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Zjotه(YڕP̴2BDҺ?ʒD^%tEXtdate:create2016-01-06T18:24:46-08:007a%tEXtdate:modify2016-01-06T18:24:46-08:00ZtEXtpdf:VersionPDF-1.5 \ 9IENDB`xarray-0.9.1/doc/_static/dataset-diagram-logo.tex0000644131111500116100000002525212652753364022054 0ustar shoyereng00000000000000\documentclass[class=minimal,border=0pt,convert={size=600,outext=.png}]{standalone} % \documentclass[class=minimal,border=0pt]{standalone} \usepackage[scaled]{helvet} \renewcommand*\familydefault{\sfdefault} % =========================================================================== % The code below (used to define the \tikzcuboid command) is copied, % unmodified, from a tex.stackexchange.com answer by the user "Tom Bombadil": % http://tex.stackexchange.com/a/29882/8335 % % It is licensed under the Creative Commons Attribution-ShareAlike 3.0 % Unported license: http://creativecommons.org/licenses/by-sa/3.0/ % =========================================================================== \usepackage[usenames,dvipsnames]{color} \usepackage{tikz} \usepackage{keyval} \usepackage{ifthen} %==================================== %emphasize vertices --> switch and emph style (e.g. thick,black) %==================================== \makeatletter % Standard Values for Parameters \newcommand{\tikzcuboid@shiftx}{0} \newcommand{\tikzcuboid@shifty}{0} \newcommand{\tikzcuboid@dimx}{3} \newcommand{\tikzcuboid@dimy}{3} \newcommand{\tikzcuboid@dimz}{3} \newcommand{\tikzcuboid@scale}{1} \newcommand{\tikzcuboid@densityx}{1} \newcommand{\tikzcuboid@densityy}{1} \newcommand{\tikzcuboid@densityz}{1} \newcommand{\tikzcuboid@rotation}{0} \newcommand{\tikzcuboid@anglex}{0} \newcommand{\tikzcuboid@angley}{90} \newcommand{\tikzcuboid@anglez}{225} \newcommand{\tikzcuboid@scalex}{1} \newcommand{\tikzcuboid@scaley}{1} \newcommand{\tikzcuboid@scalez}{sqrt(0.5)} \newcommand{\tikzcuboid@linefront}{black} \newcommand{\tikzcuboid@linetop}{black} \newcommand{\tikzcuboid@lineright}{black} \newcommand{\tikzcuboid@fillfront}{white} \newcommand{\tikzcuboid@filltop}{white} \newcommand{\tikzcuboid@fillright}{white} \newcommand{\tikzcuboid@shaded}{N} \newcommand{\tikzcuboid@shadecolor}{black} \newcommand{\tikzcuboid@shadeperc}{25} \newcommand{\tikzcuboid@emphedge}{N} \newcommand{\tikzcuboid@emphstyle}{thick} % Definition of Keys \define@key{tikzcuboid}{shiftx}[\tikzcuboid@shiftx]{\renewcommand{\tikzcuboid@shiftx}{#1}} \define@key{tikzcuboid}{shifty}[\tikzcuboid@shifty]{\renewcommand{\tikzcuboid@shifty}{#1}} \define@key{tikzcuboid}{dimx}[\tikzcuboid@dimx]{\renewcommand{\tikzcuboid@dimx}{#1}} \define@key{tikzcuboid}{dimy}[\tikzcuboid@dimy]{\renewcommand{\tikzcuboid@dimy}{#1}} \define@key{tikzcuboid}{dimz}[\tikzcuboid@dimz]{\renewcommand{\tikzcuboid@dimz}{#1}} \define@key{tikzcuboid}{scale}[\tikzcuboid@scale]{\renewcommand{\tikzcuboid@scale}{#1}} \define@key{tikzcuboid}{densityx}[\tikzcuboid@densityx]{\renewcommand{\tikzcuboid@densityx}{#1}} \define@key{tikzcuboid}{densityy}[\tikzcuboid@densityy]{\renewcommand{\tikzcuboid@densityy}{#1}} \define@key{tikzcuboid}{densityz}[\tikzcuboid@densityz]{\renewcommand{\tikzcuboid@densityz}{#1}} \define@key{tikzcuboid}{rotation}[\tikzcuboid@rotation]{\renewcommand{\tikzcuboid@rotation}{#1}} \define@key{tikzcuboid}{anglex}[\tikzcuboid@anglex]{\renewcommand{\tikzcuboid@anglex}{#1}} \define@key{tikzcuboid}{angley}[\tikzcuboid@angley]{\renewcommand{\tikzcuboid@angley}{#1}} \define@key{tikzcuboid}{anglez}[\tikzcuboid@anglez]{\renewcommand{\tikzcuboid@anglez}{#1}} \define@key{tikzcuboid}{scalex}[\tikzcuboid@scalex]{\renewcommand{\tikzcuboid@scalex}{#1}} \define@key{tikzcuboid}{scaley}[\tikzcuboid@scaley]{\renewcommand{\tikzcuboid@scaley}{#1}} \define@key{tikzcuboid}{scalez}[\tikzcuboid@scalez]{\renewcommand{\tikzcuboid@scalez}{#1}} \define@key{tikzcuboid}{linefront}[\tikzcuboid@linefront]{\renewcommand{\tikzcuboid@linefront}{#1}} \define@key{tikzcuboid}{linetop}[\tikzcuboid@linetop]{\renewcommand{\tikzcuboid@linetop}{#1}} \define@key{tikzcuboid}{lineright}[\tikzcuboid@lineright]{\renewcommand{\tikzcuboid@lineright}{#1}} \define@key{tikzcuboid}{fillfront}[\tikzcuboid@fillfront]{\renewcommand{\tikzcuboid@fillfront}{#1}} \define@key{tikzcuboid}{filltop}[\tikzcuboid@filltop]{\renewcommand{\tikzcuboid@filltop}{#1}} \define@key{tikzcuboid}{fillright}[\tikzcuboid@fillright]{\renewcommand{\tikzcuboid@fillright}{#1}} \define@key{tikzcuboid}{shaded}[\tikzcuboid@shaded]{\renewcommand{\tikzcuboid@shaded}{#1}} \define@key{tikzcuboid}{shadecolor}[\tikzcuboid@shadecolor]{\renewcommand{\tikzcuboid@shadecolor}{#1}} \define@key{tikzcuboid}{shadeperc}[\tikzcuboid@shadeperc]{\renewcommand{\tikzcuboid@shadeperc}{#1}} \define@key{tikzcuboid}{emphedge}[\tikzcuboid@emphedge]{\renewcommand{\tikzcuboid@emphedge}{#1}} \define@key{tikzcuboid}{emphstyle}[\tikzcuboid@emphstyle]{\renewcommand{\tikzcuboid@emphstyle}{#1}} % Commands \newcommand{\tikzcuboid}[1]{ \setkeys{tikzcuboid}{#1} % Process Keys passed to command \pgfmathsetmacro{\vectorxx}{\tikzcuboid@scalex*cos(\tikzcuboid@anglex)} \pgfmathsetmacro{\vectorxy}{\tikzcuboid@scalex*sin(\tikzcuboid@anglex)} \pgfmathsetmacro{\vectoryx}{\tikzcuboid@scaley*cos(\tikzcuboid@angley)} \pgfmathsetmacro{\vectoryy}{\tikzcuboid@scaley*sin(\tikzcuboid@angley)} \pgfmathsetmacro{\vectorzx}{\tikzcuboid@scalez*cos(\tikzcuboid@anglez)} \pgfmathsetmacro{\vectorzy}{\tikzcuboid@scalez*sin(\tikzcuboid@anglez)} \begin{scope}[xshift=\tikzcuboid@shiftx, yshift=\tikzcuboid@shifty, scale=\tikzcuboid@scale, rotate=\tikzcuboid@rotation, x={(\vectorxx,\vectorxy)}, y={(\vectoryx,\vectoryy)}, z={(\vectorzx,\vectorzy)}] \pgfmathsetmacro{\steppingx}{1/\tikzcuboid@densityx} \pgfmathsetmacro{\steppingy}{1/\tikzcuboid@densityy} \pgfmathsetmacro{\steppingz}{1/\tikzcuboid@densityz} \newcommand{\dimx}{\tikzcuboid@dimx} \newcommand{\dimy}{\tikzcuboid@dimy} \newcommand{\dimz}{\tikzcuboid@dimz} \pgfmathsetmacro{\secondx}{2*\steppingx} \pgfmathsetmacro{\secondy}{2*\steppingy} \pgfmathsetmacro{\secondz}{2*\steppingz} \foreach \x in {\steppingx,\secondx,...,\dimx} { \foreach \y in {\steppingy,\secondy,...,\dimy} { \pgfmathsetmacro{\lowx}{(\x-\steppingx)} \pgfmathsetmacro{\lowy}{(\y-\steppingy)} \filldraw[fill=\tikzcuboid@fillfront,draw=\tikzcuboid@linefront] (\lowx,\lowy,\dimz) -- (\lowx,\y,\dimz) -- (\x,\y,\dimz) -- (\x,\lowy,\dimz) -- cycle; } } \foreach \x in {\steppingx,\secondx,...,\dimx} { \foreach \z in {\steppingz,\secondz,...,\dimz} { \pgfmathsetmacro{\lowx}{(\x-\steppingx)} \pgfmathsetmacro{\lowz}{(\z-\steppingz)} \filldraw[fill=\tikzcuboid@filltop,draw=\tikzcuboid@linetop] (\lowx,\dimy,\lowz) -- (\lowx,\dimy,\z) -- (\x,\dimy,\z) -- (\x,\dimy,\lowz) -- cycle; } } \foreach \y in {\steppingy,\secondy,...,\dimy} { \foreach \z in {\steppingz,\secondz,...,\dimz} { \pgfmathsetmacro{\lowy}{(\y-\steppingy)} \pgfmathsetmacro{\lowz}{(\z-\steppingz)} \filldraw[fill=\tikzcuboid@fillright,draw=\tikzcuboid@lineright] (\dimx,\lowy,\lowz) -- (\dimx,\lowy,\z) -- (\dimx,\y,\z) -- (\dimx,\y,\lowz) -- cycle; } } \ifthenelse{\equal{\tikzcuboid@emphedge}{Y}}% {\draw[\tikzcuboid@emphstyle](0,\dimy,0) -- (\dimx,\dimy,0) -- (\dimx,\dimy,\dimz) -- (0,\dimy,\dimz) -- cycle;% \draw[\tikzcuboid@emphstyle] (0,0,\dimz) -- (0,\dimy,\dimz) -- (\dimx,\dimy,\dimz) -- (\dimx,0,\dimz) -- cycle;% \draw[\tikzcuboid@emphstyle](\dimx,0,0) -- (\dimx,\dimy,0) -- (\dimx,\dimy,\dimz) -- (\dimx,0,\dimz) -- cycle;% }% {} \end{scope} } \makeatother \begin{document} \begin{tikzpicture} \tikzcuboid{% shiftx=21cm,% shifty=8cm,% scale=1.00,% rotation=0,% densityx=2,% densityy=2,% densityz=2,% dimx=4,% dimy=3,% dimz=3,% linefront=purple!75!black,% linetop=purple!50!black,% lineright=purple!25!black,% fillfront=purple!25!white,% filltop=purple!50!white,% fillright=purple!75!white,% emphedge=Y,% emphstyle=ultra thick, } \tikzcuboid{% shiftx=21cm,% shifty=11.6cm,% scale=1.00,% rotation=0,% densityx=2,% densityy=2,% densityz=2,% dimx=4,% dimy=3,% dimz=3,% linefront=teal!75!black,% linetop=teal!50!black,% lineright=teal!25!black,% fillfront=teal!25!white,% filltop=teal!50!white,% fillright=teal!75!white,% emphedge=Y,% emphstyle=ultra thick, } \tikzcuboid{% shiftx=26.8cm,% shifty=8cm,% scale=1.00,% rotation=0,% densityx=10000,% densityy=2,% densityz=2,% dimx=0,% dimy=3,% dimz=3,% linefront=orange!75!black,% linetop=orange!50!black,% lineright=orange!25!black,% fillfront=orange!25!white,% filltop=orange!50!white,% fillright=orange!100!white,% emphedge=Y,% emphstyle=ultra thick, } \tikzcuboid{% shiftx=28.6cm,% shifty=8cm,% scale=1.00,% rotation=0,% densityx=10000,% densityy=2,% densityz=2,% dimx=0,% dimy=3,% dimz=3,% linefront=purple!75!black,% linetop=purple!50!black,% lineright=purple!25!black,% fillfront=purple!25!white,% filltop=purple!50!white,% fillright=red!75!white,% emphedge=Y,% emphstyle=ultra thick, } % \tikzcuboid{% % shiftx=27.1cm,% % shifty=10.1cm,% % scale=1.00,% % rotation=0,% % densityx=100,% % densityy=2,% % densityz=100,% % dimx=0,% % dimy=3,% % dimz=0,% % emphedge=Y,% % emphstyle=ultra thick, % } % \tikzcuboid{% % shiftx=27.1cm,% % shifty=10.1cm,% % scale=1.00,% % rotation=180,% % densityx=100,% % densityy=100,% % densityz=2,% % dimx=0,% % dimy=0,% % dimz=3,% % emphedge=Y,% % emphstyle=ultra thick, % } \tikzcuboid{% shiftx=26.8cm,% shifty=11.4cm,% scale=1.00,% rotation=0,% densityx=100,% densityy=2,% densityz=100,% dimx=0,% dimy=3,% dimz=0,% emphedge=Y,% emphstyle=ultra thick, } \tikzcuboid{% shiftx=25.3cm,% shifty=12.9cm,% scale=1.00,% rotation=180,% densityx=100,% densityy=100,% densityz=2,% dimx=0,% dimy=0,% dimz=3,% emphedge=Y,% emphstyle=ultra thick, } % \fill (27.1,10.1) circle[radius=2pt]; \node [font=\fontsize{100}{100}\fontfamily{phv}\selectfont, anchor=west, text width=9cm, color=white!50!black] at (30,10.6) {\textbf{\emph{x}}}; \node [font=\fontsize{100}{100}\fontfamily{phv}\selectfont, 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I88p88jܠxnPnnp$I88j?8 nO i\7H8C:=8o+8 C7(77HA$pmp ppVG[vnAA8 $np88jC;7ppq I8okO7C: 8pAA8 $np88jC;7ppq I8okO7C: 8pAA8 $np88jC;7ppq I8okO7C: 8pAA8 $np88jC;7ppq I8okO7C: 8x[upr?W ҈7H8C:8>v np[muph N9 N1AۏL 89Y I8o*7C: 8 7hs4p8 7H8C:8 n |."A=>sg8oHnp[muph8 8\&EpӓW{_[]?MkߵɝޯlkVe{_j7Gݸg|V[ֺ:r(ܰ!3go"Z{;z֧~V[Ow:*ppLmӓ#$C:294ݚc9[ I7T$IR8 8\&E_69a_SI4;sG'l$ $IAˤ}tŽM''GI"wM_~$Iڜ8HЊv).nd;'g@êgu$Imr%*$Iڬ$Ijݮ?8 8 R䰽ן7OzIujr7Q/I瞹I_7}|KomCܠ܀ %ɝ}}r<#I4#ϣs5z%tEXtdate:create2014-09-15T10:18:28-07:00pv%tEXtdate:modify2014-09-15T10:18:28-07:00+tEXtpdf:VersionPDF-1.5 \ 9IENDB`xarray-0.9.1/doc/_static/dataset-diagram.tex0000644131111500116100000002517312652753364021120 0ustar shoyereng00000000000000\documentclass[class=minimal,border=0pt,convert={density=300,outext=.png}]{standalone} % \documentclass[class=minimal,border=0pt]{standalone} \usepackage[scaled]{helvet} \renewcommand*\familydefault{\sfdefault} % =========================================================================== % The code below (used to define the \tikzcuboid command) is copied, % unmodified, from a tex.stackexchange.com answer by the user "Tom Bombadil": % http://tex.stackexchange.com/a/29882/8335 % % It is licensed under the Creative Commons Attribution-ShareAlike 3.0 % Unported license: http://creativecommons.org/licenses/by-sa/3.0/ % =========================================================================== \usepackage[usenames,dvipsnames]{color} \usepackage{tikz} \usepackage{keyval} \usepackage{ifthen} %==================================== %emphasize vertices --> switch and emph style (e.g. thick,black) %==================================== \makeatletter % Standard Values for Parameters \newcommand{\tikzcuboid@shiftx}{0} \newcommand{\tikzcuboid@shifty}{0} \newcommand{\tikzcuboid@dimx}{3} \newcommand{\tikzcuboid@dimy}{3} \newcommand{\tikzcuboid@dimz}{3} \newcommand{\tikzcuboid@scale}{1} \newcommand{\tikzcuboid@densityx}{1} \newcommand{\tikzcuboid@densityy}{1} \newcommand{\tikzcuboid@densityz}{1} \newcommand{\tikzcuboid@rotation}{0} \newcommand{\tikzcuboid@anglex}{0} \newcommand{\tikzcuboid@angley}{90} \newcommand{\tikzcuboid@anglez}{225} \newcommand{\tikzcuboid@scalex}{1} \newcommand{\tikzcuboid@scaley}{1} \newcommand{\tikzcuboid@scalez}{sqrt(0.5)} \newcommand{\tikzcuboid@linefront}{black} \newcommand{\tikzcuboid@linetop}{black} \newcommand{\tikzcuboid@lineright}{black} \newcommand{\tikzcuboid@fillfront}{white} \newcommand{\tikzcuboid@filltop}{white} \newcommand{\tikzcuboid@fillright}{white} \newcommand{\tikzcuboid@shaded}{N} \newcommand{\tikzcuboid@shadecolor}{black} \newcommand{\tikzcuboid@shadeperc}{25} \newcommand{\tikzcuboid@emphedge}{N} \newcommand{\tikzcuboid@emphstyle}{thick} % Definition of Keys \define@key{tikzcuboid}{shiftx}[\tikzcuboid@shiftx]{\renewcommand{\tikzcuboid@shiftx}{#1}} \define@key{tikzcuboid}{shifty}[\tikzcuboid@shifty]{\renewcommand{\tikzcuboid@shifty}{#1}} \define@key{tikzcuboid}{dimx}[\tikzcuboid@dimx]{\renewcommand{\tikzcuboid@dimx}{#1}} \define@key{tikzcuboid}{dimy}[\tikzcuboid@dimy]{\renewcommand{\tikzcuboid@dimy}{#1}} \define@key{tikzcuboid}{dimz}[\tikzcuboid@dimz]{\renewcommand{\tikzcuboid@dimz}{#1}} \define@key{tikzcuboid}{scale}[\tikzcuboid@scale]{\renewcommand{\tikzcuboid@scale}{#1}} \define@key{tikzcuboid}{densityx}[\tikzcuboid@densityx]{\renewcommand{\tikzcuboid@densityx}{#1}} \define@key{tikzcuboid}{densityy}[\tikzcuboid@densityy]{\renewcommand{\tikzcuboid@densityy}{#1}} \define@key{tikzcuboid}{densityz}[\tikzcuboid@densityz]{\renewcommand{\tikzcuboid@densityz}{#1}} \define@key{tikzcuboid}{rotation}[\tikzcuboid@rotation]{\renewcommand{\tikzcuboid@rotation}{#1}} \define@key{tikzcuboid}{anglex}[\tikzcuboid@anglex]{\renewcommand{\tikzcuboid@anglex}{#1}} \define@key{tikzcuboid}{angley}[\tikzcuboid@angley]{\renewcommand{\tikzcuboid@angley}{#1}} \define@key{tikzcuboid}{anglez}[\tikzcuboid@anglez]{\renewcommand{\tikzcuboid@anglez}{#1}} \define@key{tikzcuboid}{scalex}[\tikzcuboid@scalex]{\renewcommand{\tikzcuboid@scalex}{#1}} \define@key{tikzcuboid}{scaley}[\tikzcuboid@scaley]{\renewcommand{\tikzcuboid@scaley}{#1}} \define@key{tikzcuboid}{scalez}[\tikzcuboid@scalez]{\renewcommand{\tikzcuboid@scalez}{#1}} \define@key{tikzcuboid}{linefront}[\tikzcuboid@linefront]{\renewcommand{\tikzcuboid@linefront}{#1}} \define@key{tikzcuboid}{linetop}[\tikzcuboid@linetop]{\renewcommand{\tikzcuboid@linetop}{#1}} \define@key{tikzcuboid}{lineright}[\tikzcuboid@lineright]{\renewcommand{\tikzcuboid@lineright}{#1}} \define@key{tikzcuboid}{fillfront}[\tikzcuboid@fillfront]{\renewcommand{\tikzcuboid@fillfront}{#1}} \define@key{tikzcuboid}{filltop}[\tikzcuboid@filltop]{\renewcommand{\tikzcuboid@filltop}{#1}} \define@key{tikzcuboid}{fillright}[\tikzcuboid@fillright]{\renewcommand{\tikzcuboid@fillright}{#1}} \define@key{tikzcuboid}{shaded}[\tikzcuboid@shaded]{\renewcommand{\tikzcuboid@shaded}{#1}} \define@key{tikzcuboid}{shadecolor}[\tikzcuboid@shadecolor]{\renewcommand{\tikzcuboid@shadecolor}{#1}} \define@key{tikzcuboid}{shadeperc}[\tikzcuboid@shadeperc]{\renewcommand{\tikzcuboid@shadeperc}{#1}} \define@key{tikzcuboid}{emphedge}[\tikzcuboid@emphedge]{\renewcommand{\tikzcuboid@emphedge}{#1}} \define@key{tikzcuboid}{emphstyle}[\tikzcuboid@emphstyle]{\renewcommand{\tikzcuboid@emphstyle}{#1}} % Commands \newcommand{\tikzcuboid}[1]{ \setkeys{tikzcuboid}{#1} % Process Keys passed to command \pgfmathsetmacro{\vectorxx}{\tikzcuboid@scalex*cos(\tikzcuboid@anglex)} \pgfmathsetmacro{\vectorxy}{\tikzcuboid@scalex*sin(\tikzcuboid@anglex)} \pgfmathsetmacro{\vectoryx}{\tikzcuboid@scaley*cos(\tikzcuboid@angley)} \pgfmathsetmacro{\vectoryy}{\tikzcuboid@scaley*sin(\tikzcuboid@angley)} \pgfmathsetmacro{\vectorzx}{\tikzcuboid@scalez*cos(\tikzcuboid@anglez)} \pgfmathsetmacro{\vectorzy}{\tikzcuboid@scalez*sin(\tikzcuboid@anglez)} \begin{scope}[xshift=\tikzcuboid@shiftx, yshift=\tikzcuboid@shifty, scale=\tikzcuboid@scale, rotate=\tikzcuboid@rotation, x={(\vectorxx,\vectorxy)}, y={(\vectoryx,\vectoryy)}, z={(\vectorzx,\vectorzy)}] \pgfmathsetmacro{\steppingx}{1/\tikzcuboid@densityx} \pgfmathsetmacro{\steppingy}{1/\tikzcuboid@densityy} \pgfmathsetmacro{\steppingz}{1/\tikzcuboid@densityz} \newcommand{\dimx}{\tikzcuboid@dimx} \newcommand{\dimy}{\tikzcuboid@dimy} \newcommand{\dimz}{\tikzcuboid@dimz} \pgfmathsetmacro{\secondx}{2*\steppingx} \pgfmathsetmacro{\secondy}{2*\steppingy} \pgfmathsetmacro{\secondz}{2*\steppingz} \foreach \x in {\steppingx,\secondx,...,\dimx} { \foreach \y in {\steppingy,\secondy,...,\dimy} { \pgfmathsetmacro{\lowx}{(\x-\steppingx)} \pgfmathsetmacro{\lowy}{(\y-\steppingy)} \filldraw[fill=\tikzcuboid@fillfront,draw=\tikzcuboid@linefront] (\lowx,\lowy,\dimz) -- (\lowx,\y,\dimz) -- (\x,\y,\dimz) -- (\x,\lowy,\dimz) -- cycle; } } \foreach \x in {\steppingx,\secondx,...,\dimx} { \foreach \z in {\steppingz,\secondz,...,\dimz} { \pgfmathsetmacro{\lowx}{(\x-\steppingx)} \pgfmathsetmacro{\lowz}{(\z-\steppingz)} \filldraw[fill=\tikzcuboid@filltop,draw=\tikzcuboid@linetop] (\lowx,\dimy,\lowz) -- (\lowx,\dimy,\z) -- (\x,\dimy,\z) -- (\x,\dimy,\lowz) -- cycle; } } \foreach \y in {\steppingy,\secondy,...,\dimy} { \foreach \z in {\steppingz,\secondz,...,\dimz} { \pgfmathsetmacro{\lowy}{(\y-\steppingy)} \pgfmathsetmacro{\lowz}{(\z-\steppingz)} \filldraw[fill=\tikzcuboid@fillright,draw=\tikzcuboid@lineright] (\dimx,\lowy,\lowz) -- (\dimx,\lowy,\z) -- (\dimx,\y,\z) -- (\dimx,\y,\lowz) -- cycle; } } \ifthenelse{\equal{\tikzcuboid@emphedge}{Y}}% {\draw[\tikzcuboid@emphstyle](0,\dimy,0) -- (\dimx,\dimy,0) -- (\dimx,\dimy,\dimz) -- (0,\dimy,\dimz) -- cycle;% \draw[\tikzcuboid@emphstyle] (0,0,\dimz) -- (0,\dimy,\dimz) -- (\dimx,\dimy,\dimz) -- (\dimx,0,\dimz) -- cycle;% \draw[\tikzcuboid@emphstyle](\dimx,0,0) -- (\dimx,\dimy,0) -- (\dimx,\dimy,\dimz) -- (\dimx,0,\dimz) -- cycle;% }% {} \end{scope} } \makeatother \begin{document} \begin{tikzpicture} \tikzcuboid{% shiftx=16cm,% shifty=8cm,% scale=1.00,% rotation=0,% densityx=2,% densityy=2,% densityz=2,% dimx=4,% dimy=3,% dimz=3,% linefront=teal!75!black,% linetop=teal!50!black,% lineright=teal!25!black,% fillfront=teal!25!white,% filltop=teal!50!white,% fillright=teal!75!white,% emphedge=Y,% emphstyle=very thick, } \tikzcuboid{% shiftx=21cm,% shifty=8cm,% scale=1.00,% rotation=0,% densityx=2,% densityy=2,% densityz=2,% dimx=4,% dimy=3,% dimz=3,% linefront=purple!75!black,% linetop=purple!50!black,% lineright=purple!25!black,% fillfront=purple!25!white,% filltop=purple!50!white,% fillright=purple!75!white,% emphedge=Y,% emphstyle=very thick, } \tikzcuboid{% shiftx=26.2cm,% shifty=8cm,% scale=1.00,% rotation=0,% densityx=10000,% densityy=2,% densityz=2,% dimx=0,% dimy=3,% dimz=3,% linefront=orange!75!black,% linetop=orange!50!black,% lineright=orange!25!black,% fillfront=orange!25!white,% filltop=orange!50!white,% fillright=orange!100!white,% emphedge=Y,% emphstyle=very thick, } \tikzcuboid{% shiftx=27.6cm,% shifty=8cm,% scale=1.00,% rotation=0,% densityx=10000,% densityy=2,% densityz=2,% dimx=0,% dimy=3,% dimz=3,% linefront=purple!75!black,% linetop=purple!50!black,% lineright=purple!25!black,% fillfront=purple!25!white,% filltop=purple!50!white,% fillright=red!75!white,% emphedge=Y,% emphstyle=very thick, } \tikzcuboid{% shiftx=28cm,% shifty=6.5cm,% scale=1.00,% rotation=0,% densityx=2,% densityx=2,% densityy=100,% densityz=100,% dimx=4,% dimy=0,% dimz=0,% emphedge=Y,% emphstyle=very thick, } \tikzcuboid{% shiftx=28cm,% shifty=6.5cm,% scale=1.00,% rotation=0,% densityx=100,% densityy=2,% densityz=100,% dimx=0,% dimy=3,% dimz=0,% emphedge=Y,% emphstyle=very thick, } \tikzcuboid{% shiftx=28cm,% shifty=6.5cm,% scale=1.00,% rotation=180,% densityx=100,% densityy=100,% densityz=2,% dimx=0,% dimy=0,% dimz=3,% emphedge=Y,% emphstyle=very thick, } \node [font=\fontsize{11}{11}\selectfont] at (18,11.5) {temperature}; \node [font=\fontsize{11}{11}\selectfont] at (23,11.5) {precipitation}; \node [font=\fontsize{11}{11}\selectfont] at (25.8,11.5) {latitude}; \node [font=\fontsize{11}{11}\selectfont] at (27.5,11.47) {longitude}; \node [font=\fontsize{11}{11}\selectfont] at (28,10) {x}; \node [font=\fontsize{11}{11}\selectfont] at (29.5,8.5) {y}; \node [font=\fontsize{11}{11}\selectfont] at (32,7) {t}; \node [font=\fontsize{11}{11}\selectfont] at (31,10) {reference\_time}; \fill (31,9.5) circle[radius=2pt]; \end{tikzpicture} \end{document} xarray-0.9.1/doc/_static/opendap-prism-tmax.png0000644131111500116100000031502312652753364021576 0ustar shoyereng00000000000000PNG  IHDR &^sBIT|d pHYsaa?i IDATx{eUust,4z5b\()MYIR*^oFS&R>*1 Oc4B 6l^k9c:@:{s>w~((((((?