‹l$8M…íuH‹Ăé´ L‰¤$Ř E…˙�{ IŤE˙M‹çL;řŹk ˙8I JŤ í H‰D$(˙%H H‹L$XL‹ŘH;L$`Ť& H‹˝p E3ÉH‹t$H I‹ÉM…í~2L‹…h H‹× @ ff „ I‹ LD$@I‰ËH‰
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H˙ÁHÖI;Í|ćMŤu˙M‹ůM…öŽő f D K‹űK‹ ăK‹óH;Ń~"H;Č}MH;Đ}"K‰ăH‹ÖK‰ űH‹ÎIŻÔIŻĎë H;Č~+H;ĐŢK‰ăH‹ÖK‰ óH‹ÎIŻÔIŻÎH‹9L‹:H‰:L‰9K‹ ăM‹×I‹Ć K9 Ó} I˙ÂK9 Ó|÷I; Ă} H˙ČI; Ă|÷L;Đ9I‹ ĂL‹ĆK‹ÓK‰ ÓI‰ĂH‹ĐMŻÂHŻÖM‹ 8I˙ÂH˙ČH‹ :I‰ 8L‰ :L;Đ~¤I;ÄI‹ĘIMĎM;âL‹ůIMĆL‹đH;ČŚ˙˙˙E3ÉHcL$0H‰L$PH�ů˙Ž¤ @ f „ HŤÍ H‹„h L‹DhH˙ČL;Ŕ|IH‹DhIŻŔH)…h H‹„h HŻDhH+řH‰˝p L‰LhH‹L$PH‹˝p H˙ÉH‰L$PH�ů˙śë'H‹DÍhH…h HĽÍh H‰˝p H˙DĚhH‹˝p L‹l$8H‹t$HH‹D$XH˙ŔH‰D$XH;D$`Śńý˙˙H‹\$ I‹Ë˙ëE H‹L$(˙@F H‹Ăë H‹
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¤F MŤM˙E‹ÇHŤFJ H‹ ˙…F 3ŔL‹¤$Ř H‹Ť€ H3Ěč + H�Ä� A_A^A]_^[]ĂĚĚĚĚĚĚĚĚĚĚ@USVWAUAVAWHŤ¬$pü˙˙H�ě� H‹Çd H3ÄH‰…€ H‹Y L‹ń‹y‹ňH‹2‡ ą Mcř˙�h E3ÉH‹ÓL‹Ŕ‹ĎH‹‡ ˙�Ŕ D‹ÎHŤL$0L‹ŔH‰D$ I‹ÖH‹Řč�ć˙˙L‹l$8M…íuH‹Ăé¤ L‰¤$Ř E…˙�k IŤE˙M‹çL;řŹ[ ˙ČE JŤ H‰D$(˙µD H‹L$XL‹ŘH;L$`Ť H‹˝p E3ÉH‹t$H I‹ÉM…í~2L‹…h H‹× @ ff „ A‹ LD$@A‰‹H‰
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H˙ÁHÖI;Í|ćMŤu˙M‹ůM…öŽđ f D C‹»C‹ ŁC‹ł;Ń~ ;Č}J;Đ} C‰ŁH‹ÖC‰ »H‹ÎIŻÔIŻĎë ;Č~*;ĐŕC‰ŁH‹ÖC‰ łH‹ÎIŻÔIŻÎH‹9L‹:H‰:L‰9C‹ ŁM‹×I‹ĆC9 “} f�I˙ÂC9 “|÷A; �}
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�H˙ČA; �|÷L;Đ9C‹“L‹ĆA‹ �C‰ “A‰�H‹ĐMŻÂHŻÖM‹ 8I˙ÂH˙ČH‹ :I‰ 8L‰ :L;Đ~ˇI;ÄI‹ĘIMĎM;âL‹ůIMĆL‹đH;ČŚ˙˙˙E3ÉHcL$0H‰L$PH�ů˙Ž™ f�HŤÍ H‹„h L‹DhH˙ČL;Ŕ|IH‹DhIŻŔH)…h H‹„h HŻDhH+řH‰˝p L‰LhH‹L$PH‹˝p H˙ÉH‰L$PH�ů˙śë'H‹DÍhH…h HĽÍh H‰˝p H˙DĚhH‹˝p L‹l$8H‹t$HH‹D$XH˙ŔH‰D$XH;D$`Śţ˙˙H‹\$ I‹Ë˙‹B H‹L$(˙ŕB H‹Ăë H‹
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|
|||
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|
|||
|
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Fill missing values (NaNs) with most recent non-missing values.
