B Y @sdZddlZddlZddddddd d d d g ZdddZdddZd ddZd!ddZd"ddZ d#ddZ d$dd Z d%dd Z d&dd Z d'dd Zd(ddZddZd)ddZdS)*z.argmin)r )r)rrrr rrrrr+scCsdd}t|||||dS)z(Slow move_argmax for unaccelerated dtypecSstj|dd}tdg|j}tddd||<||}ytj||d}Wnztk r|}t|}tj|tj |dtj ||d tj }|jdkrtj }ntj||d}tj ||<YnX|S)NF)rr )r )rr)rrrrZ nanargmaxrrrrrargmaxr r!r"r#)rr r$r%r&rrrr'Es   zmove_argmax..argmax)r )r)rrrr r'rrrr CscCsttj||||dS)z(Slow move_median for unaccelerated dtype)r )rrZ nanmedian)rrrr rrrr [scCstt||||dS)z&Slow move_rank for unaccelerated dtype)r )rlastrank)rrrr rrrr `sc Ksttj|dd}|dkr|}n2|}||kr>d}t|||fn|dkrNtd|jdkr`td|dkrptd|d krtd ||j|krtd t|jjtjrt |}n t |j}t dg|j} t | } t tt d xbt|j|D]P} t|| d } t | d | | d | |<| | |<||| fd |i||| <qWWdQRXt||||} tj|| <|S)z>Generic moving window function implemented with a python loop.F)rNz1min_count (%d) cannot be greater than window (%d)rz&`min_count` must be greater than zero.z(moving window functions require ndim > 0z)An `axis` value of None is not supported.z`window` must be at least 1.z`window` is too long.ignorer )rrrrshape issubclassdtypetypeZinexactZ empty_likeemptyrlistwarningscatch_warnings simplefilterrangemin_maskr")funcrrrr kwargsZmcmsgyidx1idx2iwinr%rrrrgs@     * rc Cs||k|}tdg|j}tdg|j}tdg|j}t|d||<td| ||<td|||<||}|||}tj|jtjd} ||k| |<|||k| |<| S)N)r-)Zcumsumrrrr/r+bool) rrrr nr;r<Zidx3Znidx1r%rrrr6s  r6c Cs`tj|dd}|j}|jdkrlt|j}||tj||jd}| tj |jdkrh|jdkrhtj }|St dg|}t dd||<t dg|}d||<t | |}||}||k |} ||k |} | | | dd }||d}d |d }|dkr0|dkrd}t ||r\tj }n,t||dkdt|t ||tj |S) a The ranking of the last element along the axis, ignoring NaNs. The ranking is normalized to be between -1 and 1 instead of the more common 1 and N. The results are adjusted for ties. Parameters ---------- a : ndarray Input array. If `a` is not an array, a conversion is attempted. axis : int, optional The axis over which to rank. By default (axis=-1) the ranking (and reducing) is performed over the last axis. Returns ------- d : array In the case of, for example, a 2d array of shape (n, m) and axis=1, the output will contain the rank (normalized to be between -1 and 1 and adjusted for ties) of the the last element of each row. The output in this example will have shape (n,). Examples -------- Create an array: >>> y1 = larry([1, 2, 3]) What is the rank of the last element (the value 3 in this example)? It is the largest element so the rank is 1.0: >>> import numpy as np >>> from la.afunc import lastrank >>> x1 = np.array([1, 2, 3]) >>> lastrank(x1) 1.0 Now let's try an example where the last element has the smallest value: >>> x2 = np.array([3, 2, 1]) >>> lastrank(x2) -1.0 Here's an example where the last element is not the minimum or maximum value: >>> x3 = np.array([1, 3, 4, 5, 2]) >>> lastrank(x3) -0.5 F)rr)r-r)Nr g?g@g?)rrrsizer0r+popr/r-Zfillr"rrsumZputmask) rr rr+rZindlastZindlast2r@Z a_indlastgerrrr(s:5        r()Nr )Nr )Nr r)Nr r)Nr )Nr )Nr )Nr )Nr )Nr )Nr )r )__doc__r1Znumpyr__all__rrrrrrrr r r rr6r(rrrrs$            &