> (((ހ(((a (((ހ(((a (((ހ(((a (((ހ(((a (((ހ(((a (((ހ(((a (((ހ(((a (((ހ(((a (((ހ(((a (((ހ(((a (((ހ(((a (((ހ(((a (((ހ(((a (((ހ(((a (((ހ(((a (((ހ(((a 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K.Iq3sߎm۶W^믿믿!TUE ?2ēO>:TiO?wqoߎO?3fa?7|;>%J?{w( Ls=׹ 2z ֭_`޼yw@ ?Ѽys\}xN>mSBQ\q>}:&M.__SMO<0tP? ˜1c#gs1cEJ)S"""""j!"""""pBDDDDD<y"""""  DDDDD@3g8!"""""pBD;v@QQƏ_c͛qia)hQjqBD@Q array([[ 0.4691123 , -0.28286334, -1.5090585 ], [-1.13563237, 1.21211203, -0.17321465]]) second value: array([[ 1.4691123 , 0.71713666, -0.5090585 ], [-0.13563237, 2.21211203, 0.82678535]]) The same non-destructive merging between ``DataArray`` index coordinates is used in the :py:class:`~xarray.Dataset` constructor: .. ipython:: python xr.Dataset({'a': arr[:-1], 'b': arr[1:]}) .. _combine: Combine ~~~~~~~ The instance method :py:meth:`~xarray.DataArray.combine_first` combines two datasets/data arrays and defaults to non-null values in the calling object, using values from the called object to fill holes. The resulting coordinates are the union of coordinate labels. Vacant cells as a result of the outer-join are filled with ``NaN``. For example: .. ipython:: python ar0 = xr.DataArray([[0, 0], [0, 0]], [('x', ['a', 'b']), ('y', [-1, 0])]) ar1 = xr.DataArray([[1, 1], [1, 1]], [('x', ['b', 'c']), ('y', [0, 1])]) ar0.combine_first(ar1) ar1.combine_first(ar0) For datasets, ``ds0.combine_first(ds1)`` works similarly to ``xr.merge([ds0, ds1])``, except that ``xr.merge`` raises ``MergeError`` when there are conflicting values in variables to be merged, whereas ``.combine_first`` defaults to the calling object's values. .. _update: Update ~~~~~~ In contrast to ``merge``, :py:meth:`~xarray.Dataset.update` modifies a dataset in-place without checking for conflicts, and will overwrite any existing variables with new values: .. ipython:: python ds.update({'space': ('space', [10.2, 9.4, 3.9])}) However, dimensions are still required to be consistent between different Dataset variables, so you cannot change the size of a dimension unless you replace all dataset variables that use it. ``update`` also performs automatic alignment if necessary. Unlike ``merge``, it maintains the alignment of the original array instead of merging indexes: .. ipython:: python ds.update(other) The exact same alignment logic when setting a variable with ``__setitem__`` syntax: .. ipython:: python ds['baz'] = xr.DataArray([9, 9, 9, 9, 9], coords=[('x', list('abcde'))]) ds.baz Equals and identical ~~~~~~~~~~~~~~~~~~~~ xarray objects can be compared by using the :py:meth:`~xarray.Dataset.equals`, :py:meth:`~xarray.Dataset.identical` and :py:meth:`~xarray.Dataset.broadcast_equals` methods. These methods are used by the optional ``compat`` argument on ``concat`` and ``merge``. :py:attr:`~xarray.Dataset.equals` checks dimension names, indexes and array values: .. ipython:: python arr.equals(arr.copy()) :py:attr:`~xarray.Dataset.identical` also checks attributes, and the name of each object: .. ipython:: python arr.identical(arr.rename('bar')) :py:attr:`~xarray.Dataset.broadcast_equals` does a more relaxed form of equality check that allows variables to have different dimensions, as long as values are constant along those new dimensions: .. ipython:: python left = xr.Dataset(coords={'x': 0}) right = xr.Dataset({'x': [0, 0, 0]}) left.broadcast_equals(right) Like pandas objects, two xarray objects are still equal or identical if they have missing values marked by ``NaN`` in the same locations. In contrast, the ``==`` operation performs element-wise comparison (like numpy): .. ipython:: python arr == arr.copy() Note that ``NaN`` does not compare equal to ``NaN`` in element-wise comparison; you may need to deal with missing values explicitly. .. _combining.no_conflicts: Merging with 'no_conflicts' ~~~~~~~~~~~~~~~~~~~~~~~~~~~ The ``compat`` argument ``'no_conflicts'`` is only available when combining xarray objects with ``merge``. In addition to the above comparison methods it allows the merging of xarray objects with locations where *either* have ``NaN`` values. This can be used to combine data with overlapping coordinates as long as any non-missing values agree or are disjoint: .. ipython:: python ds1 = xr.Dataset({'a': ('x', [10, 20, 30, np.nan])}, {'x': [1, 2, 3, 4]}) ds2 = xr.Dataset({'a': ('x', [np.nan, 30, 40, 50])}, {'x': [2, 3, 4, 5]}) xr.merge([ds1, ds2], compat='no_conflicts') Note that due to the underlying representation of missing values as floating point numbers (``NaN``), variable data type is not always preserved when merging in this manner. xarray-0.9.1/doc/computation.rst0000644131111500116100000002125713043511243016773 0ustar shoyereng00000000000000.. _comput: ########### Computation ########### The labels associated with :py:class:`~xarray.DataArray` and :py:class:`~xarray.Dataset` objects enables some powerful shortcuts for computation, notably including aggregation and broadcasting by dimension names. Basic array math ================ Arithmetic operations with a single DataArray automatically vectorize (like numpy) over all array values: .. ipython:: python :suppress: import numpy as np import pandas as pd import xarray as xr np.random.seed(123456) .. ipython:: python arr = xr.DataArray(np.random.randn(2, 3), [('x', ['a', 'b']), ('y', [10, 20, 30])]) arr - 3 abs(arr) You can also use any of numpy's or scipy's many `ufunc`__ functions directly on a DataArray: __ http://docs.scipy.org/doc/numpy/reference/ufuncs.html .. ipython:: python np.sin(arr) Data arrays also implement many :py:class:`numpy.ndarray` methods: .. ipython:: python arr.round(2) arr.T .. _missing_values: Missing values ============== xarray objects borrow the :py:meth:`~xarray.DataArray.isnull`, :py:meth:`~xarray.DataArray.notnull`, :py:meth:`~xarray.DataArray.count`, :py:meth:`~xarray.DataArray.dropna` and :py:meth:`~xarray.DataArray.fillna` methods for working with missing data from pandas: .. ipython:: python x = xr.DataArray([0, 1, np.nan, np.nan, 2], dims=['x']) x.isnull() x.notnull() x.count() x.dropna(dim='x') x.fillna(-1) Like pandas, xarray uses the float value ``np.nan`` (not-a-number) to represent missing values. Aggregation =========== Aggregation methods have been updated to take a `dim` argument instead of `axis`. This allows for very intuitive syntax for aggregation methods that are applied along particular dimension(s): .. ipython:: python arr.sum(dim='x') arr.std(['x', 'y']) arr.min() If you need to figure out the axis number for a dimension yourself (say, for wrapping code designed to work with numpy arrays), you can use the :py:meth:`~xarray.DataArray.get_axis_num` method: .. ipython:: python arr.get_axis_num('y') These operations automatically skip missing values, like in pandas: .. ipython:: python xr.DataArray([1, 2, np.nan, 3]).mean() If desired, you can disable this behavior by invoking the aggregation method with ``skipna=False``. .. _comput.rolling: Rolling window operations ========================= ``DataArray`` objects include a :py:meth:`~xarray.DataArray.rolling` method. This method supports rolling window aggregation: .. ipython:: python arr = xr.DataArray(np.arange(0, 7.5, 0.5).reshape(3, 5), dims=('x', 'y')) arr :py:meth:`~xarray.DataArray.rolling` is applied along one dimension using the name of the dimension as a key (e.g. ``y``) and the window size as the value (e.g. ``3``). We get back a ``Rolling`` object: .. ipython:: python arr.rolling(y=3) The label position and minimum number of periods in the rolling window are controlled by the ``center`` and ``min_periods`` arguments: .. ipython:: python arr.rolling(y=3, min_periods=2, center=True) Aggregation and summary methods can be applied directly to the ``Rolling`` object: .. ipython:: python r = arr.rolling(y=3) r.mean() r.reduce(np.std) Note that rolling window aggregations are much faster (both asymptotically and because they avoid a loop in Python) when bottleneck_ is installed. Otherwise, we fall back to a slower, pure Python implementation. .. _bottleneck: https://github.com/kwgoodman/bottleneck/ Finally, we can manually iterate through ``Rolling`` objects: .. ipython:: python @verbatim for label, arr_window in r: # arr_window is a view of x .. _compute.broadcasting: Broadcasting by dimension name ============================== ``DataArray`` objects are automatically align themselves ("broadcasting" in the numpy parlance) by dimension name instead of axis order. With xarray, you do not need to transpose arrays or insert dimensions of length 1 to get array operations to work, as commonly done in numpy with :py:func:`np.reshape` or :py:const:`np.newaxis`. This is best illustrated by a few examples. Consider two one-dimensional arrays with different sizes aligned along different dimensions: .. ipython:: python a = xr.DataArray([1, 2], [('x', ['a', 'b'])]) a b = xr.DataArray([-1, -2, -3], [('y', [10, 20, 30])]) b With xarray, we can apply binary mathematical operations to these arrays, and their dimensions are expanded automatically: .. ipython:: python a * b Moreover, dimensions are always reordered to the order in which they first appeared: .. ipython:: python c = xr.DataArray(np.arange(6).reshape(3, 2), [b['y'], a['x']]) c a + c This means, for example, that you always subtract an array from its transpose: .. ipython:: python c - c.T You can explicitly broadcast xaray data structures by using the :py:func:`~xarray.broadcast` function: .. ipython:: python a2, b2 = xr.broadcast(a, b) a2 b2 .. _math automatic alignment: Automatic alignment =================== xarray enforces alignment between *index* :ref:`coordinates` (that is, coordinates with the same name as a dimension, marked by ``*``) on objects used in binary operations. Similarly to pandas, this alignment is automatic for arithmetic on binary operations. The default result of a binary operation is by the *intersection* (not the union) of coordinate labels: .. ipython:: python arr = xr.DataArray(np.arange(3), [('x', range(3))]) arr + arr[:-1] If coordinate values for a dimension are missing on either argument, all matching dimensions must have the same size: .. ipython:: python @verbatim In [1]: arr + xr.DataArray([1, 2], dims='x') ValueError: arguments without labels along dimension 'x' cannot be aligned because they have different dimension size(s) {2} than the size of the aligned dimension labels: 3 However, one can explicitly change this default automatic alignment type ("inner") via :py:func:`~xarray.set_options()` in context manager: .. ipython:: python with xr.set_options(arithmetic_join="outer"): arr + arr[:1] arr + arr[:1] Before loops or performance critical code, it's a good idea to align arrays explicitly (e.g., by putting them in the same Dataset or using :py:func:`~xarray.align`) to avoid the overhead of repeated alignment with each operation. See :ref:`align and reindex` for more details. .. note:: There is no automatic alignment between arguments when performing in-place arithmetic operations such as ``+=``. You will need to use :ref:`manual alignment`. This ensures in-place arithmetic never needs to modify data types. .. _coordinates math: Coordinates =========== Although index coordinates are aligned, other coordinates are not, and if their values conflict, they will be dropped. This is necessary, for example, because indexing turns 1D coordinates into scalar coordinates: .. ipython:: python arr[0] arr[1] # notice that the scalar coordinate 'x' is silently dropped arr[1] - arr[0] Still, xarray will persist other coordinates in arithmetic, as long as there are no conflicting values: .. ipython:: python # only one argument has the 'x' coordinate arr[0] + 1 # both arguments have the same 'x' coordinate arr[0] - arr[0] Math with datasets ================== Datasets support arithmetic operations by automatically looping over all data variables: .. ipython:: python ds = xr.Dataset({'x_and_y': (('x', 'y'), np.random.randn(3, 5)), 'x_only': ('x', np.random.randn(3))}, coords=arr.coords) ds > 0 Datasets support most of the same methods found on data arrays: .. ipython:: python ds.mean(dim='x') abs(ds) Unfortunately, a limitation of the current version of numpy means that we cannot override ufuncs for datasets, because datasets cannot be written as a single array [1]_. :py:meth:`~xarray.Dataset.apply` works around this limitation, by applying the given function to each variable in the dataset: .. ipython:: python ds.apply(np.sin) Datasets also use looping over variables for *broadcasting* in binary arithmetic. You can do arithmetic between any ``DataArray`` and a dataset: .. ipython:: python ds + arr Arithmetic between two datasets matches data variables of the same name: .. ipython:: python ds2 = xr.Dataset({'x_and_y': 0, 'x_only': 100}) ds - ds2 Similarly to index based alignment, the result has the intersection of all matching variables, and ``ValueError`` is raised if the result would be empty. .. [1] In some future version of NumPy, we should be able to override ufuncs for datasets by making use of ``__numpy_ufunc__``. xarray-0.9.1/doc/conf.py0000644131111500116100000002576713043511243015210 0ustar shoyereng00000000000000# -*- coding: utf-8 -*- # # xarray documentation build configuration file, created by # sphinx-quickstart on Thu Feb 6 18:57:54 2014. # # This file is execfile()d with the current directory set to its # containing dir. # # Note that not all possible configuration values are present in this # autogenerated file. # # All configuration values have a default; values that are commented out # serve to show the default. from __future__ import print_function from __future__ import division from __future__ import absolute_import import sys import warnings import os print("python exec:", sys.executable) print("sys.path:", sys.path) try: import numpy print("numpy: %s, %s" % (numpy.__version__, numpy.__file__)) except ImportError: print("no numpy") try: import scipy print("scipy: %s, %s" % (scipy.__version__, scipy.__file__)) except ImportError: print("no scipy") try: import pandas print("pandas: %s, %s" % (pandas.__version__, pandas.__file__)) except ImportError: print("no pandas") try: import matplotlib matplotlib.use('Agg') print("matplotlib: %s, %s" % (matplotlib.__version__, matplotlib.__file__)) except ImportError: print("no matplotlib") try: import dask print("dask: %s, %s" % (dask.__version__, dask.__file__)) except ImportError: print("no dask") try: import IPython print("ipython: %s, %s" % (IPython.__version__, IPython.__file__)) except ImportError: print("no ipython") try: with warnings.catch_warnings(): # https://github.com/mwaskom/seaborn/issues/892 warnings.simplefilter("ignore") import seaborn print("seaborn: %s, %s" % (seaborn.__version__, seaborn.__file__)) except ImportError: print("no seaborn") try: import cartopy print("cartopy: %s, %s" % (cartopy.__version__, cartopy.__file__)) except ImportError: print("no cartopy") try: import netCDF4 print("netCDF4: %s, %s" % (netCDF4.__version__, netCDF4.__file__)) except ImportError: print("no netCDF4") import xarray print("xarray: %s, %s" % (xarray.__version__, xarray.__file__)) # -- General configuration ------------------------------------------------ # If your documentation needs a minimal Sphinx version, state it here. #needs_sphinx = '1.0' # Add any Sphinx extension module names here, as strings. They can be # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom # ones. extensions = [ 'sphinx.ext.autodoc', 'sphinx.ext.autosummary', 'sphinx.ext.intersphinx', 'sphinx.ext.extlinks', 'sphinx.ext.mathjax', 'numpydoc', 'IPython.sphinxext.ipython_directive', 'IPython.sphinxext.ipython_console_highlighting', ] extlinks = {'issue': ('https://github.com/pydata/xarray/issues/%s', 'GH'), 'pull': ('https://github.com/pydata/xarray/pull/%s', 'PR'), } autosummary_generate = True numpydoc_class_members_toctree = True numpydoc_show_class_members = False # Add any paths that contain templates here, relative to this directory. templates_path = ['_templates'] # The suffix of source filenames. source_suffix = '.rst' # The encoding of source files. #source_encoding = 'utf-8-sig' # The master toctree document. master_doc = 'index' # General information about the project. project = 'xarray' copyright = '2014-2016, xarray Developers' # The version info for the project you're documenting, acts as replacement for # |version| and |release|, also used in various other places throughout the # built documents. # # The short X.Y version. version = xarray.version.short_version # The full version, including alpha/beta/rc tags. release = xarray.__version__ # The language for content autogenerated by Sphinx. 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See the documentation for # a list of builtin themes. # on_rtd is whether we are on readthedocs.org, this line of code grabbed from # docs.readthedocs.org on_rtd = os.environ.get('READTHEDOCS', None) == 'True' if not on_rtd: # only import and set the theme if we're building docs locally import sphinx_rtd_theme html_theme = 'sphinx_rtd_theme' html_theme_path = [sphinx_rtd_theme.get_html_theme_path()] # otherwise, readthedocs.org uses their theme by default, so no need to specify it # Theme options are theme-specific and customize the look and feel of a theme # further. For a list of options available for each theme, see the # documentation. #html_theme_options = {} # Add any paths that contain custom themes here, relative to this directory. #html_theme_path = [] # The name for this set of Sphinx documents. If None, it defaults to # " v documentation". #html_title = None # A shorter title for the navigation bar. 