|
|||
|
|
|
|||
|
|
Filling proceeds along the specified axis from small index values to large
|
|||
|
|
index values.
|
|||
|
|
|
|||
|
|
Parameters
|
|||
|
|
----------
|
|||
|
|
a : array_like
|
|||
|
|
Input array. If `a` is not an array, a conversion is attempted.
|
|||
|
|
n : {int, None}, optional
|
|||
|
|
How far to push values. If the most recent non-NaN array element is
|
|||
|
|
more than `n` index positions away, than a NaN is returned. The default
|
|||
|
|
(n = None) is to push the entire length of the slice. If `n` is an integer
|
|||
|
|
it must be nonnegative.
|
|||
|
|
axis : int, optional
|
|||
|
|
Axis along which the elements of the array are pushed. The default
|
|||
|
|
(axis=-1) is to push along the last axis of the input array.
|
|||
|
|
|
|||
|
|
Returns
|
|||
|
|
-------
|
|||
|
|
y : ndarray
|
|||
|
|
An array with the same shape and dtype as `a`.
|
|||
|
|
|
|||
|
|
See also
|
|||
|
|
--------
|
|||
|
|
bottleneck.replace: Replace specified value of an array with new value.
|
|||
|
|
|
|||
|
|
Examples
|
|||
|
|
--------
|
|||
|
|
>>> a = np.array([5, np.nan, np.nan, 6, np.nan])
|
|||
|
|
>>> bn.push(a)
|
|||
|
|
array([ 5., 5., 5., 6., 6.])
|
|||
|
|
>>> bn.push(a, n=1)
|
|||
|
|
array([ 5., 5., nan, 6., 6.])
|
|||
|
|
>>> bn.push(a, n=2)
|
|||
|
|
array([ 5., 5., 5., 6., 6.])
|
|||
|
|
|
|||
|
|
Bottleneck non-reducing functions that operate along an axis. partition(a, kth, axis=-1)
|
|||
|
|
|
|||
|
|
Partition array elements along given axis.
|
|||
|
|
|
|||
|
|
A 1d array B is partitioned at array index `kth` if three conditions
|
|||
|
|
are met: (1) B[kth] is in its sorted position, (2) all elements to the
|
|||
|
|
left of `kth` are less than or equal to B[kth], and (3) all elements
|
|||
|
|
to the right of `kth` are greater than or equal to B[kth]. Note that
|
|||
|
|
the array elements in conditions (2) and (3) are in general unordered.
|
|||
|
|
|
|||
|
|
Shuffling the input array may change the output. The only guarantee is
|
|||
|
|
given by the three conditions above.
|
|||
|
|
|
|||
|
|
This functions is not protected against NaN. Therefore, you may get
|
|||
|
|
unexpected results if the input contains NaN.
|
|||
|
|
|
|||
|
|
Parameters
|
|||
|
|
----------
|
|||
|
|
a : array_like
|
|||
|
|
Input array. If `a` is not an array, a conversion is attempted.
|
|||
|
|
kth : int
|
|||
|
|
The value of the element at index `kth` will be in its sorted
|
|||
|
|
position. Smaller (larger) or equal values will be to the left
|
|||
|
|
(right) of index `kth`.
|
|||
|
|
axis : {int, None}, optional
|
|||
|
|
Axis along which the partition is performed. The default
|
|||
|
|
(axis=-1) is to partition along the last axis.
|
|||
|
|
|
|||
|
|
Returns
|
|||
|
|
-------
|
|||
|
|
y : ndarray
|
|||
|
|
A partitioned copy of the input array with the same shape and
|
|||
|
|
type of `a`.
|
|||
|
|
|
|||
|
|
See Also
|
|||
|
|
--------
|
|||
|
|
bottleneck.argpartition: Indices that would partition an array
|
|||
|
|
|
|||
|
|
Notes
|
|||
|
|
-----
|
|||
|
|
Unexpected results may occur if the input array contains NaN.