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Default is True. #html_show_sphinx = True # If true, "(C) Copyright ..." is shown in the HTML footer. Default is True. #html_show_copyright = True # If true, an OpenSearch description file will be output, and all pages will # contain a tag referring to it. The value of this option must be the # base URL from which the finished HTML is served. #html_use_opensearch = '' # This is the file name suffix for HTML files (e.g. ".xhtml"). #html_file_suffix = None # Output file base name for HTML help builder. htmlhelp_basename = 'xarraydoc' # -- Options for LaTeX output --------------------------------------------- latex_elements = { # The paper size ('letterpaper' or 'a4paper'). #'papersize': 'letterpaper', # The font size ('10pt', '11pt' or '12pt'). #'pointsize': '10pt', # Additional stuff for the LaTeX preamble. #'preamble': '', } # Grouping the document tree into LaTeX files. List of tuples # (source start file, target name, title, # author, documentclass [howto, manual, or own class]). latex_documents = [ ('index', 'xarray.tex', 'xarray Documentation', 'xarray Developers', 'manual'), ] # The name of an image file (relative to this directory) to place at the top of # the title page. #latex_logo = None # For "manual" documents, if this is true, then toplevel headings are parts, # not chapters. #latex_use_parts = False # If true, show page references after internal links. #latex_show_pagerefs = False # If true, show URL addresses after external links. #latex_show_urls = False # Documents to append as an appendix to all manuals. #latex_appendices = [] # If false, no module index is generated. #latex_domain_indices = True # -- Options for manual page output --------------------------------------- # One entry per manual page. List of tuples # (source start file, name, description, authors, manual section). man_pages = [ ('index', 'xarray', 'xarray Documentation', ['xarray Developers'], 1) ] # If true, show URL addresses after external links. #man_show_urls = False # -- Options for Texinfo output ------------------------------------------- # Grouping the document tree into Texinfo files. List of tuples # (source start file, target name, title, author, # dir menu entry, description, category) texinfo_documents = [ ('index', 'xarray', 'xarray Documentation', 'xarray Developers', 'xarray', 'N-D labeled arrays and datasets in Python.', 'Miscellaneous'), ] # Documents to append as an appendix to all manuals. #texinfo_appendices = [] # If false, no module index is generated. #texinfo_domain_indices = True # How to display URL addresses: 'footnote', 'no', or 'inline'. #texinfo_show_urls = 'footnote' # If true, do not generate a @detailmenu in the "Top" node's menu. #texinfo_no_detailmenu = False # Example configuration for intersphinx: refer to the Python standard library. intersphinx_mapping = { 'python': ('https://docs.python.org/3.5/', None), 'pandas': ('http://pandas.pydata.org/pandas-docs/stable/', None), 'iris': ('http://scitools.org.uk/iris/docs/latest/', None), 'numpy': ('http://docs.scipy.org/doc/numpy/', None), } xarray-0.9.1/doc/dask.rst0000644131111500116100000002333713043511243015354 0ustar shoyereng00000000000000.. _dask: Out of core computation with dask ================================= xarray integrates with `dask `__ to support streaming computation on datasets that don't fit into memory. Currently, dask is an entirely optional feature for xarray. However, the benefits of using dask are sufficiently strong that dask may become a required dependency in a future version of xarray. For a full example of how to use xarray's dask integration, read the `blog post introducing xarray and dask`_. .. _blog post introducing xarray and dask: http://continuum.io/blog/xray-dask What is a dask array? --------------------- .. image:: _static/dask_array.png :width: 40 % :align: right :alt: A dask array Dask divides arrays into many small pieces, called *chunks*, each of which is presumed to be small enough to fit into memory. Unlike NumPy, which has eager evaluation, operations on dask arrays are lazy. Operations queue up a series of tasks mapped over blocks, and no computation is performed until you actually ask values to be computed (e.g., to print results to your screen or write to disk). At that point, data is loaded into memory and computation proceeds in a streaming fashion, block-by-block. The actual computation is controlled by a multi-processing or thread pool, which allows dask to take full advantage of multiple processers available on most modern computers. For more details on dask, read `its documentation `__. .. _dask.io: Reading and writing data ------------------------ The usual way to create a dataset filled with dask arrays is to load the data from a netCDF file or files. You can do this by supplying a ``chunks`` argument to :py:func:`~xarray.open_dataset` or using the :py:func:`~xarray.open_mfdataset` function. .. ipython:: python :suppress: import numpy as np import pandas as pd import xarray as xr np.random.seed(123456) np.set_printoptions(precision=3, linewidth=100, threshold=100, edgeitems=3) ds = xr.Dataset({'temperature': (('time', 'latitude', 'longitude'), np.random.randn(365, 180, 360)), 'time': pd.date_range('2015-01-01', periods=365), 'longitude': np.arange(360), 'latitude': np.arange(89.5, -90.5, -1)}) ds.to_netcdf('example-data.nc') .. ipython:: python ds = xr.open_dataset('example-data.nc', chunks={'time': 10}) ds In this example ``latitude`` and ``longitude`` do not appear in the ``chunks`` dict, so only one chunk will be used along those dimensions. It is also entirely equivalent to open a dataset using ``open_dataset`` and then chunk the data use the ``chunk`` method, e.g., ``xr.open_dataset('example-data.nc').chunk({'time': 10})``. To open multiple files simultaneously, use :py:func:`~xarray.open_mfdataset`:: xr.open_mfdataset('my/files/*.nc') This function will automatically concatenate and merge dataset into one in the simple cases that it understands (see :py:func:`~xarray.auto_combine` for the full disclaimer). By default, ``open_mfdataset`` will chunk each netCDF file into a single dask array; again, supply the ``chunks`` argument to control the size of the resulting dask arrays. In more complex cases, you can open each file individually using ``open_dataset`` and merge the result, as described in :ref:`combining data`. You'll notice that printing a dataset still shows a preview of array values, even if they are actually dask arrays. We can do this quickly with dask because we only need to the compute the first few values (typically from the first block). To reveal the true nature of an array, print a DataArray: .. ipython:: python ds.temperature Once you've manipulated a dask array, you can still write a dataset too big to fit into memory back to disk by using :py:meth:`~xarray.Dataset.to_netcdf` in the usual way. Using dask with xarray ---------------------- Nearly all existing xarray methods (including those for indexing, computation, concatenating and grouped operations) have been extended to work automatically with dask arrays. When you load data as a dask array in an xarray data structure, almost all xarray operations will keep it as a dask array; when this is not possible, they will raise an exception rather than unexpectedly loading data into memory. Converting a dask array into memory generally requires an explicit conversion step. One noteable exception is indexing operations: to enable label based indexing, xarray will automatically load coordinate labels into memory. The easiest way to convert an xarray data structure from lazy dask arrays into eager, in-memory numpy arrays is to use the :py:meth:`~xarray.Dataset.load` method: .. ipython:: python ds.load() You can also access :py:attr:`~xarray.DataArray.values`, which will always be a numpy array: .. ipython:: :verbatim: In [5]: ds.temperature.values Out[5]: array([[[ 4.691e-01, -2.829e-01, ..., -5.577e-01, 3.814e-01], [ 1.337e+00, -1.531e+00, ..., 8.726e-01, -1.538e+00], ... # truncated for brevity Explicit conversion by wrapping a DataArray with ``np.asarray`` also works: .. ipython:: :verbatim: In [5]: np.asarray(ds.temperature) Out[5]: array([[[ 4.691e-01, -2.829e-01, ..., -5.577e-01, 3.814e-01], [ 1.337e+00, -1.531e+00, ..., 8.726e-01, -1.538e+00], ... With the current version of dask, there is no automatic alignment of chunks when performing operations between dask arrays with different chunk sizes. If your computation involves multiple dask arrays with different chunks, you may need to explicitly rechunk each array to ensure compatibility. With xarray, both converting data to a dask arrays and converting the chunk sizes of dask arrays is done with the :py:meth:`~xarray.Dataset.chunk` method: .. ipython:: python :suppress: ds = ds.chunk({'time': 10}) .. ipython:: python rechunked = ds.chunk({'latitude': 100, 'longitude': 100}) You can view the size of existing chunks on an array by viewing the :py:attr:`~xarray.Dataset.chunks` attribute: .. ipython:: python rechunked.chunks If there are not consistent chunksizes between all the arrays in a dataset along a particular dimension, an exception is raised when you try to access ``.chunks``. .. note:: In the future, we would like to enable automatic alignment of dask chunksizes (but not the other way around). We might also require that all arrays in a dataset share the same chunking alignment. Neither of these are currently done. NumPy ufuncs like ``np.sin`` currently only work on eagerly evaluated arrays (this will change with the next major NumPy release). We have provided replacements that also work on all xarray objects, including those that store lazy dask arrays, in the :ref:`xarray.ufuncs ` module: .. ipython:: python import xarray.ufuncs as xu xu.sin(rechunked) To access dask arrays directly, use the new :py:attr:`DataArray.data ` attribute. This attribute exposes array data either as a dask array or as a numpy array, depending on whether it has been loaded into dask or not: .. ipython:: python ds.temperature.data .. note:: In the future, we may extend ``.data`` to support other "computable" array backends beyond dask and numpy (e.g., to support sparse arrays). Chunking and performance ------------------------ The ``chunks`` parameter has critical performance implications when using dask arrays. If your chunks are too small, queueing up operations will be extremely slow, because dask will translates each operation into a huge number of operations mapped across chunks. Computation on dask arrays with small chunks can also be slow, because each operation on a chunk has some fixed overhead from the Python interpreter and the dask task executor. Conversely, if your chunks are too big, some of your computation may be wasted, because dask only computes results one chunk at a time. A good rule of thumb to create arrays with a minimum chunksize of at least one million elements (e.g., a 1000x1000 matrix). With large arrays (10+ GB), the cost of queueing up dask operations can be noticeable, and you may need even larger chunksizes. .. ipython:: python :suppress: import os os.remove('example-data.nc') Optimization Tips ----------------- With analysis pipelines involving both spatial subsetting and temporal resampling, dask performance can become very slow in certain cases. Here are some optimization tips we have found through experience: 1. Do your spatial and temporal indexing (e.g. ``.sel()`` or ``.isel()``) early in the pipeline, especially before calling ``resample()`` or ``groupby()``. Grouping and rasampling triggers some computation on all the blocks, which in theory should commute with indexing, but this optimization hasn't been implemented in dask yet. (See `dask issue #746 `_). 2. Save intermediate results to disk as a netCDF files (using ``to_netcdf()``) and then load them again with ``open_dataset()`` for further computations. For example, if subtracting temporal mean from a dataset, save the temporal mean to disk before subtracting. Again, in theory, dask should be able to do the computation in a streaming fashion, but in practice this is a fail case for the dask scheduler, because it tries to keep every chunk of an array that it computes in memory. (See `dask issue #874 `_) 3. Specify smaller chunks across space when using ``open_mfdataset()`` (e.g., ``chunks={'latitude': 10, 'longitude': 10}``). This makes spatial subsetting easier, because there's no risk you will load chunks of data referring to different chunks (probably not necessary if you follow suggestion 1). xarray-0.9.1/doc/data-structures.rst0000644131111500116100000004760513043675344017605 0ustar shoyereng00000000000000.. _data structures: Data Structures =============== .. ipython:: python :suppress: import numpy as np import pandas as pd import xarray as xr np.random.seed(123456) np.set_printoptions(threshold=10) DataArray --------- :py:class:`xarray.DataArray` is xarray's implementation of a labeled, multi-dimensional array. It has several key properties: - ``values``: a :py:class:`numpy.ndarray` holding the array's values - ``dims``: dimension names for each axis (e.g., ``('x', 'y', 'z')``) - ``coords``: a dict-like container of arrays (*coordinates*) that label each point (e.g., 1-dimensional arrays of numbers, datetime objects or strings) - ``attrs``: an ``OrderedDict`` to hold arbitrary metadata (*attributes*) xarray uses ``dims`` and ``coords`` to enable its core metadata aware operations. Dimensions provide names that xarray uses instead of the ``axis`` argument found in many numpy functions. Coordinates enable fast label based indexing and alignment, building on the functionality of the ``index`` found on a pandas :py:class:`~pandas.DataFrame` or :py:class:`~pandas.Series`. DataArray objects also can have a ``name`` and can hold arbitrary metadata in the form of their ``attrs`` property (an ordered dictionary). Names and attributes are strictly for users and user-written code: xarray makes no attempt to interpret them, and propagates them only in unambiguous cases (see FAQ, :ref:`approach to metadata`). .. _creating a dataarray: Creating a DataArray ~~~~~~~~~~~~~~~~~~~~ The :py:class:`~xarray.DataArray` constructor takes: - ``data``: a multi-dimensional array of values (e.g., a numpy ndarray, :py:class:`~pandas.Series`, :py:class:`~pandas.DataFrame` or :py:class:`~pandas.Panel`) - ``coords``: a list or dictionary of coordinates - ``dims``: a list of dimension names. If omitted, dimension names are taken from ``coords`` if possible. - ``attrs``: a dictionary of attributes to add to the instance - ``name``: a string that names the instance .. ipython:: python data = np.random.rand(4, 3) locs = ['IA', 'IL', 'IN'] times = pd.date_range('2000-01-01', periods=4) foo = xr.DataArray(data, coords=[times, locs], dims=['time', 'space']) foo Only ``data`` is required; all of other arguments will be filled in with default values: .. ipython:: python xr.DataArray(data) As you can see, dimension names are always present in the xarray data model: if you do not provide them, defaults of the form ``dim_N`` will be created. However, coordinates are always optional, and dimensions do not have automatic coordinate labels. .. note:: This is different from pandas, where axes always have tick labels, which default to the integers ``[0, ..., n-1]``. Prior to xarray v0.9, xarray copied this behavior: default coordinates for each dimension would be created if coordinates were not supplied explicitly. This is no longer the case. Coordinates can be specified in the following ways: - A list of values with length equal to the number of dimensions, providing coordinate labels for each dimension. Each value must be of one of the following forms: * A :py:class:`~xarray.DataArray` or :py:class:`~xarray.Variable` * A tuple of the form ``(dims, data[, attrs])``, which is converted into arguments for :py:class:`~xarray.Variable` * A pandas object or scalar value, which is converted into a ``DataArray`` * A 1D array or list, which is interpreted as values for a one dimensional coordinate variable along the same dimension as it's name - A dictionary of ``{coord_name: coord}`` where values are of the same form as the list. Supplying coordinates as a dictionary allows other coordinates than those corresponding to dimensions (more on these later). If you supply ``coords`` as a dictionary, you must explicitly provide ``dims``. As a list of tuples: .. ipython:: python xr.DataArray(data, coords=[('time', times), ('space', locs)]) As a dictionary: .. ipython:: python xr.DataArray(data, coords={'time': times, 'space': locs, 'const': 42, 'ranking': ('space', [1, 2, 3])}, dims=['time', 'space']) As a dictionary with coords across multiple dimensions: .. ipython:: python xr.DataArray(data, coords={'time': times, 'space': locs, 'const': 42, 'ranking': (('time', 'space'), np.arange(12).reshape(4,3))}, dims=['time', 'space']) If you create a ``DataArray`` by supplying a pandas :py:class:`~pandas.Series`, :py:class:`~pandas.DataFrame` or :py:class:`~pandas.Panel`, any non-specified arguments in the ``DataArray`` constructor will be filled in from the pandas object: .. ipython:: python df = pd.DataFrame({'x': [0, 1], 'y': [2, 3]}, index=['a', 'b']) df.index.name = 'abc' df.columns.name = 'xyz' df xr.DataArray(df) DataArray properties ~~~~~~~~~~~~~~~~~~~~ Let's take a look at the important properties on our array: .. ipython:: python foo.values foo.dims foo.coords foo.attrs print(foo.name) You can modify ``values`` inplace: .. ipython:: python foo.values = 1.0 * foo.values .. note:: The array values in a :py:class:`~xarray.DataArray` have a single (homogeneous) data type. To work with heterogeneous or structured data types in xarray, use coordinates, or put separate ``DataArray`` objects in a single :py:class:`~xarray.Dataset` (see below). Now fill in some of that missing metadata: .. ipython:: python foo.name = 'foo' foo.attrs['units'] = 'meters' foo The :py:meth:`~xarray.DataArray.rename` method is another option, returning a new data array: .. ipython:: python foo.rename('bar') DataArray Coordinates ~~~~~~~~~~~~~~~~~~~~~ The ``coords`` property is ``dict`` like. Individual coordinates can be accessed from the coordinates by name, or even by indexing the data array itself: .. ipython:: python foo.coords['time'] foo['time'] These are also :py:class:`~xarray.DataArray` objects, which contain tick-labels for each dimension. Coordinates can also be set or removed by using the dictionary like syntax: .. ipython:: python foo['ranking'] = ('space', [1, 2, 3]) foo.coords del foo['ranking'] foo.coords Dataset ------- :py:class:`xarray.Dataset` is xarray's multi-dimensional equivalent of a :py:class:`~pandas.DataFrame`. It is a dict-like container of labeled arrays (:py:class:`~xarray.DataArray` objects) with aligned dimensions. It is designed as an in-memory representation of the data model from the `netCDF`__ file format. __ http://www.unidata.ucar.edu/software/netcdf/ In addition to the dict-like interface of the dataset itself, which can be used to access any variable in a dataset, datasets have four key properties: - ``dims``: a dictionary mapping from dimension names to the fixed length of each dimension (e.g., ``{'x': 6, 'y': 6, 'time': 8}``) - ``data_vars``: a dict-like container of DataArrays corresponding to variables - ``coords``: another dict-like container of DataArrays intended to label points used in ``data_vars`` (e.g., arrays of numbers, datetime objects or strings) - ``attrs``: an ``OrderedDict`` to hold arbitrary metadata The distinction between whether a variables falls in data or coordinates (borrowed from `CF conventions`_) is mostly semantic, and you can probably get away with ignoring it if you like: dictionary like access on a dataset will supply variables found in either category. However, xarray does make use of the distinction for indexing and computations. Coordinates indicate constant/fixed/independent quantities, unlike the varying/measured/dependent quantities that belong in data. .. _CF conventions: http://cfconventions.org/ Here is an example of how we might structure a dataset for a weather forecast: .. image:: _static/dataset-diagram.png In this example, it would be natural to call ``temperature`` and ``precipitation`` "data variables" and all the other arrays "coordinate variables" because they label the points along the dimensions. (see [1]_ for more background on this example). .. _dataarray constructor: Creating a Dataset ~~~~~~~~~~~~~~~~~~ To make an :py:class:`~xarray.Dataset` from scratch, supply dictionaries for any variables (``data_vars``), coordinates (``coords``) and attributes (``attrs``). - ``data_vars`` should be a dictionary with each key as the name of the variable and each value as one of: * A :py:class:`~xarray.DataArray` or :py:class:`~xarray.Variable` * A tuple of the form ``(dims, data[, attrs])``, which is converted into arguments for :py:class:`~xarray.Variable` * A pandas object, which is converted into a ``DataArray`` * A 1D array or list, which is interpreted as values for a one dimensional coordinate variable along the same dimension as it's name - ``coords`` should be a dictionary of the same form as ``data_vars``. - ``attrs`` should be a dictionary. Let's create some fake data for the example we show above: .. ipython:: python temp = 15 + 8 * np.random.randn(2, 2, 3) precip = 10 * np.random.rand(2, 2, 3) lon = [[-99.83, -99.32], [-99.79, -99.23]] lat = [[42.25, 42.21], [42.63, 42.59]] # for real use cases, its good practice to supply array attributes such as # units, but we won't bother here for the sake of brevity ds = xr.Dataset({'temperature': (['x', 'y', 'time'], temp), 'precipitation': (['x', 'y', 'time'], precip)}, coords={'lon': (['x', 'y'], lon), 'lat': (['x', 'y'], lat), 'time': pd.date_range('2014-09-06', periods=3), 'reference_time': pd.Timestamp('2014-09-05')}) ds Here we pass :py:class:`xarray.DataArray` objects or a pandas object as values in the dictionary: .. ipython:: python xr.Dataset({'bar': foo}) .. ipython:: python xr.Dataset({'bar': foo.to_pandas()}) Where a pandas object is supplied as a value, the names of its indexes are used as dimension names, and its data is aligned to any existing dimensions. You can also create an dataset from: - A :py:class:`pandas.DataFrame` or :py:class:`pandas.Panel` along its columns and items respectively, by passing it into the :py:class:`~xarray.Dataset` directly - A :py:class:`pandas.DataFrame` with :py:meth:`Dataset.from_dataframe `, which will additionally handle MultiIndexes See :ref:`pandas` - A netCDF file on disk with :py:func:`~xarray.open_dataset`. See :ref:`io`. Dataset contents ~~~~~~~~~~~~~~~~ :py:class:`~xarray.Dataset` implements the Python dictionary interface, with values given by :py:class:`xarray.DataArray` objects: .. ipython:: python 'temperature' in ds ds.keys() ds['temperature'] The valid keys include each listed coordinate and data variable. Data and coordinate variables are also contained separately in the :py:attr:`~xarray.Dataset.data_vars` and :py:attr:`~xarray.Dataset.coords` dictionary-like attributes: .. ipython:: python ds.data_vars ds.coords Finally, like data arrays, datasets also store arbitrary metadata in the form of `attributes`: .. ipython:: python ds.attrs ds.attrs['title'] = 'example attribute' ds xarray does not enforce any restrictions on attributes, but serialization to some file formats may fail if you use objects that are not strings, numbers or :py:class:`numpy.ndarray` objects. As a useful shortcut, you can use attribute style access for reading (but not setting) variables and attributes: .. ipython:: python ds.temperature This is particularly useful in an exploratory context, because you can tab-complete these variable names with tools like IPython. Dictionary like methods ~~~~~~~~~~~~~~~~~~~~~~~ We can update a dataset in-place using Python's standard dictionary syntax. For example, to create this example dataset from scratch, we could have written: .. ipython:: python ds = xr.Dataset() ds['temperature'] = (('x', 'y', 'time'), temp) ds['precipitation'] = (('x', 'y', 'time'), precip) ds.coords['lat'] = (('x', 'y'), lat) ds.coords['lon'] = (('x', 'y'), lon) ds.coords['time'] = pd.date_range('2014-09-06', periods=3) ds.coords['reference_time'] = pd.Timestamp('2014-09-05') To change the variables in a ``Dataset``, you can use all the standard dictionary methods, including ``values``, ``items``, ``__delitem__``, ``get`` and :py:meth:`~xarray.Dataset.update`. Note that assigning a ``DataArray`` or pandas object to a ``Dataset`` variable using ``__setitem__`` or ``update`` will :ref:`automatically align` the array(s) to the original dataset's indexes. You can copy a ``Dataset`` by calling the :py:meth:`~xarray.Dataset.copy` method. By default, the copy is shallow, so only the container will be copied: the arrays in the ``Dataset`` will still be stored in the same underlying :py:class:`numpy.ndarray` objects. You can copy all data by calling ``ds.copy(deep=True)``. .. _transforming datasets: Transforming datasets ~~~~~~~~~~~~~~~~~~~~~ In addition to dictionary-like methods (described above), xarray has additional methods (like pandas) for transforming datasets into new objects. For removing variables, you can select and drop an explicit list of variables by indexing with a list of names or using the :py:meth:`~xarray.Dataset.drop` methods to return a new ``Dataset``. These operations keep around coordinates: .. ipython:: python list(ds[['temperature']]) list(ds[['x']]) list(ds.drop('temperature')) If a dimension name is given as an argument to ``drop``, it also drops all variables that use that dimension: .. ipython:: python list(ds.drop('time')) As an alternate to dictionary-like modifications, you can use :py:meth:`~xarray.Dataset.assign` and :py:meth:`~xarray.Dataset.assign_coords`. These methods return a new dataset with additional (or replaced) or values: .. ipython:: python ds.assign(temperature2 = 2 * ds.temperature) There is also the :py:meth:`~xarray.Dataset.pipe` method that allows you to use a method call with an external function (e.g., ``ds.pipe(func)``) instead of simply calling it (e.g., ``func(ds)``). This allows you to write pipelines for transforming you data (using "method chaining") instead of writing hard to follow nested function calls: .. ipython:: python # these lines are equivalent, but with pipe we can make the logic flow # entirely from left to right plt.plot((2 * ds.temperature.sel(x=0)).mean('y')) (ds.temperature .sel(x=0) .pipe(lambda x: 2 * x) .mean('y') .pipe(plt.plot)) Both ``pipe`` and ``assign`` replicate the pandas methods of the same names (:py:meth:`DataFrame.pipe ` and :py:meth:`DataFrame.assign `). With xarray, there is no performance penalty for creating new datasets, even if variables are lazily loaded from a file on disk. Creating new objects instead of mutating existing objects often results in easier to understand code, so we encourage using this approach. Renaming variables ~~~~~~~~~~~~~~~~~~ Another useful option is the :py:meth:`~xarray.Dataset.rename` method to rename dataset variables: .. ipython:: python ds.rename({'temperature': 'temp', 'precipitation': 'precip'}) The related :py:meth:`~xarray.Dataset.swap_dims` method allows you do to swap dimension and non-dimension variables: .. ipython:: python ds.coords['day'] = ('time', [6, 7, 8]) ds.swap_dims({'time': 'day'}) .. _coordinates: Coordinates ----------- Coordinates are ancillary variables stored for ``DataArray`` and ``Dataset`` objects in the ``coords`` attribute: .. ipython:: python ds.coords Unlike attributes, xarray *does* interpret and persist coordinates in operations that transform xarray objects. One dimensional coordinates with a name equal to their sole dimension (marked by ``*`` when printing a dataset or data array) take on a special meaning in xarray. They are used for label based indexing and alignment, like the ``index`` found on a pandas :py:class:`~pandas.DataFrame` or :py:class:`~pandas.Series`. Indeed, these "dimension" coordinates use a :py:class:`pandas.Index` internally to store their values. Other than for indexing, xarray does not make any direct use of the values associated with coordinates. Coordinates with names not matching a dimension are not used for alignment or indexing, nor are they required to match when doing arithmetic (see :ref:`coordinates math`). Modifying coordinates ~~~~~~~~~~~~~~~~~~~~~ To entirely add or remove coordinate arrays, you can use dictionary like syntax, as shown above. To convert back and forth between data and coordinates, you can use the :py:meth:`~xarray.Dataset.set_coords` and :py:meth:`~xarray.Dataset.reset_coords` methods: .. ipython:: python ds.reset_coords() ds.set_coords(['temperature', 'precipitation']) ds['temperature'].reset_coords(drop=True) Notice that these operations skip coordinates with names given by dimensions, as used for indexing. This mostly because we are not entirely sure how to design the interface around the fact that xarray cannot store a coordinate and variable with the name but different values in the same dictionary. But we do recognize that supporting something like this would be useful. Coordinates methods ~~~~~~~~~~~~~~~~~~~ ``Coordinates`` objects also have a few useful methods, mostly for converting them into dataset objects: .. ipython:: python ds.coords.to_dataset() The merge method is particularly interesting, because it implements the same logic used for merging coordinates in arithmetic operations (see :ref:`comput`): .. ipython:: python alt = xr.Dataset(coords={'z': [10], 'lat': 0, 'lon': 0}) ds.coords.merge(alt.coords) The ``coords.merge`` method may be useful if you want to implement your own binary operations that act on xarray objects. In the future, we hope to write more helper functions so that you can easily make your functions act like xarray's built-in arithmetic. Indexes ~~~~~~~ To convert a coordinate (or any ``DataArray``) into an actual :py:class:`pandas.Index`, use the :py:meth:`~xarray.DataArray.to_index` method: .. ipython:: python ds['time'].to_index() A useful shortcut is the ``indexes`` property (on both ``DataArray`` and ``Dataset``), which lazily constructs a dictionary whose keys are given by each dimension and whose the values are ``Index`` objects: .. ipython:: python ds.indexes MultiIndex coordinates ~~~~~~~~~~~~~~~~~~~~~~ Xarray supports labeling coordinate values with a :py:class:`pandas.MultiIndex`: .. ipython:: python midx = pd.MultiIndex.from_arrays([['R', 'R', 'V', 'V'], [.1, .2, .7, .9]], names=('band', 'wn')) mda = xr.DataArray(np.random.rand(4), coords={'spec': midx}, dims='spec') mda For convenience multi-index levels are directly accessible as "virtual" or "derived" coordinates (marked by ``-`` when printing a dataset or data array): .. ipython:: python mda['band'] mda.wn Indexing with multi-index levels is also possible using the ``sel`` method (see :ref:`multi-level indexing`). Unlike other coordinates, "virtual" level coordinates are not stored in the ``coords`` attribute of ``DataArray`` and ``Dataset`` objects (although they are shown when printing the ``coords`` attribute). Consequently, most of the coordinates related methods don't apply for them. It also can't be used to replace one particular level. Because in a ``DataArray`` or ``Dataset`` object each multi-index level is accessible as a "virtual" coordinate, its name must not conflict with the names of the other levels, coordinates and data variables of the same object. Even though Xarray set default names for multi-indexes with unnamed levels, it is recommended that you explicitly set the names of the levels. .. [1] Latitude and longitude are 2D arrays because the dataset uses `projected coordinates`__. ``reference_time`` refers to the reference time at which the forecast was made, rather than ``time`` which is the valid time for which the forecast applies. __ http://en.wikipedia.org/wiki/Map_projection xarray-0.9.1/doc/environment.yml0000644131111500116100000000051513043511243016760 0ustar shoyereng00000000000000name: xarray-docs channels: - conda-forge - defaults dependencies: - python=3.5 - numpy=1.11.2 - pandas=0.19.1 - numpydoc=0.6.0 - matplotlib=2.0.0 - seaborn=0.7.1 - dask=0.12.0 - ipython=5.1.0 - sphinx=1.5 - netCDF4=1.2.5 - hdf4=4.2.12 # temporary install to get netCD4 working (GH1106) - cartopy=0.14.3 xarray-0.9.1/doc/examples/0000755131111500116100000000000013043675421015517 5ustar shoyereng00000000000000xarray-0.9.1/doc/examples/_code/0000755131111500116100000000000013043675421016570 5ustar shoyereng00000000000000xarray-0.9.1/doc/examples/_code/accessor_example.py0000644131111500116100000000130612755117657022472 0ustar shoyereng00000000000000import xarray as xr @xr.register_dataset_accessor('geo') class GeoAccessor(object): def __init__(self, xarray_obj): self._obj = xarray_obj self._center = None @property def center(self): """Return the geographic center point of this dataset.""" if self._center is None: # we can use a cache on our accessor objects, because accessors # themselves are cached on instances that access them. lon = self._obj.latitude lat = self._obj.longitude self._center = (float(lon.mean()), float(lat.mean())) return self._center def plot(self): """Plot data on a map.""" return 'plotting!' xarray-0.9.1/doc/examples/_code/weather_data_setup.py0000644131111500116100000000117312652753364023024 0ustar shoyereng00000000000000import xarray as xr import numpy as np import pandas as pd import seaborn as sns # pandas aware plotting library np.random.seed(123) times = pd.date_range('2000-01-01', '2001-12-31', name='time') annual_cycle = np.sin(2 * np.pi * (times.dayofyear / 365.25 - 0.28)) base = 10 + 15 * annual_cycle.reshape(-1, 1) tmin_values = base + 3 * np.random.randn(annual_cycle.size, 3) tmax_values = base + 10 + 3 * np.random.randn(annual_cycle.size, 3) ds = xr.Dataset({'tmin': (('time', 'location'), tmin_values), 'tmax': (('time', 'location'), tmax_values)}, {'time': times, 'location': ['IA', 'IN', 'IL']}) xarray-0.9.1/doc/examples/examples_pairplot.png0000644131111500116100000022352212652753364021773 0ustar shoyereng00000000000000PNG  IHDRXXfsBIT|d pHYsaa?i IDATxyx$lLI2Y&!!$l.bV[pO99mK{;^Zu)*"aL 3 $<L&Cu]\=|~rB!SF} BqK!bI%B1$B!b` !BL1 B!XB!SL,!B)&B!K!bI%B1$B!b` !BL1 B!XB!SL,!B)6cO?L?@cc#>(Y{^ !zsu԰|r{9VrOɖ-[ؾ};O>$۶mh4^ !z5cfjkkIKK#""B̾}hll?)lڴse˖=d!B\fLUWWǬY/--%77XAA%%%WrxB!`\.ؽ{7vV??u~xx8mmmWiB!͈jMMMg?chh!cxx*V!׻`q!t{A󇇇 B!D(Grr26H۽^ **k_rMC11{n~sNef0 yWlhZ>U*}}82KѨ 㓱]<0D8277v̈k޼y'|~ `4yyٱceee<tÉ>25'cxy|yltVy|277v̈%  ~ņ gx{1j5/2lذ^z=l!B\f @jj*ꄯL&6o|G$B11%B1H%B1$B!b` !BL1 B!XB!SL,!B)6c`700@Uɋ~ؖ))MBKXUI~wE.:N3/z hdB!&uNaBv!B\S$K!bI%B1$B!b` !BL1 B!XB!SL,!B)&B!K!N8GO* !׸S`dr<"OV!ƕŷKihjŷK);}uMK!9AhO,^FD(200@UI45!! p\?qMBL BN_.tIױvyד_0E#BD '_Ɲsۥ^.IeH,! ] }Lbo N>?d/jSKrXB!4s]:[gg^|#?%bjD7z\]~Bif]N`'֕ B1oߜR8_ZWq< BBܽ4G(pdGg<:w)sMܘw7șM6O+?766裏Ϛ5kػwUBq<εvI2zmXmlXƏGNRWֺhp챋isR*eY^4f>v=' 33-[}v|ImۆhʣB!.dΙ2[߀FEnJ[v(ֶS^ QD1Σ̈́k)ڡ8 _ !K/Dnn./܉ Kuגq׫31Fg<.LyyL1tmbhTgCVVs֓i c~_E*%C:͒X^Tz]QŰݎbՂDV斂xZiip05 KS8QIў:G31+eFX۶m|FFF裏GMMDEE]=BBfT^3$$)TNL{MwYMF_;_r b*Tϯ*%FPG*O3/#ѲD <ߜZ $ߑłC$/Oy]́r Kx e\UA<~Bc:Wʌ6oތ^ v\?!fj8|0Ͽ{ pLZ2)Vw]R]vf3f:>+ߥPUW vŸ?:FENJEjY4-;s4x>,52pƮR ޚNPa:-gXAN ehEI*Pv6z&|Wʌbcc~ DR@\\F~;vPVV?Ap8/Xc|S>4ӳ㓱1iCm2/pG f'G(+ocZ24DoMZ4]d ggs^!?@TxCvm}dEUK{?kpNu r[sdys{^iۥl\Ntxת >B1 yI%u$uMa|T\Oߠ g{.Nvqe:߹+>H8^HMгh3Yh}ITMdY\%͔<n3XB!L6":[㌭'u쇧ЧbǑI'<5UQÊ&[hX!׈F١Xif3v5؝. %;)YʵgF8\ц e:> =<|Y !WՈIIm'Ͻv^;HIm'#c6xZМG35 Zm sR"xgtb>('gV!Z&'%وlzGkl*:{X:/N{?6`Ea¸fES^yim,! 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Suppose we have a netCDF or xarray Dataset of monthly mean data and we want to calculate the seasonal average. To do this properly, we need to calculate the weighted average considering that each month has a different number of days. Suppose we have a netCDF or ``xarray.Dataset`` of monthly mean data and we want to calculate the seasonal average. To do this properly, we need to calculate the weighted average considering that each month has a different number of days. .. code:: python %matplotlib inline import numpy as np import pandas as pd import xarray as xr from netCDF4 import num2date import matplotlib.pyplot as plt print("numpy version : ", np.__version__) print("pandas version : ", pd.__version__) print("xarray version : ", xr.__version__) .. parsed-literal:: numpy version : 1.11.1 pandas version : 0.18.1 xarray version : 0.8.2 Some calendar information so we can support any netCDF calendar. ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ .. code:: python dpm = {'noleap': [0, 31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31], '365_day': [0, 31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31], 'standard': [0, 31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31], 'gregorian': [0, 31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31], 'proleptic_gregorian': [0, 31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31], 'all_leap': [0, 31, 29, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31], '366_day': [0, 31, 29, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31], '360_day': [0, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30]} A few calendar functions to determine the number of days in each month ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ If you were just using the standard calendar, it would be easy to use the ``calendar.month_range`` function. .. code:: python def leap_year(year, calendar='standard'): """Determine if year is a leap year""" leap = False if ((calendar in ['standard', 'gregorian', 'proleptic_gregorian', 'julian']) and (year % 4 == 0)): leap = True if ((calendar == 'proleptic_gregorian') and (year % 100 == 0) and (year % 400 != 0)): leap = False elif ((calendar in ['standard', 'gregorian']) and (year % 100 == 0) and (year % 400 != 0) and (year < 1583)): leap = False return leap def get_dpm(time, calendar='standard'): """ return a array of days per month corresponding to the months provided in `months` """ month_length = np.zeros(len(time), dtype=np.int) cal_days = dpm[calendar] for i, (month, year) in enumerate(zip(time.month, time.year)): month_length[i] = cal_days[month] if leap_year(year, calendar=calendar): month_length[i] += 1 return month_length Open the ``Dataset`` ^^^^^^^^^^^^^^^^^^^^ .. code:: python ds = xr.tutorial.load_dataset('rasm') print(ds) .. parsed-literal:: Dimensions: (time: 36, x: 275, y: 205) Coordinates: * time (time) datetime64[ns] 1980-09-16T12:00:00 1980-10-17 ... * y (y) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 ... * x (x) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 ... Data variables: Tair (time, y, x) float64 nan nan nan nan nan nan nan nan nan nan ... yc (y, x) float64 16.53 16.78 17.02 17.27 17.51 17.76 18.0 18.25 ... xc (y, x) float64 189.2 189.4 189.6 189.7 189.9 190.1 190.2 190.4 ... Attributes: title: /workspace/jhamman/processed/R1002RBRxaaa01a/lnd/temp/R1002RBRxaaa01a.vic.ha.1979-09-01.nc institution: U.W. source: RACM R1002RBRxaaa01a output_frequency: daily output_mode: averaged convention: CF-1.4 references: Based on the initial model of Liang et al., 1994, JGR, 99, 14,415- 14,429. comment: Output from the Variable Infiltration Capacity (VIC) model. nco_openmp_thread_number: 1 NCO: 4.3.7 history: history deleted for brevity Now for the heavy lifting: ^^^^^^^^^^^^^^^^^^^^^^^^^^ We first have to come up with the weights, - calculate the month lengths for each monthly data record - calculate weights using ``groupby('time.season')`` Finally, we just need to multiply our weights by the ``Dataset`` and sum allong the time dimension. .. code:: python # Make a DataArray with the number of days in each month, size = len(time) month_length = xr.DataArray(get_dpm(ds.time.to_index(), calendar='noleap'), coords=[ds.time], name='month_length') # Calculate the weights by grouping by 'time.season'. # Conversion to float type ('astype(float)') only necessary for Python 2.x weights = month_length.groupby('time.season') / month_length.astype(float).groupby('time.season').sum() # Test that the sum of the weights for each season is 1.0 np.testing.assert_allclose(weights.groupby('time.season').sum().values, np.ones(4)) # Calculate