|
|||
|
|
|
|||
|
|
Examples
|
|||
|
|
--------
|
|||
|
|
Create a numpy array:
|
|||
|
|
|
|||
|
|
>>> a = np.array([1, 0, 3, 4, 2])
|
|||
|
|
|
|||
|
|
Partition array so that the first 3 elements (indices 0, 1, 2) are the
|
|||
|
|
smallest 3 elements (note, as in this example, that the smallest 3
|
|||
|
|
elements may not be sorted):
|
|||
|
|
|
|||
|
|
>>> bn.partition(a, kth=2)
|
|||
|
|
array([1, 0, 2, 4, 3])
|
|||
|
|
|
|||
|
|
Now Partition array so that the last 2 elements are the largest 2
|
|||
|
|
elements:
|
|||
|
|
|
|||
|
|
>>> bn.partition(a, kth=3)
|
|||
|
|
array([1, 0, 2, 3, 4])
|
|||
|
|
|
|||
|
|
nanrankdata(a, axis=None)
|
|||
|
|
|
|||
|
|
Ranks the data, dealing with ties and NaNs appropriately.
|
|||
|
|
|
|||
|
|
Equal values are assigned a rank that is the average of the ranks that
|
|||
|
|
would have been otherwise assigned to all of the values within that set.
|
|||
|
|
Ranks begin at 1, not 0.
|
|||
|
|
|
|||
|
|
NaNs in the input array are returned as NaNs.
|
|||
|
|
|
|||
|
|
Parameters
|
|||
|
|
----------
|
|||
|
|
a : array_like
|
|||
|
|
Input array. If `a` is not an array, a conversion is attempted.
|
|||
|
|
axis : {int, None}, optional
|
|||
|
|
Axis along which the elements of the array are ranked. The default
|
|||
|
|
(axis=None) is to rank the elements of the flattened array.
|
|||
|
|
|
|||
|
|
Returns
|
|||
|
|
-------
|
|||
|
|
y : ndarray
|
|||
|
|
An array with the same shape as `a`. The dtype is 'float64'.
|
|||
|
|
|
|||
|
|
See also
|
|||
|
|
--------
|
|||
|
|
bottleneck.rankdata: Ranks the data, dealing with ties and appropriately.
|
|||
|
|
|
|||
|
|
Examples
|
|||
|
|
--------
|
|||
|
|
>>> bn.nanrankdata([np.nan, 2, 2, 3])
|
|||
|
|
array([ nan, 1.5, 1.5, 3. ])
|
|||
|
|
>>> bn.nanrankdata([[np.nan, 2], [2, 3]])
|
|||
|
|
array([ nan, 1.5, 1.5, 3. ])
|
|||
|
|
>>> bn.nanrankdata([[np.nan, 2], [2, 3]], axis=0)
|
|||
|
|
array([[ nan, 1.],
|
|||
|
|
[ 1., 2.]])
|
|||
|
|
>>> bn.nanrankdata([[np.nan, 2], [2, 3]], axis=1)
|
|||
|
|
array([[ nan, 1.],
|
|||
|
|
[ 1., 2.]])
|
|||
|
|
|
|||
|
|
Xu € @ € đ” € hu € €. € ठ€ xu € P@ € đ € �u € ĐI € Đ› € ”u € ĐM € @� € rankdata(a, axis=None)
|
|||
|
|
|
|||
|
|
Ranks the data, dealing with ties appropriately.
|
|||
|
|
|
|||
|
|
Equal values are assigned a rank that is the average of the ranks that
|
|||
|
|
would have been otherwise assigned to all of the values within that set.
|
|||
|
|
Ranks begin at 1, not 0.
|
|||
|
|
|
|||
|
|
Parameters
|
|||
|
|
----------
|
|||
|
|
a : array_like
|
|||
|
|
Input array. If `a` is not an array, a conversion is attempted.
|
|||
|
|
axis : {int, None}, optional
|
|||
|
|
Axis along which the elements of the array are ranked. The default
|
|||
|
|
(axis=None) is to rank the elements of the flattened array.
|
|||
|
|
|
|||
|
|
Returns
|
|||
|
|
-------
|
|||
|
|
y : ndarray
|
|||
|
|
An array with the same shape as `a`. The dtype is 'float64'.
|
|||
|
|
|
|||
|
|
See also
|
|||
|
|
--------
|
|||
|
|
bottleneck.nanrankdata: Ranks the data dealing with ties and NaNs.
|