the weighted average ds_weighted = (ds * weights).groupby('time.season').sum(dim='time') .. code:: python print(ds_weighted) .. parsed-literal:: Dimensions: (season: 4, x: 275, y: 205) Coordinates: * y (y) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 ... * x (x) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 ... * season (season) object 'DJF' 'JJA' 'MAM' 'SON' Data variables: Tair (season, y, x) float64 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... xc (season, y, x) float64 189.2 189.4 189.6 189.7 189.9 190.1 ... yc (season, y, x) float64 16.53 16.78 17.02 17.27 17.51 17.76 18.0 ... .. code:: python # only used for comparisons ds_unweighted = ds.groupby('time.season').mean('time') ds_diff = ds_weighted - ds_unweighted .. code:: python # Quick plot to show the results notnull = pd.notnull(ds_unweighted['Tair'][0]) fig, axes = plt.subplots(nrows=4, ncols=3, figsize=(14,12)) for i, season in enumerate(('DJF', 'MAM', 'JJA', 'SON')): ds_weighted['Tair'].sel(season=season).where(notnull).plot.pcolormesh( ax=axes[i, 0], vmin=-30, vmax=30, cmap='Spectral_r', add_colorbar=True, extend='both') ds_unweighted['Tair'].sel(season=season).where(notnull).plot.pcolormesh( ax=axes[i, 1], vmin=-30, vmax=30, cmap='Spectral_r', add_colorbar=True, extend='both') ds_diff['Tair'].sel(season=season).where(notnull).plot.pcolormesh( ax=axes[i, 2], vmin=-0.1, vmax=.1, cmap='RdBu_r', add_colorbar=True, extend='both') axes[i, 0].set_ylabel(season) axes[i, 1].set_ylabel('') axes[i, 2].set_ylabel('') for ax in axes.flat: ax.axes.get_xaxis().set_ticklabels([]) ax.axes.get_yaxis().set_ticklabels([]) ax.axes.axis('tight') ax.set_xlabel('') axes[0, 0].set_title('Weighted by DPM') axes[0, 1].set_title('Equal Weighting') axes[0, 2].set_title('Difference') plt.tight_layout() fig.suptitle('Seasonal Surface Air Temperature', fontsize=16, y=1.02) .. parsed-literal:: .. image:: monthly_means_output.png .. code:: python # Wrap it into a simple function def season_mean(ds, calendar='standard'): # Make a DataArray of season/year groups year_season = xr.DataArray(ds.time.to_index().to_period(freq='Q-NOV').to_timestamp(how='E'), coords=[ds.time], name='year_season') # Make a DataArray with the number of days in each month, size = len(time) month_length = xr.DataArray(get_dpm(ds.time.to_index(), calendar=calendar), coords=[ds.time], name='month_length') # Calculate the weights by grouping by 'time.season' weights = month_length.groupby('time.season') / month_length.groupby('time.season').sum() # Test that the sum of the weights for each season is 1.0 np.testing.assert_allclose(weights.groupby('time.season').sum().values, np.ones(4)) # Calculate the weighted average return (ds * weights).groupby('time.season').sum(dim='time') xarray-0.9.1/doc/examples/monthly_means_output.png0000644131111500116100000154641712652753364022554 0ustar shoyereng00000000000000PNG  IHDRtBsBIT|d pHYs  ~ IDATxw%Gu&r@H 2,`˜`l0".€-c K"Llda$VZ7tܹsgvVڠ}:uN+J @  @ @L0@ @ ؋;@ H0@ @ ؋;@ H0@ @ ؋;@DTXD6HCDnkDdewU"2#";D WYdDC"ID =YBD>%"VD>eE=׽Qx߈sw;@ ;ށ@ p`!">|:0 x:py?6o#"끷jD 87 pKU=ئvUnOտ':p;!Z9/h`UעW{UdDFbU=m%x7 ꫻]|ZDd4i~<0٪޷ΛTT5}X#ܧ끏..UԠhW rO!/]j{w7 !j~+z<9JD "[D%"S{KD>'"|"q)wPDuXOȏDdڇsHDN)s,"KDdVD "Sn|RDenr8g+]7e|X;N4(i~wȯDdRDȏE}_uIDJW7}|CD~""ݍs2ǀv,5"fm~b9SDy_D]e ]D=; "rZD;nBzʮ7ߞߋȫ{ʼw|UDv?N׭="RFp2nYN{Ӆ"?]O=GtrkDri"2~GQ  /7ȱÀÅS LW"̮[pa < nW]C gE]e\2#E~]7#g?>!"'wY3FXnlȁOغ^~xp_H)=ɇ < wHpm_yoEU|2cہuK/|:|x ,q/[i]s>>e/45588GDg?\'{흾=ݗէ/~{> 9g$_b/?L1}ODB/>>>g|^Xي3$S38x^x~-;e8eߵl>DpԮv:n.1-p6 glowx]6㉁u-? 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V ͅ˦jz^?6C[ > =DžVfBfG=8z 4} s8ansçn:aHyn>cMFϠeω9O: f>} sq}QDDZ h xG.9qf;a#"n>\G. 0?^xGDDDDDDDDDDDDD܌""""""""""""""񎈈CD;"""""""""""""b=Dt#"""""""""""""9 O IENDB`xarray-0.9.1/doc/examples/multidimensional-coords.rst0000644131111500116100000000664113043511243023113 0ustar shoyereng00000000000000.. _examples.multidim: Working with Multidimensional Coordinates ========================================= Author: `Ryan Abernathey `__ Many datasets have *physical coordinates* which differ from their *logical coordinates*. Xarray provides several ways to plot and analyze such datasets. .. ipython:: python import numpy as np import pandas as pd import xarray as xr import netCDF4 import cartopy.crs as ccrs import matplotlib.pyplot as plt As an example, consider this dataset from the `xarray-data `__ repository. .. ipython:: python ds = xr.tutorial.load_dataset('rasm') ds In this example, the *logical coordinates* are ``x`` and ``y``, while the *physical coordinates* are ``xc`` and ``yc``, which represent the latitudes and longitude of the data. .. ipython:: python ds.xc.attrs ds.yc.attrs Plotting -------- Let's examine these coordinate variables by plotting them. .. ipython:: python fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(9,3)) ds.xc.plot(ax=ax1); @savefig xarray_multidimensional_coords_8_2.png width=100% ds.yc.plot(ax=ax2); Note that the variables ``xc`` (longitude) and ``yc`` (latitude) are two-dimensional scalar fields. If we try to plot the data variable ``Tair``, by default we get the logical coordinates. .. ipython:: python :suppress: f = plt.figure(figsize=(6, 4)) .. ipython:: python @savefig xarray_multidimensional_coords_10_1.png width=5in ds.Tair[0].plot(); In order to visualize the data on a conventional latitude-longitude grid, we can take advantage of xarray's ability to apply `cartopy `__ map projections. .. ipython:: python plt.figure(figsize=(7,2)); ax = plt.axes(projection=ccrs.PlateCarree()); ds.Tair[0].plot.pcolormesh(ax=ax, transform=ccrs.PlateCarree(), x='xc', y='yc', add_colorbar=False); ax.coastlines(); @savefig xarray_multidimensional_coords_12_0.png width=100% plt.tight_layout(); Multidimensional Groupby ------------------------ The above example allowed us to visualize the data on a regular latitude-longitude grid. But what if we want to do a calculation that involves grouping over one of these physical coordinates (rather than the logical coordinates), for example, calculating the mean temperature at each latitude. This can be achieved using xarray's ``groupby`` function, which accepts multidimensional variables. By default, ``groupby`` will use every unique value in the variable, which is probably not what we want. Instead, we can use the ``groupby_bins`` function to specify the output coordinates of the group. .. ipython:: python :suppress: f = plt.figure(figsize=(6, 4.5)) .. ipython:: python # define two-degree wide latitude bins lat_bins = np.arange(0, 91, 2) # define a label for each bin corresponding to the central latitude lat_center = np.arange(1, 90, 2) # group according to those bins and take the mean Tair_lat_mean = ds.Tair.groupby_bins('xc', lat_bins, labels=lat_center).mean() # plot the result @savefig xarray_multidimensional_coords_14_1.png width=5in Tair_lat_mean.plot(); Note that the resulting coordinate for the ``groupby_bins`` operation got the ``_bins`` suffix appended: ``xc_bins``. This help us distinguish it from the original multidimensional variable ``xc``. xarray-0.9.1/doc/examples/quick-overview.rst0000644131111500116100000001030513043511243021217 0ustar shoyereng00000000000000############## Quick overview ############## Here are some quick examples of what you can do with :py:class:`xarray.DataArray` objects. Everything is explained in much more detail in the rest of the documentation. To begin, import numpy, pandas and xarray using their customary abbreviations: .. ipython:: python import numpy as np import pandas as pd import xarray as xr Create a DataArray ------------------ You can make a DataArray from scratch by supplying data in the form of a numpy array or list, with optional *dimensions* and *coordinates*: .. ipython:: python xr.DataArray(np.random.randn(2, 3)) data = xr.DataArray(np.random.randn(2, 3), coords={'x': ['a', 'b']}, dims=('x', 'y')) data If you supply a pandas :py:class:`~pandas.Series` or :py:class:`~pandas.DataFrame`, metadata is copied directly: .. ipython:: python xr.DataArray(pd.Series(range(3), index=list('abc'), name='foo')) Here are the key properties for a ``DataArray``: .. ipython:: python # like in pandas, values is a numpy array that you can modify in-place data.values data.dims data.coords # you can use this dictionary to store arbitrary metadata data.attrs Indexing -------- xarray supports four kind of indexing. These operations are just as fast as in pandas, because we borrow pandas' indexing machinery. .. ipython:: python # positional and by integer label, like numpy data[[0, 1]] # positional and by coordinate label, like pandas data.loc['a':'b'] # by dimension name and integer label data.isel(x=slice(2)) # by dimension name and coordinate label data.sel(x=['a', 'b']) Computation ----------- Data arrays work very similarly to numpy ndarrays: .. ipython:: python data + 10 np.sin(data) data.T data.sum() However, aggregation operations can use dimension names instead of axis numbers: .. ipython:: python data.mean(dim='x') Arithmetic operations broadcast based on dimension name. This means you don't need to insert dummy dimensions for alignment: .. ipython:: python a = xr.DataArray(np.random.randn(3), [data.coords['y']]) b = xr.DataArray(np.random.randn(4), dims='z') a b a + b It also means that in most cases you do not need to worry about the order of dimensions: .. ipython:: python data - data.T Operations also align based on index labels: .. ipython:: python data[:-1] - data[:1] GroupBy ------- xarray supports grouped operations using a very similar API to pandas: .. ipython:: python labels = xr.DataArray(['E', 'F', 'E'], [data.coords['y']], name='labels') labels data.groupby(labels).mean('y') data.groupby(labels).apply(lambda x: x - x.min()) pandas ------ Xarray objects can be easily converted to and from pandas objects: .. ipython:: python series = data.to_series() series # convert back series.to_xarray() Datasets -------- :py:class:`xarray.Dataset` is a dict-like container of aligned ``DataArray`` objects. You can think of it as a multi-dimensional generalization of the :py:class:`pandas.DataFrame`: .. ipython:: python ds = xr.Dataset({'foo': data, 'bar': ('x', [1, 2]), 'baz': np.pi}) ds Use dictionary indexing to pull out ``Dataset`` variables as ``DataArray`` objects: .. ipython:: python ds['foo'] Variables in datasets can have different ``dtype`` and even different dimensions, but all dimensions are assumed to refer to points in the same shared coordinate system. You can do almost everything you can do with ``DataArray`` objects with ``Dataset`` objects (including indexing and arithmetic) if you prefer to work with multiple variables at once. NetCDF ------ NetCDF is the recommended binary serialization format for xarray objects. Users from the geosciences will recognize that the :py:class:`~xarray.Dataset` data model looks very similar to a netCDF file (which, in fact, inspired it). You can directly read and write xarray objects to disk using :py:meth:`~xarray.Dataset.to_netcdf`, :py:func:`~xarray.open_dataset` and :py:func:`~xarray.open_dataarray`: .. ipython:: python ds.to_netcdf('example.nc') xr.open_dataset('example.nc') .. ipython:: python :suppress: import os os.remove('example.nc') xarray-0.9.1/doc/examples/weather-data.rst0000644131111500116100000000560213043511243020611 0ustar shoyereng00000000000000.. _toy weather data: Toy weather data ================ Here is an example of how to easily manipulate a toy weather dataset using xarray and other recommended Python libraries: .. contents:: :local: :depth: 1 Shared setup: .. literalinclude:: _code/weather_data_setup.py .. ipython:: python :suppress: fpath = "examples/_code/weather_data_setup.py" with open(fpath) as f: code = compile(f.read(), fpath, 'exec') exec(code) Examine a dataset with pandas_ and seaborn_ ------------------------------------------- .. _pandas: http://pandas.pydata.org .. _seaborn: http://stanford.edu/~mwaskom/software/seaborn .. ipython:: python ds df = ds.to_dataframe() df.head() df.describe() @savefig examples_tmin_tmax_plot.png ds.mean(dim='location').to_dataframe().plot() .. ipython:: :verbatim: In [6]: sns.pairplot(df.reset_index(), vars=ds.data_vars) Out[6]: .. image:: examples_pairplot.png .. .. ipython:: python @savefig examples_pairplot.png sns.pairplot(df.reset_index(), vars=ds.data_vars) .. _average by month: Probability of freeze by calendar month --------------------------------------- .. ipython:: python freeze = (ds['tmin'] <= 0).groupby('time.month').mean('time') freeze @savefig examples_freeze_prob.png freeze.to_pandas().plot() .. _monthly average: Monthly averaging ----------------- .. ipython:: python monthly_avg = ds.resample('1MS', dim='time', how='mean') @savefig examples_tmin_tmax_plot_mean.png monthly_avg.sel(location='IA').to_dataframe().plot(style='s-') Note that ``MS`` here refers to Month-Start; ``M`` labels Month-End (the last day of the month). .. _monthly anomalies: Calculate monthly anomalies --------------------------- In climatology, "anomalies" refer to the difference between observations and typical weather for a particular season. Unlike observations, anomalies should not show any seasonal cycle. .. ipython:: python climatology = ds.groupby('time.month').mean('time') anomalies = ds.groupby('time.month') - climatology @savefig examples_anomalies_plot.png anomalies.mean('location').to_dataframe()[['tmin', 'tmax']].plot() .. _fill with climatology: Fill missing values with climatology ------------------------------------ The :py:func:`~xarray.Dataset.fillna` method on grouped objects lets you easily fill missing values by group: .. ipython:: python # throw away the first half of every month some_missing = ds.tmin.sel(time=ds['time.day'] > 15).reindex_like(ds) filled = some_missing.groupby('time.month').fillna(climatology.tmin) both = xr.Dataset({'some_missing': some_missing, 'filled': filled}) both df = both.sel(time='2000').mean('location').reset_coords(drop=True).to_dataframe() @savefig examples_filled.png df[['filled', 'some_missing']].plot() xarray-0.9.1/doc/examples.rst0000644131111500116100000000025012755117657016261 0ustar shoyereng00000000000000Examples ======== .. toctree:: :maxdepth: 2 examples/quick-overview examples/weather-data examples/monthly-means examples/multidimensional-coords xarray-0.9.1/doc/faq.rst0000644131111500116100000001733213043511243015177 0ustar shoyereng00000000000000Frequently Asked Questions ========================== Why is pandas not enough? ------------------------- pandas, thanks to its unrivaled speed and flexibility, has emerged as the premier python package for working with labeled arrays. So why are we contributing to further fragmentation__ in the ecosystem for working with data arrays in Python? __ http://wesmckinney.com/blog/a-roadmap-for-rich-scientific-data-structures-in-python/ Sometimes, we really want to work with collections of higher dimensional arrays (`ndim > 2`), or arrays for which the order of dimensions (e.g., columns vs rows) shouldn't really matter. For example, climate and weather data is often natively expressed in 4 or more dimensions: time, x, y and z. Pandas does support `N-dimensional panels`__, but the implementation is very limited: __ http://pandas.pydata.org/pandas-docs/stable/dsintro.html#panelnd-experimental - You need to create a new factory type for each dimensionality. - You can't do math between NDPanels with different dimensionality. - Each dimension in a NDPanel has a name (e.g., 'labels', 'items', 'major_axis', etc.) but the dimension names refer to order, not their meaning. You can't specify an operation as to be applied along the "time" axis. Fundamentally, the N-dimensional panel is limited by its context in pandas's tabular model, which treats a 2D ``DataFrame`` as a collections of 1D ``Series``, a 3D ``Panel`` as a collection of 2D ``DataFrame``, and so on. In my experience, it usually easier to work with a DataFrame with a hierarchical index rather than to use higher dimensional (*N > 3*) data structures in pandas. Another use case is handling collections of arrays with different numbers of dimensions. For example, suppose you have a 2D array and a handful of associated 1D arrays that share one of the same axes. Storing these in one pandas object is possible but awkward -- you can either upcast all the 1D arrays to 2D and store everything in a ``Panel``, or put everything in a ``DataFrame``, where the first few columns have a different meaning than the other columns. In contrast, this sort of data structure fits very naturally in an xarray ``Dataset``. Pandas gets a lot of things right, but scientific users need fully multi- dimensional data structures. How do xarray data structures differ from those found in pandas? ---------------------------------------------------------------- The main distinguishing feature of xarray's ``DataArray`` over labeled arrays in pandas is that dimensions can have names (e.g., "time", "latitude", "longitude"). Names are much easier to keep track of than axis numbers, and xarray uses dimension names for indexing, aggregation and broadcasting. Not only can you write ``x.sel(time='2000-01-01')`` and ``x.mean(dim='time')``, but operations like ``x - x.mean(dim='time')`` always work, no matter the order of the "time" dimension. You never need to reshape arrays (e.g., with ``np.newaxis``) to align them for arithmetic operations in xarray. Should I use xarray instead of pandas? -------------------------------------- It's not an either/or choice! xarray provides robust support for converting back and forth between the tabular data-structures of pandas and its own multi-dimensional data-structures. That said, you should only bother with xarray if some aspect of data is fundamentally multi-dimensional. If your data is unstructured or one-dimensional, stick with pandas, which is a more developed toolkit for doing data analysis in Python. .. _approach to metadata: What is your approach to metadata? ---------------------------------- We are firm believers in the power of labeled data! In addition to dimensions and coordinates, xarray supports arbitrary metadata in the form of global (Dataset) and variable specific (DataArray) attributes (``attrs``). Automatic interpretation of labels is powerful but also reduces flexibility. With xarray, we draw a firm line between labels that the library understands (``dims`` and ``coords``) and labels for users and user code (``attrs``). For example, we do not automatically interpret and enforce units or `CF conventions`_. (An exception is serialization to and from netCDF files.) .. _CF conventions: http://cfconventions.org/latest.html An implication of this choice is that we do not propagate ``attrs`` through most operations unless explicitly flagged (some methods have a ``keep_attrs`` option). Similarly, xarray does not check for conflicts between ``attrs`` when combining arrays and datasets, unless explicitly requested with the option ``compat='identical'``. The guiding principle is that metadata should not be allowed to get in the way. What other netCDF related Python libraries should I know about? --------------------------------------------------------------- `netCDF4-python`__ provides a lower level interface for working with netCDF and OpenDAP datasets in Python. We use netCDF4-python internally in xarray, and have contributed a number of improvements and fixes upstream. xarray does not yet support all of netCDF4-python's features, such as writing to netCDF groups or modifying files on-disk. __ https://github.com/Unidata/netcdf4-python Iris_ (supported by the UK Met office) provides similar tools for in- memory manipulation of labeled arrays, aimed specifically at weather and climate data needs. Indeed, the Iris :py:class:`~iris.cube.Cube` was direct inspiration for xarray's :py:class:`~xarray.DataArray`. xarray and Iris take very different approaches to handling metadata: Iris strictly interprets `CF conventions`_. Iris particularly shines at mapping, thanks to its integration with Cartopy_. .. _Iris: http://scitools.org.uk/iris/ .. _Cartopy: http://scitools.org.uk/cartopy/docs/latest/ `UV-CDAT`__ is another Python library that implements in-memory netCDF-like variables and `tools for working with climate data`__. __ http://uvcdat.llnl.gov/ __ http://drclimate.wordpress.com/2014/01/02/a-beginners-guide-to-scripting-with-uv-cdat/ We think the design decisions we have made for xarray (namely, basing it on pandas) make it a faster and more flexible data analysis tool. That said, Iris and CDAT have some great domain specific functionality, and we would love to have support for converting their native objects to and from xarray (see :issue:`37` and :issue:`133`) How should I cite xarray? ------------------------- If you are using xarray and would like to cite it in academic publication, we would certainly appreciate it. We recommend two citations. 1. At a minimum, we recommend citing the xarray overview journal article, submitted to the Journal of Open Research Software. - Hoyer, S., Hamman, J. (In review). Xarray: N-D labeled arrays and datasets in Python. Journal of Open Research Software. Here’s an example of a BibTeX entry:: @article{hoyer2016xarray, title = {xarray: {N-D} labeled arrays and datasets in {Python}}, author = {Hoyer, S. and J. Hamman}, journal = {In review, J. Open Res. Software}, year = {2016} } 2. You may also want to cite a specific version of the xarray package. We provide a `Zenodo`__ citation and DOI for this purpose: .. image:: https://zenodo.org/badge/7766/pydata/xarray.svg :target: https://zenodo.org/badge/latestdoi/7766/pydata/xarray. An example BibTeX entry:: @misc{xarray_v0_8_0, author = {Stephan Hoyer and Clark Fitzgerald and Joe Hamman and others}, title = {xarray: v0.8.0}, month = aug, year = 2016, doi = {10.5281/zenodo.59499}, url = {http://dx.doi.org/10.5281/zenodo.59499} } __ https://zenodo.org/badge/latestdoi/7766/pydata/xarray xarray-0.9.1/doc/groupby.rst0000644131111500116100000001423713043511243016120 0ustar shoyereng00000000000000.. _groupby: GroupBy: split-apply-combine ---------------------------- xarray supports `"group by"`__ operations with the same API as pandas to implement the `split-apply-combine`__ strategy: __ http://pandas.pydata.org/pandas-docs/stable/groupby.html __ http://www.jstatsoft.org/v40/i01/paper - Split your data into multiple independent groups. - Apply some function to each group. - Combine your groups back into a single data object. Group by operations work on both :py:class:`~xarray.Dataset` and :py:class:`~xarray.DataArray` objects. Most of the examples focus on grouping by a single one-dimensional variable, although support for grouping over a multi-dimensional variable has recently been implemented. Note that for one-dimensional data, it is usually faster to rely on pandas' implementation of the same pipeline. Split ~~~~~ Let's create a simple example dataset: .. ipython:: python :suppress: import numpy as np import pandas as pd import xarray as xr np.random.seed(123456) .. ipython:: python ds = xr.Dataset({'foo': (('x', 'y'), np.random.rand(4, 3))}, coords={'x': [10, 20, 30, 40], 'letters': ('x', list('abba'))}) arr = ds['foo'] ds If we groupby the name of a variable or coordinate in a dataset (we can also use a DataArray directly), we get back a ``GroupBy`` object: .. ipython:: python ds.groupby('letters') This object works very similarly to a pandas GroupBy object. You can view the group indices with the ``groups`` attribute: .. ipython:: python ds.groupby('letters').groups You can also iterate over groups in ``(label, group)`` pairs: .. ipython:: python list(ds.groupby('letters')) Just like in pandas, creating a GroupBy object is cheap: it does not actually split the data until you access particular values. Binning ~~~~~~~ Sometimes you don't want to use all the unique values to determine the groups but instead want to "bin" the data into coarser groups. You could always create a customized coordinate, but xarray facilitates this via the :py:meth:`~xarray.Dataset.groupby_bins` method. .. ipython:: python x_bins = [0,25,50] ds.groupby_bins('x', x_bins).groups The binning is implemented via `pandas.cut`__, whose documentation details how the bins are assigned. As seen in the example above, by default, the bins are labeled with strings using set notation to precisely identify the bin limits. To override this behavior, you can specify the bin labels explicitly. Here we choose `float` labels which identify the bin centers: .. ipython:: python x_bin_labels = [12.5,37.5] ds.groupby_bins('x', x_bins, labels=x_bin_labels).groups __ http://pandas.pydata.org/pandas-docs/version/0.17.1/generated/pandas.cut.html Apply ~~~~~ To apply a function to each group, you can use the flexible :py:meth:`~xarray.DatasetGroupBy.apply` method. The resulting objects are automatically concatenated back together along the group axis: .. ipython:: python def standardize(x): return (x - x.mean()) / x.std() arr.groupby('letters').apply(standardize) GroupBy objects also have a :py:meth:`~xarray.DatasetGroupBy.reduce` method and methods like :py:meth:`~xarray.DatasetGroupBy.mean` as shortcuts for applying an aggregation function: .. ipython:: python arr.groupby('letters').mean(dim='x') Using a groupby is thus also a convenient shortcut for aggregating over all dimensions *other than* the provided one: .. ipython:: python ds.groupby('x').std() First and last ~~~~~~~~~~~~~~ There are two special aggregation operations that are currently only found on groupby objects: first and last. These provide the first or last example of values for group along the grouped dimension: .. ipython:: python ds.groupby('letters').first() By default, they skip missing values (control this with ``skipna``). Grouped arithmetic ~~~~~~~~~~~~~~~~~~ GroupBy objects also support a limited set of binary arithmetic operations, as a shortcut for mapping over all unique labels. Binary arithmetic is supported for ``(GroupBy, Dataset)`` and ``(GroupBy, DataArray)`` pairs, as long as the dataset or data array uses the unique grouped values as one of its index coordinates. For example: .. ipython:: python alt = arr.groupby('letters').mean() alt ds.groupby('letters') - alt This last line is roughly equivalent to the following:: results = [] for label, group in ds.groupby('letters'): results.append(group - alt.sel(x=label)) xr.concat(results, dim='x') Squeezing ~~~~~~~~~ When grouping over a dimension, you can control whether the dimension is squeezed out or if it should remain with length one on each group by using the ``squeeze`` parameter: .. ipython:: python next(iter(arr.groupby('x'))) .. ipython:: python next(iter(arr.groupby('x', squeeze=False))) Although xarray will attempt to automatically :py:attr:`~xarray.DataArray.transpose` dimensions back into their original order when you use apply, it is sometimes useful to set ``squeeze=False`` to guarantee that all original dimensions remain unchanged. You can always squeeze explicitly later with the Dataset or DataArray :py:meth:`~xarray.DataArray.squeeze` methods. .. _groupby.multidim: Multidimensional Grouping ~~~~~~~~~~~~~~~~~~~~~~~~~ Many datasets have a multidimensional coordinate variable (e.g. longitude) which is different from the logical grid dimensions (e.g. nx, ny). Such variables are valid under the `CF conventions`__. Xarray supports groupby operations over multidimensional coordinate variables: __ http://cfconventions.org/cf-conventions/v1.6.0/cf-conventions.html#_two_dimensional_latitude_longitude_coordinate_variables .. ipython:: python da = xr.DataArray([[0,1],[2,3]], coords={'lon': (['ny','nx'], [[30,40],[40,50]] ), 'lat': (['ny','nx'], [[10,10],[20,20]] ),}, dims=['ny','nx']) da da.groupby('lon').sum() da.groupby('lon').apply(lambda x: x - x.mean(), shortcut=False) Because multidimensional groups have the ability to generate a very large number of bins, coarse-binning via :py:meth:`~xarray.Dataset.groupby_bins` may be desirable: .. ipython:: python da.groupby_bins('lon', [0,45,50]).sum() xarray-0.9.1/doc/index.rst0000644131111500116100000000615712755117657015566 0ustar shoyereng00000000000000 .. image:: _static/dataset-diagram-logo.png :width: 300 px :align: center | N-D labeled arrays and datasets in Python ========================================= **xarray** (formerly **xray**) is an open source project and Python package that aims to bring the labeled data power of pandas_ to the physical sciences, by providing N-dimensional variants of the core pandas data structures. Our goal is to provide a pandas-like and pandas-compatible toolkit for analytics on multi-dimensional arrays, rather than the tabular data for which pandas excels. Our approach adopts the `Common Data Model`_ for self- describing scientific data in widespread use in the Earth sciences: ``xarray.Dataset`` is an in-memory representation of a netCDF file. .. note:: xray is now xarray! See :ref:`the v0.7.0 release notes` for more details. The preferred URL for these docs is now http://xarray.pydata.org. .. _pandas: http://pandas.pydata.org .. _Common Data Model: http://www.unidata.ucar.edu/software/thredds/current/netcdf-java/CDM .. _netCDF: http://www.unidata.ucar.edu/software/netcdf .. _OPeNDAP: http://www.opendap.org/ Documentation ------------- .. toctree:: :maxdepth: 1 whats-new why-xarray faq examples installing data-structures indexing computation groupby reshaping combining time-series pandas io dask plotting api internals See also -------- - Stephan Hoyer's `SciPy2015 talk`_ introducing xarray to a general audience. - Stephan Hoyer's `2015 Unidata Users Workshop talk`_ and `tutorial`_ (`with answers`_) introducing xarray to users familiar with netCDF. - `Nicolas Fauchereau's tutorial`_ on xarray for netCDF users. .. _SciPy2015 talk: https://www.youtube.com/watch?v=X0pAhJgySxk .. _2015 Unidata Users Workshop talk: https://www.youtube.com/watch?v=J9ypQOnt5l8 .. _tutorial: https://github.com/Unidata/unidata-users-workshop/blob/master/notebooks/xray-tutorial.ipynb .. _with answers: https://github.com/Unidata/unidata-users-workshop/blob/master/notebooks/xray-tutorial-with-answers.ipynb .. _Nicolas Fauchereau's tutorial: http://nbviewer.ipython.org/github/nicolasfauchereau/metocean/blob/master/notebooks/xray.ipynb Get in touch ------------ - Ask usage questions on `StackOverflow`_. - Report bugs, suggest features or view the source code `on GitHub`_. - For less well defined questions or ideas, use the `mailing list`_. - You can also try our chatroom `on Gitter`_. .. _StackOverFlow: http://stackoverflow.com/questions/tagged/python-xarray .. _mailing list: https://groups.google.com/forum/#!forum/xarray .. _on Gitter: https://gitter.im/pydata/xarray .. _on GitHub: http://github.com/pydata/xarray License ------- xarray is available under the open source `Apache License`__. __ http://www.apache.org/licenses/LICENSE-2.0.html History ------- xarray is an evolution of an internal tool developed at `The Climate Corporation`__. It was originally written by Climate Corp researchers Stephan Hoyer, Alex Kleeman and Eugene Brevdo and was released as open source in May 2014. The project was renamed from "xray" in January 2016. __ http://climate.com/ xarray-0.9.1/doc/indexing.rst0000644131111500116100000004231513043511243016234 0ustar shoyereng00000000000000.. _indexing: Indexing and selecting data =========================== .. ipython:: python :suppress: import numpy as np import pandas as pd import xarray as xr np.random.seed(123456) Similarly to pandas objects, xarray objects support both integer and label based lookups along each dimension. However, xarray objects also have named dimensions, so you can optionally use dimension names instead of relying on the positional ordering of dimensions. Thus in total, xarray supports four different kinds of indexing, as described below and summarized in this table: .. |br| raw:: html
+------------------+--------------+---------------------------------+--------------------------------+ | Dimension lookup | Index lookup | ``DataArray`` syntax | ``Dataset`` syntax | +==================+==============+=================================+================================+ | Positional | By integer | ``arr[:, 0]`` | *not available* | +------------------+--------------+---------------------------------+--------------------------------+ | Positional | By label | ``arr.loc[:, 'IA']`` | *not available* | +------------------+--------------+---------------------------------+--------------------------------+ | By name | By integer | ``arr.isel(space=0)`` or |br| | ``ds.isel(space=0)`` or |br| | | | | ``arr[dict(space=0)]`` | ``ds[dict(space=0)]`` | +------------------+--------------+---------------------------------+--------------------------------+ | By name | By label | ``arr.sel(space='IA')`` or |br| | ``ds.sel(space='IA')`` or |br| | | | | ``arr.loc[dict(space='IA')]`` | ``ds.loc[dict(space='IA')]`` | +------------------+--------------+---------------------------------+--------------------------------+ Positional indexing ------------------- Indexing a :py:class:`~xarray.DataArray` directly works (mostly) just like it does for numpy arrays, except that the returned object is always another DataArray: .. ipython:: python arr = xr.DataArray(np.random.rand(4, 3), [('time', pd.date_range('2000-01-01', periods=4)), ('space', ['IA', 'IL', 'IN'])]) arr[:2] arr[0, 0] arr[:, [2, 1]] Attributes are persisted in all indexing operations. .. warning:: Positional indexing deviates from the NumPy when indexing with multiple arrays like ``arr[[0, 1], [0, 1]]``, as described in :ref:`orthogonal`. See :ref:`pointwise indexing` for how to achieve this functionality in xarray. xarray also supports label-based indexing, just like pandas. Because we use a :py:class:`pandas.Index` under the hood, label based indexing is very fast. To do label based indexing, use the :py:attr:`~xarray.DataArray.loc` attribute: .. ipython:: python arr.loc['2000-01-01':'2000-01-02', 'IA'] You can perform any of the label indexing operations `supported by pandas`__, including indexing with individual, slices and arrays of labels, as well as indexing with boolean arrays. Like pandas, label based indexing in xarray is *inclusive* of both the start and stop bounds. __ http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-label Setting values with label based indexing is also supported: .. ipython:: python arr.loc['2000-01-01', ['IL', 'IN']] = -10 arr Indexing with labeled dimensions -------------------------------- With labeled dimensions, we do not have to rely on dimension order and can use them explicitly to slice data. There are two ways to do this: 1. Use a dictionary as the argument for array positional or label based array indexing: .. ipython:: python # index by integer array indices arr[dict(space=0, time=slice(None, 2))] # index by dimension coordinate labels arr.loc[dict(time=slice('2000-01-01', '2000-01-02'))] 2. Use the :py:meth:`~xarray.DataArray.sel` and :py:meth:`~xarray.DataArray.isel` convenience methods: .. ipython:: python # index by integer array indices arr.isel(space=0, time=slice(None, 2)) # index by dimension coordinate labels arr.sel(time=slice('2000-01-01', '2000-01-02')) The arguments to these methods can be any objects that could index the array along the dimension given by the keyword, e.g., labels for an individual value, Python :py:func:`slice` objects or 1-dimensional arrays. .. note:: We would love to be able to do indexing with labeled dimension names inside brackets, but unfortunately, Python `does yet not support`__ indexing with keyword arguments like ``arr[space=0]`` __ http://legacy.python.org/dev/peps/pep-0472/ .. warning:: Do not try to assign values when using any of the indexing methods ``isel``, ``isel_points``, ``sel`` or ``sel_points``:: # DO NOT do this arr.isel(space=0) = 0 Depending on whether the underlying numpy indexing returns a copy or a view, the method will fail, and when it fails, **it will fail silently**. Instead, you should use normal index assignment:: # this is safe arr[dict(space=0)] = 0 .. _pointwise indexing: Pointwise indexing ------------------ xarray pointwise indexing supports the indexing along multiple labeled dimensions using list-like objects. While :py:meth:`~xarray.DataArray.isel` performs orthogonal indexing, the :py:meth:`~xarray.DataArray.isel_points` method provides similar numpy indexing behavior as if you were using multiple lists to index an array (e.g. ``arr[[0, 1], [0, 1]]`` ): .. ipython:: python # index by integer array indices da = xr.DataArray(np.arange(56).reshape((7, 8)), dims=['x', 'y']) da da.isel_points(x=[0, 1, 6], y=[0, 1, 0]) There is also :py:meth:`~xarray.DataArray.sel_points`, which analogously allows you to do point-wise indexing by label: .. ipython:: python times = pd.to_datetime(['2000-01-03', '2000-01-02', '2000-01-01']) arr.sel_points(space=['IA', 'IL', 'IN'], time=times) The equivalent pandas method to ``sel_points`` is :py:meth:`~pandas.DataFrame.lookup`. Dataset indexing ---------------- We can also use these methods to index all variables in a dataset simultaneously, returning a new dataset: .. ipython:: python ds = arr.to_dataset(name='foo') ds.isel(space=[0], time=[0]) ds.sel(time='2000-01-01') ds2 = da.to_dataset(name='bar') ds2.isel_points(x=[0, 1, 6], y=[0, 1, 0], dim='points') Positional indexing on a dataset is not supported because the ordering of dimensions in a dataset is somewhat ambiguous (it can vary between different arrays). However, you can do normal indexing with labeled dimensions: .. ipython:: python ds[dict(space=[0], time=[0])] ds.loc[dict(time='2000-01-01')] Using indexing to *assign* values to a subset of dataset (e.g., ``ds[dict(space=0)] = 1``) is not yet supported. Dropping labels --------------- The :py:meth:`~xarray.Dataset.drop` method returns a new object with the listed index labels along a dimension dropped: .. ipython:: python ds.drop(['IN', 'IL'], dim='space') ``drop`` is both a ``Dataset`` and ``DataArray`` method. .. _nearest neighbor lookups: Nearest neighbor lookups ------------------------ The label based selection methods :py:meth:`~xarray.Dataset.sel`, :py:meth:`~xarray.Dataset.reindex` and :py:meth:`~xarray.Dataset.reindex_like` all support ``method`` and ``tolerance`` keyword argument. The method parameter allows for enabling nearest neighbor (inexact) lookups by use of the methods ``'pad'``, ``'backfill'`` or ``'nearest'``: .. ipython:: python data = xr.DataArray([1, 2, 3], [('x', [0, 1, 2])]) data.sel(x=[1.1, 1.9], method='nearest') data.sel(x=0.1, method='backfill') data.reindex(x=[0.5, 1, 1.5, 2, 2.5], method='pad') Tolerance limits the maximum distance for valid matches with an inexact lookup: .. ipython:: python data.reindex(x=[1.1, 1.5], method='nearest', tolerance=0.2) Using ``method='nearest'`` or a scalar argument with ``.sel()`` requires pandas version 0.16 or newer. Using ``tolerance`` requries pandas version 0.17 or newer. The method parameter is not yet supported if any of the arguments to ``.sel()`` is a ``slice`` object: .. ipython:: :verbatim: In [1]: data.sel(x=slice(1, 3), method='nearest') NotImplementedError However, you don't need to use ``method`` to do inexact slicing. Slicing already returns all values inside the range (inclusive), as long as the index labels are monotonic increasing: .. ipython:: python data.sel(x=slice(0.9, 3.1)) Indexing axes with monotonic decreasing labels also works, as long as the ``slice`` or ``.loc`` arguments are also decreasing: .. ipython:: python reversed_data = data[::-1] reversed_data.loc[3.1:0.9] .. _masking with where: Masking with ``where`` ---------------------- Indexing methods on xarray objects generally return a subset of the original data. However, it is sometimes useful to select an object with the same shape as the original data, but with some elements masked. To do this type of selection in xarray, use :py:meth:`~xarray.DataArray.where`: .. ipython:: python arr2 = xr.DataArray(np.arange(16).reshape(4, 4), dims=['x', 'y']) arr2.where(arr2.x + arr2.y < 4) This is particularly useful for ragged indexing of multi-dimensional data, e.g., to apply a 2D mask to an image. Note that ``where`` follows all the usual xarray broadcasting and alignment rules for binary operations (e.g., ``+``) between the object being indexed and the condition, as described in :ref:`comput`: .. ipython:: python arr2.where(arr2.y < 2) By default ``where`` maintains the original size of the data. For cases where the selected data size is much smaller than the original data, use of the option ``drop=True`` clips coordinate elements that are fully masked: .. ipython:: python arr2.where(arr2.y < 2, drop=True) .. _multi-level indexing: Multi-level indexing -------------------- Just like pandas, advanced indexing on multi-level indexes is possible with ``loc`` and ``sel``. You can slice a multi-index by providing multiple indexers, i.e., a tuple of slices, labels, list of labels, or any selector allowed by pandas: .. ipython:: python midx = pd.MultiIndex.from_product([list('abc'), [0, 1]], names=('one', 'two')) mda = xr.DataArray(np.random.rand(6, 3), [('x', midx), ('y', range(3))]) mda mda.sel(x=(list('ab'), [0])) You can also select multiple elements by providing a list of labels or tuples or a slice of tuples: .. ipython:: python mda.sel(x=[('a', 0), ('b', 1)]) Additionally, xarray supports dictionaries: .. ipython:: python mda.sel(x={'one': 'a', 'two': 0}) For convenience, ``sel`` also accepts multi-index levels directly as keyword arguments: .. ipython:: python mda.sel(one='a', two=0) Note that using ``sel`` it is not possible to mix a dimension indexer with level indexers for that dimension (e.g., ``mda.sel(x={'one': 'a'}, two=0)`` will raise a ``ValueError``). Like pandas, xarray handles partial selection on multi-index (level drop). As shown below, it also renames the dimension / coordinate when the multi-index is reduced to a single index. .. ipython:: python mda.loc[{'one': 'a'}, ...] Unlike pandas, xarray does not guess whether you provide index levels or dimensions when using ``loc`` in some ambiguous cases. For example, for ``mda.loc[{'one': 'a', 'two': 0}]`` and ``mda.loc['a', 0]`` xarray always interprets ('one', 'two') and ('a', 0) as the names and labels of the 1st and 2nd dimension, respectively. You must specify all dimensions or use the ellipsis in the ``loc`` specifier, e.g. in the example above, ``mda.loc[{'one': 'a', 'two': 0}, :]`` or ``mda.loc[('a', 0), ...]``. Multi-dimensional indexing -------------------------- xarray does not yet support efficient routines for generalized multi-dimensional indexing or regridding. However, we are definitely interested in adding support for this in the future (see :issue:`475` for the ongoing discussion). .. _copies vs views: Copies vs. views ---------------- Whether array indexing returns a view or a copy of the underlying data depends on the nature of the labels. For positional (integer) indexing, xarray follows the same rules as NumPy: * Positional indexing with only integers and slices returns a view. * Positional indexing with arrays or lists returns a copy. The rules for label based indexing are more complex: * Label-based indexing with only slices returns a view. * Label-based indexing with arrays returns a copy. * Label-based indexing with scalars returns a view or a copy, depending upon if the corresponding positional indexer can be represented as an integer or a slice object. The exact rules are determined by pandas. Whether data is a copy or a view is more predictable in xarray than in pandas, so unlike pandas, xarray does not produce `SettingWithCopy warnings`_. However, you should still avoid assignment with chained indexing. .. _SettingWithCopy warnings: http://pandas.pydata.org/pandas-docs/stable/indexing.html#returning-a-view-versus-a-copy .. _orthogonal: Orthogonal (outer) vs. vectorized indexing ------------------------------------------ Indexing with xarray objects has one important difference from indexing numpy arrays: you can only use one-dimensional arrays to index xarray objects, and each indexer is applied "orthogonally" along independent axes, instead of using numpy's broadcasting rules to vectorize indexers. This means you can do indexing like this, which would require slightly more awkward syntax with numpy arrays: .. ipython:: python arr[arr['time.day'] > 1, arr['space'] != 'IL'] This is a much simpler model than numpy's `advanced indexing`__. If you would like to do advanced-style array indexing in xarray, you have several options: * :ref:`pointwise indexing` * :ref:`masking with where` * Index the underlying NumPy array directly using ``.values``, e.g., __ http://docs.scipy.org/doc/numpy/reference/arrays.indexing.html .. ipython:: python arr.values[arr.values > 0.5] .. _align and reindex: Align and reindex ----------------- xarray's ``reindex``, ``reindex_like`` and ``align`` impose a ``DataArray`` or ``Dataset`` onto a new set of coordinates corresponding to dimensions. The original values are subset to the index labels still found in the new labels, and values corresponding to new labels not found in the original object are in-filled with `NaN`. xarray operations that combine multiple objects generally automatically align their arguments to share the same indexes. However, manual alignment can be useful for greater control and for increased performance. To reindex a particular dimension, use :py:meth:`~xarray.DataArray.reindex`: .. ipython:: python arr.reindex(space=['IA', 'CA']) The :py:meth:`~xarray.DataArray.reindex_like` method is a useful shortcut. To demonstrate, we will make a subset DataArray with new values: .. ipython:: python foo = arr.rename('foo') baz = (10 * arr[:2, :2]).rename('baz') baz Reindexing ``foo`` with ``baz`` selects out the first two values along each dimension: .. ipython:: python foo.reindex_like(baz) The opposite operation asks us to reindex to a larger shape, so we fill in the missing values with `NaN`: .. ipython:: python baz.reindex_like(foo) The :py:func:`~xarray.align` function lets us perform more flexible database-like ``'inner'``, ``'outer'``, ``'left'`` and ``'right'`` joins: .. ipython:: python xr.align(foo, baz, join='inner') xr.align(foo, baz, join='outer') Both ``reindex_like`` and ``align`` work interchangeably between :py:class:`~xarray.DataArray` and :py:class:`~xarray.Dataset` objects, and with any number of matching dimension names: .. ipython:: python ds ds.reindex_like(baz) other = xr.DataArray(['a', 'b', 'c'], dims='other') # this is a no-op, because there are no shared dimension names ds.reindex_like(other) .. _indexing.missing_coordinates: Missing coordinate labels ------------------------- Coordinate labels for each dimension are optional (as of xarray v0.9). Label based indexing with ``.sel`` and ``.loc`` uses standard positional, integer-based indexing as a fallback for dimensions without a coordinate label: .. ipython:: python array = xr.DataArray([1, 2, 3], dims='x') array.sel(x=[0, -1]) Alignment between xarray objects where one or both do not have coordinate labels succeeds only if all dimensions of the same name have the same length. Otherwise, it raises an informative error: .. ipython:: :verbatim: In [62]: xr.align(array, array[:2]) ValueError: arguments without labels along dimension 'x' cannot be aligned because they have different dimension sizes: {2, 3} Underlying Indexes ------------------ xarray uses the :py:class:`pandas.Index` internally to perform indexing operations. If you need to access the underlying indexes, they are available through the :py:attr:`~xarray.DataArray.indexes` attribute. .. ipython:: python arr arr.indexes arr.indexes['time'] Use :py:meth:`~xarray.DataArray.get_index` to get an index for a dimension, falling back to a default :py:class:`pandas.RangeIndex` if it has no coordinate labels: .. ipython:: python array array.get_index('x') xarray-0.9.1/doc/installing.rst0000644131111500116100000000506513043511243016574 0ustar shoyereng00000000000000.. _installing: Installation ============ Required dependencies --------------------- - Python 2.7, 3.4, 3.5, or 3.6 - `numpy `__ (1.7 or later) - `pandas `__ (0.15.0 or later) Optional dependencies --------------------- For netCDF and IO ~~~~~~~~~~~~~~~~~ - `netCDF4 `__: recommended if you want to use xarray for reading or writing netCDF files - `scipy `__: used as a fallback for reading/writing netCDF3 - `pydap `__: used as a fallback for accessing OPeNDAP - `h5netcdf `__: an alternative library for reading and writing netCDF4 files that does not use the netCDF-C libraries - `pynio `__: for reading GRIB and other geoscience specific file formats For accelerating xarray ~~~~~~~~~~~~~~~~~~~~~~~ - `bottleneck `__: speeds up NaN-skipping and rolling window aggregations by a large factor (1.0 or later) - `cyordereddict `__: speeds up most internal operations with xarray data structures (for python versions < 3.5) For parallel computing ~~~~~~~~~~~~~~~~~~~~~~ - `dask.array `__ (0.9.0 or later): required for :ref:`dask`. For plotting ~~~~~~~~~~~~ - `matplotlib `__: required for :ref:`plotting` - `cartopy `__: recommended for :ref:`plot-maps` - `seaborn `__: for better color palettes Instructions ------------ xarray itself is a pure Python package, but its dependencies are not. The easiest way to get everything installed is to use conda_. To install xarray with its recommended dependencies using the conda command line tool:: $ conda install xarray dask netCDF4 bottleneck .. _conda: http://conda.io/ We recommend using the community maintained `conda-forge `__ channel if you need difficult\-to\-build dependencies such as cartopy or pynio:: $ conda install -c conda-forge xarray cartopy pynio New releases may also appear in conda-forge before being updated in the default channel. If you don't use conda, be sure you have the required dependencies (numpy and pandas) installed first. Then, install xarray with pip:: $ pip install xarray To run the test suite after installing xarray, install `py.test `__ (``pip install pytest``) and run ``py.test --pyargs xarray``. xarray-0.9.1/doc/internals.rst0000644131111500116100000001362513043511243016430 0ustar shoyereng00000000000000.. _internals: xarray Internals ================ .. currentmodule:: xarray xarray builds upon two of the foundational libraries of the scientific Python stack, NumPy and pandas. It is written in pure Python (no C or Cython extensions), which makes it easy to develop and extend. Instead, we push compiled code to :ref:`optional dependencies`. Variable objects ---------------- The core internal data structure in xarray is the :py:class:`~xarray.Variable`, which is used as the basic building block behind xarray's :py:class:`~xarray.Dataset` and :py:class:`~xarray.DataArray` types. A ``Variable`` consists of: - ``dims``: A tuple of dimension names. - ``data``: The N-dimensional array (typically, a NumPy or Dask array) storing the Variable's data. It must have the same number of dimensions as the length of ``dims``. - ``attrs``: An ordered dictionary of metadata associated with this array. By convention, xarray's built-in operations never use this metadata. - ``encoding``: Another ordered dictionary used to store information about how these variable's data is represented on disk. See :ref:`io.encoding` for more details. ``Variable`` has an interface similar to NumPy arrays, but extended to make use of named dimensions. For example, it uses ``dim`` in preference to an ``axis`` argument for methods like ``mean``, and supports :ref:`compute.broadcasting`. However, unlike ``Dataset`` and ``DataArray``, the basic ``Variable`` does not include coordinate labels along each axis. ``Variable`` is public API, but because of its incomplete support for labeled data, it is mostly intended for advanced uses, such as in xarray itself or for writing new backends. You can access the variable objects that correspond to xarray objects via the (readonly) :py:attr:`Dataset.variables ` and :py:attr:`DataArray.variable ` attributes. Extending xarray ---------------- .. ipython:: python :suppress: import numpy as np import pandas as pd import xarray as xr np.random.seed(123456) xarray is designed as a general purpose library, and hence tries to avoid including overly domain specific functionality. But inevitably, the need for more domain specific logic arises. One standard solution to this problem is to subclass Dataset and/or DataArray to add domain specific functionality. However, inheritance is not very robust. It's easy to inadvertently use internal APIs when subclassing, which means that your code may break when xarray upgrades. Furthermore, many builtin methods will only return native xarray objects. The standard advice is to use `composition over inheritance`__, but reimplementing an API as large as xarray's on your own objects can be an onerous task, even if most methods are only forwarding to xarray implementations. __ https://github.com/pydata/xarray/issues/706 If you simply want the ability to call a function with the syntax of a method call, then the builtin :py:meth:`~xarray.DataArray.pipe` method (copied from pandas) may suffice. To resolve this issue for more complex cases, xarray has the :py:func:`~xarray.register_dataset_accessor` and :py:func:`~xarray.register_dataarray_accessor` decorators for adding custom "accessors" on xarray objects. Here's how you might use these decorators to write a custom "geo" accessor implementing a geography specific extension to xarray: .. literalinclude:: examples/_code/accessor_example.py This achieves the same result as if the ``Dataset`` class had a cached property defined that returns an instance of your class: .. code-block:: python class Dataset: ... @property def geo(self) return GeoAccessor(self) However, using the register accessor decorators is preferable to simply adding your own ad-hoc property (i.e., ``Dataset.geo = property(...)``), for several reasons: 1. It ensures that the name of your property does not accidentally conflict with any other attributes or methods (including other accessors). 2. Instances of accessor object will be cached on the xarray object that creates them. This means you can save state on them (e.g., to cache computed properties). 3. Using an accessor provides an implicit namespace for your custom functionality that clearly identifies it as separate from built-in xarray methods. Back in an interactive IPython session, we can use these properties: .. ipython:: python :suppress: exec(open("examples/_code/accessor_example.py").read()) .. ipython:: python ds = xr.Dataset({'longitude': np.linspace(0, 10), 'latitude': np.linspace(0, 20)}) ds.geo.center ds.geo.plot() The intent here is that libraries that extend xarray could add such an accessor to implement subclass specific functionality rather than using actual subclasses or patching in a large number of domain specific methods. For further reading on ways to write new accessors and the philosophy behind the approach, see :issue:`1080`. To help users keep things straight, please `let us know `_ if you plan to write a new accessor for an open source library. In the future, we will maintain a list of accessors and the libraries that implement them on this page. Here are several existing libraries that build functionality upon xarray. They may be useful points of reference for your work: - `xgcm `_: General Circulation Model Postprocessing. Uses subclassing and custom xarray backends. - `PyGDX `_: Python 3 package for accessing data stored in GAMS Data eXchange (GDX) files. Also uses a custom subclass. - `windspharm `_: Spherical harmonic wind analysis in Python. - `eofs `_: EOF analysis in Python. .. TODO: consider adding references to these projects somewhere more prominent .. in the documentation? maybe the FAQ page? xarray-0.9.1/doc/io.rst0000644131111500116100000004360213043511243015036 0ustar shoyereng00000000000000.. _io: Serialization and IO ==================== xarray supports direct serialization and IO to several file formats, from simple :ref:`io.pickle` files to the more flexible :ref:`io.netcdf` format. .. ipython:: python :suppress: import numpy as np import pandas as pd import xarray as xr np.random.seed(123456) .. _io.pickle: Pickle ------ The simplest way to serialize an xarray object is to use Python's built-in pickle module: .. ipython:: python import pickle ds = xr.Dataset({'foo': (('x', 'y'), np.random.rand(4, 5))}, coords={'x': [10, 20, 30, 40], 'y': pd.date_range('2000-01-01', periods=5), 'z': ('x', list('abcd'))}) # use the highest protocol (-1) because it is way faster than the default # text based pickle format pkl = pickle.dumps(ds, protocol=-1) pickle.loads(pkl) Pickle support is important because it doesn't require any external libraries and lets you use xarray objects with Python modules like :py:mod:`multiprocessing`. However, there are two important caveats: 1. To simplify serialization, xarray's support for pickle currently loads all array values into memory before dumping an object. This means it is not suitable for serializing datasets too big to load into memory (e.g., from netCDF or OPeNDAP). 2. Pickle will only work as long as the internal data structure of xarray objects remains unchanged. Because the internal design of xarray is still being refined, we make no guarantees (at this point) that objects pickled with this version of xarray will work in future versions. .. _dictionary io: Dictionary ---------- We can convert a ``Dataset`` (or a ``DataArray``) to a dict using :py:meth:`~xarray.Dataset.to_dict`: .. ipython:: python d = ds.to_dict() d We can create a new xarray object from a dict using :py:meth:`~xarray.Dataset.from_dict`: .. ipython:: python ds_dict = xr.Dataset.from_dict(d) ds_dict Dictionary support allows for flexible use of xarray objects. It doesn't require external libraries and dicts can easily be pickled, or converted to json, or geojson. All the values are converted to lists, so dicts might be quite large. .. _io.netcdf: netCDF ------ The recommended way to store xarray data structures is `netCDF`__, which is a binary file format for self-described datasets that originated in the geosciences. xarray is based on the netCDF data model, so netCDF files on disk directly correspond to :py:class:`~xarray.Dataset` objects. NetCDF is supported on almost all platforms, and parsers exist for the vast majority of scientific programming languages. Recent versions of netCDF are based on the even more widely used HDF5 file-format. __ http://www.unidata.ucar.edu/software/netcdf/ .. tip:: If you aren't familiar with this data format, the `netCDF FAQ`_ is a good place to start. .. _netCDF FAQ: http://www.unidata.ucar.edu/software/netcdf/docs/faq.html#What-Is-netCDF Reading and writing netCDF files with xarray requires scipy or the `netCDF4-Python`__ library to be installed (the later is required to read/write netCDF V4 files and use the compression options described below). __ https://github.com/Unidata/netcdf4-python We can save a Dataset to disk using the :py:attr:`Dataset.to_netcdf ` method: .. ipython:: python ds.to_netcdf('saved_on_disk.nc') By default, the file is saved as netCDF4 (assuming netCDF4-Python is installed). You can control the format and engine used to write the file with the ``format`` and ``engine`` arguments. We can load netCDF files to create a new Dataset using :py:func:`~xarray.open_dataset`: .. ipython:: python ds_disk = xr.open_dataset('saved_on_disk.nc') ds_disk Similarly, a DataArray can be saved to disk using the :py:attr:`DataArray.to_netcdf ` method, and loaded from disk using the :py:func:`~xarray.open_dataarray` function. As netCDF files correspond to :py:class:`~xarray.Dataset` objects, these functions internally convert the ``DataArray`` to a ``Dataset`` before saving, and then convert back when loading, ensuring that the ``DataArray`` that is loaded is always exactly the same as the one that was saved. A dataset can also be loaded or written to a specific group within a netCDF file. To load from a group, pass a ``group`` keyword argument to the ``open_dataset`` function. The group can be specified as a path-like string, e.g., to access subgroup 'bar' within group 'foo' pass '/foo/bar' as the ``group`` argument. When writing multiple groups in one file, pass ``mode='a'`` to ``to_netcdf`` to ensure that each call does not delete the file. Data is always loaded lazily from netCDF files. You can manipulate, slice and subset Dataset and DataArray objects, and no array values are loaded into memory until you try to perform some sort of actual computation. For an example of how these lazy arrays work, see the OPeNDAP section below. It is important to note that when you modify values of a Dataset, even one linked to files on disk, only the in-memory copy you are manipulating in xarray is modified: the original file on disk is never touched. .. tip:: xarray's lazy loading of remote or on-disk datasets is often but not always desirable. Before performing computationally intense operations, it is often a good idea to load a Dataset (or DataArray) entirely into memory by invoking the :py:meth:`~xarray.Dataset.load` method. Datasets have a :py:meth:`~xarray.Dataset.close` method to close the associated netCDF file. However, it's often cleaner to use a ``with`` statement: .. ipython:: python # this automatically closes the dataset after use with xr.open_dataset('saved_on_disk.nc') as ds: print(ds.keys()) Although xarray provides reasonable support for incremental reads of files on disk, it does not support incremental writes, which can be a useful strategy for dealing with datasets too big to fit into memory. Instead, xarray integrates with dask.array (see :ref:`dask`), which provides a fully featured engine for streaming computation. .. _io.encoding: Reading encoded data ~~~~~~~~~~~~~~~~~~~~ NetCDF files follow some conventions for encoding datetime arrays (as numbers with a "units" attribute) and for packing and unpacking data (as described by the "scale_factor" and "add_offset" attributes). If the argument ``decode_cf=True`` (default) is given to ``open_dataset``, xarray will attempt to automatically decode the values in the netCDF objects according to `CF conventions`_. Sometimes this will fail, for example, if a variable has an invalid "units" or "calendar" attribute. For these cases, you can turn this decoding off manually. .. _CF conventions: http://cfconventions.org/ You can view this encoding information (among others) in the :py:attr:`DataArray.encoding ` attribute: .. ipython:: :verbatim: In [1]: ds_disk['y'].encoding Out[1]: {'calendar': u'proleptic_gregorian', 'chunksizes': None, 'complevel': 0, 'contiguous': True, 'dtype': dtype('float64'), 'fletcher32': False, 'least_significant_digit': None, 'shuffle': False, 'source': 'saved_on_disk.nc', 'units': u'days since 2000-01-01 00:00:00', 'zlib': False} Note that all operations that manipulate variables other than indexing will remove encoding information. .. ipython:: python :suppress: ds_disk.close() import os os.remove('saved_on_disk.nc') .. _io.netcdf.writing_encoded: Writing encoded data ~~~~~~~~~~~~~~~~~~~~ Conversely, you can customize how xarray writes netCDF files on disk by providing explicit encodings for each dataset variable. The ``encoding`` argument takes a dictionary with variable names as keys and variable specific encodings as values. These encodings are saved as attributes on the netCDF variables on disk, which allows xarray to faithfully read encoded data back into memory. It is important to note that using encodings is entirely optional: if you do not supply any of these encoding options, xarray will write data to disk using a default encoding, or the options in the ``encoding`` attribute, if set. This works perfectly fine in most cases, but encoding can be useful for additional control, especially for enabling compression. In the file on disk, these encodings as saved as attributes on each variable, which allow xarray and other CF-compliant tools for working with netCDF files to correctly read the data. Scaling and type conversions ............................ These encoding options work on any version of the netCDF file format: - ``dtype``: Any valid NumPy dtype or string convertable to a dtype, e.g., ``'int16'`` or ``'float32'``. This controls the type of the data written on disk. - ``_FillValue``: Values of ``NaN`` in xarray variables are remapped to this value when saved on disk. This is important when converting floating point with missing values to integers on disk, because ``NaN`` is not a valid value for integer dtypes. As a default, variables with float types are attributed a ``_FillValue`` of ``NaN`` in the output file. - ``scale_factor`` and ``add_offset``: Used to convert from encoded data on disk to to the decoded data in memory, according to the formula ``decoded = scale_factor * encoded + add_offset``. These parameters can be fruitfully combined to compress discretized data on disk. For example, to save the variable ``foo`` with a precision of 0.1 in 16-bit integers while converting ``NaN`` to ``-9999``, we would use ``encoding={'foo': {'dtype': 'int16', 'scale_factor': 0.1, '_FillValue': -9999}}``. Compression and decompression with such discretization is extremely fast. Chunk based compression ....................... ``zlib``, ``complevel``, ``fletcher32``, ``continguous`` and ``chunksizes`` can be used for enabling netCDF4/HDF5's chunk based compression, as described in the `documentation for createVariable`_ for netCDF4-Python. This only works for netCDF4 files and thus requires using ``format='netCDF4'`` and either ``engine='netcdf4'`` or ``engine='h5netcdf'``. .. _documentation for createVariable: http://unidata.github.io/netcdf4-python/#netCDF4.Dataset.createVariable Chunk based gzip compression can yield impressive space savings, especially for sparse data, but it comes with significant performance overhead. HDF5 libraries can only read complete chunks back into memory, and maximum decompression speed is in the range of 50-100 MB/s. Worse, HDF5's compression and decompression currently cannot be parallelized with dask. For these reasons, we recommend trying discretization based compression (described above) first. Time units .......... The ``units`` and ``calendar`` attributes control how xarray serializes ``datetime64`` and ``timedelta64`` arrays to datasets on disk as numeric values. The ``units`` encoding should be a string like ``'days since 1900-01-01'`` for ``datetime64`` data or a string like ``'days'`` for ``timedelta64`` data. ``calendar`` should be one of the calendar types supported by netCDF4-python: 'standard', 'gregorian', 'proleptic_gregorian' 'noleap', '365_day', '360_day', 'julian', 'all_leap', '366_day'. By default, xarray uses the 'proleptic_gregorian' calendar and units of the smallest time difference between values, with a reference time of the first time value. OPeNDAP ------- xarray includes support for `OPeNDAP`__ (via the netCDF4 library or Pydap), which lets us access large datasets over HTTP. __ http://www.opendap.org/ For example, we can open a connection to GBs of weather data produced by the `PRISM`__ project, and hosted by `IRI`__ at Columbia: __ http://www.prism.oregonstate.edu/ __ http://iri.columbia.edu/ .. ipython source code for this section we don't use this to avoid hitting the DAP server on every doc build. remote_data = xr.open_dataset( 'http://iridl.ldeo.columbia.edu/SOURCES/.OSU/.PRISM/.monthly/dods', decode_times=False) tmax = remote_data.tmax[:500, ::3, ::3] tmax @savefig opendap-prism-tmax.png tmax[0].plot() .. ipython:: :verbatim: In [3]: remote_data = xr.open_dataset( ...: 'http://iridl.ldeo.columbia.edu/SOURCES/.OSU/.PRISM/.monthly/dods', ...: decode_times=False) In [4]: remote_data Out[4]: Dimensions: (T: 1422, X: 1405, Y: 621) Coordinates: * X (X) float32 -125.0 -124.958 -124.917 -124.875 -124.833 -124.792 -124.75 ... * T (T) float32 -779.5 -778.5 -777.5 -776.5 -775.5 -774.5 -773.5 -772.5 -771.5 ... * Y (Y) float32 49.9167 49.875 49.8333 49.7917 49.75 49.7083 49.6667 49.625 ... Data variables: ppt (T, Y, X) float64 ... tdmean (T, Y, X) float64 ... tmax (T, Y, X) float64 ... tmin (T, Y, X) float64 ... Attributes: Conventions: IRIDL expires: 1375315200 .. TODO: update this example to show off decode_cf? .. note:: Like many real-world datasets, this dataset does not entirely follow `CF conventions`_. Unexpected formats will usually cause xarray's automatic decoding to fail. The way to work around this is to either set ``decode_cf=False`` in ``open_dataset`` to turn off all use of CF conventions, or by only disabling the troublesome parser. In this case, we set ``decode_times=False`` because the time axis here provides the calendar attribute in a format that xarray does not expect (the integer ``360`` instead of a string like ``'360_day'``). We can select and slice this data any number of times, and nothing is loaded over the network until we look at particular values: .. ipython:: :verbatim: In [4]: tmax = remote_data['tmax'][:500, ::3, ::3] In [5]: tmax Out[5]: [48541500 values with dtype=float64] Coordinates: * Y (Y) float32 49.9167 49.7917 49.6667 49.5417 49.4167 49.2917 ... * X (X) float32 -125.0 -124.875 -124.75 -124.625 -124.5 -124.375 ... * T (T) float32 -779.5 -778.5 -777.5 -776.5 -775.5 -774.5 -773.5 ... Attributes: pointwidth: 120 standard_name: air_temperature units: Celsius_scale expires: 1443657600 # the data is downloaded automatically when we make the plot In [6]: tmax[0].plot() .. image:: _static/opendap-prism-tmax.png .. _io.pynio: Formats supported by PyNIO -------------------------- xarray can also read GRIB, HDF4 and other file formats supported by PyNIO_, if PyNIO is installed. To use PyNIO to read such files, supply ``engine='pynio'`` to :py:func:`~xarray.open_dataset`. We recommend installing PyNIO via conda:: conda install -c dbrown pynio .. _PyNIO: https://www.pyngl.ucar.edu/Nio.shtml .. _combining multiple files: Formats supported by Pandas --------------------------- For more options (tabular formats and CSV files in particular), consider exporting your objects to pandas and using its broad range of `IO tools`_. .. _IO tools: http://pandas.pydata.org/pandas-docs/stable/io.html Combining multiple files ------------------------ NetCDF files are often encountered in collections, e.g., with different files corresponding to different model runs. xarray can straightforwardly combine such files into a single Dataset by making use of :py:func:`~xarray.concat`. .. note:: Version 0.5 includes support for manipulating datasets that don't fit into memory with dask_. If you have dask installed, you can open multiple files simultaneously using :py:func:`~xarray.open_mfdataset`:: xr.open_mfdataset('my/files/*.nc') This function automatically concatenates and merges multiple files into a single xarray dataset. It is the recommended way to open multiple files with xarray. For more details, see :ref:`dask.io` and a `blog post`_ by Stephan Hoyer. .. _dask: http://dask.pydata.org .. _blog post: http://stephanhoyer.com/2015/06/11/xray-dask-out-of-core-labeled-arrays/ For example, here's how we could approximate ``MFDataset`` from the netCDF4 library:: from glob import glob import xarray as xr def read_netcdfs(files, dim): # glob expands paths with * to a list of files, like the unix shell paths = sorted(glob(files)) datasets = [xr.open_dataset(p) for p in paths] combined = xr.concat(dataset, dim) return combined combined = read_netcdfs('/all/my/files/*.nc', dim='time') This function will work in many cases, but it's not very robust. First, it never closes files, which means it will fail one you need to load more than a few thousands file. Second, it assumes that you want all the data from each file and that it can all fit into memory. In many situations, you only need a small subset or an aggregated summary of the data from each file. Here's a slightly more sophisticated example of how to remedy these deficiencies:: def read_netcdfs(files, dim, transform_func=None): def process_one_path(path): # use a context manager, to ensure the file gets closed after use with xr.open_dataset(path) as ds: # transform_func should do some sort of selection or # aggregation if transform_func is not None: ds = transform_func(ds) # load all data from the transformed dataset, to ensure we can # use it after closing each original file ds.load() return ds paths = sorted(glob(files)) datasets = [process_one_path(p) for p in paths] combined = xr.concat(datasets, dim) return combined # here we suppose we only care about the combined mean of each file; # you might also use indexing operations like .sel to subset datasets combined = read_netcdfs('/all/my/files/*.nc', dim='time', transform_func=lambda ds: ds.mean()) This pattern works well and is very robust. 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For example, for plotting labeled data, we highly recommend using the visualization `built in to pandas itself`__ or provided by the pandas aware libraries such as `Seaborn`__. __ http://pandas.pydata.org/pandas-docs/stable/visualization.html __ http://stanford.edu/~mwaskom/software/seaborn/ .. ipython:: python :suppress: import numpy as np import pandas as pd import xarray as xr np.random.seed(123456) Hierarchical and tidy data ~~~~~~~~~~~~~~~~~~~~~~~~~~ Tabular data is easiest to work with when it meets the criteria for `tidy data`__: * Each column holds a different variable. * Each rows holds a different observation. __ http://www.jstatsoft.org/v59/i10/ In this "tidy data" format, we can represent any :py:class:`~xarray.Dataset` and :py:class:`~xarray.DataArray` in terms of :py:class:`pandas.DataFrame` and :py:class:`pandas.Series`, respectively (and vice-versa). The representation works by flattening non-coordinates to 1D, and turning the tensor product of coordinate indexes into a :py:class:`pandas.MultiIndex`. Dataset and DataFrame --------------------- To convert any dataset to a ``DataFrame`` in tidy form, use the :py:meth:`Dataset.to_dataframe() ` method: .. ipython:: python ds = xr.Dataset({'foo': (('x', 'y'), np.random.randn(2, 3))}, coords={'x': [10, 20], 'y': ['a', 'b', 'c'], 'along_x': ('x', np.random.randn(2)), 'scalar': 123}) ds df = ds.to_dataframe() df We see that each variable and coordinate in the Dataset is now a column in the DataFrame, with the exception of indexes which are in the index. To convert the ``DataFrame`` to any other convenient representation, use ``DataFrame`` methods like :py:meth:`~pandas.DataFrame.reset_index`, :py:meth:`~pandas.DataFrame.stack` and :py:meth:`~pandas.DataFrame.unstack`. To create a ``Dataset`` from a ``DataFrame``, use the :py:meth:`~xarray.Dataset.from_dataframe` class method or the equivalent :py:meth:`pandas.DataFrame.to_xarray ` method (pandas v0.18 or later): .. ipython:: python xr.Dataset.from_dataframe(df) Notice that that dimensions of variables in the ``Dataset`` have now expanded after the round-trip conversion to a ``DataFrame``. This is because every object in a ``DataFrame`` must have the same indices, so we need to broadcast the data of each array to the full size of the new ``MultiIndex``. Likewise, all the coordinates (other than indexes) ended up as variables, because pandas does not distinguish non-index coordinates. DataArray and Series -------------------- ``DataArray`` objects have a complementary representation in terms of a :py:class:`pandas.Series`. Using a Series preserves the ``Dataset`` to ``DataArray`` relationship, because ``DataFrames`` are dict-like containers of ``Series``. The methods are very similar to those for working with DataFrames: .. ipython:: python s = ds['foo'].to_series() s # or equivalently, with Series.to_xarray() xr.DataArray.from_series(s) Both the ``from_series`` and ``from_dataframe`` methods use reindexing, so they work even if not the hierarchical index is not a full tensor product: .. ipython:: python s[::2] s[::2].to_xarray() Multi-dimensional data ~~~~~~~~~~~~~~~~~~~~~~ Tidy data is great, but it sometimes you want to preserve dimensions instead of automatically stacking them into a ``MultiIndex``. :py:meth:`DataArray.to_pandas() ` is a shortcut that lets you convert a DataArray directly into a pandas object with the same dimensionality (i.e., a 1D array is converted to a :py:class:`~pandas.Series`, 2D to :py:class:`~pandas.DataFrame` and 3D to :py:class:`~pandas.Panel`): .. ipython:: python arr = xr.DataArray(np.random.randn(2, 3), coords=[('x', [10, 20]), ('y', ['a', 'b', 'c'])]) df = arr.to_pandas() df To perform the inverse operation of converting any pandas objects into a data array with the same shape, simply use the :py:class:`~xarray.DataArray` constructor: .. ipython:: python xr.DataArray(df) Both the ``DataArray`` and ``Dataset`` constructors directly convert pandas objects into xarray objects with the same shape. This means that they preserve all use of multi-indexes: .. ipython:: python index = pd.MultiIndex.from_arrays([['a', 'a', 'b'], [0, 1, 2]], names=['one', 'two']) df = pd.DataFrame({'x': 1, 'y': 2}, index=index) ds = xr.Dataset(df) ds However, you will need to set dimension names explicitly, either with the ``dims`` argument on in the ``DataArray`` constructor or by calling :py:class:`~xarray.Dataset.rename` on the new object. .. _panel transition: Transitioning from pandas.Panel to xarray ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ :py:class:`~pandas.Panel`, pandas's data structure for 3D arrays, has always been a second class data structure compared to the Series and DataFrame. To allow pandas developers to focus more on its core functionality built around the DataFrame, pandas plans to eventually deprecate Panel. xarray has most of ``Panel``'s features, a more explicit API (particularly around indexing), and the ability to scale to >3 dimensions with the same interface. As discussed :ref:`elsewhere ` in the docs, there are two primary data structures in xarray: ``DataArray`` and ``Dataset``. You can imagine a ``DataArray`` as a n-dimensional pandas ``Series`` (i.e. a single typed array), and a ``Dataset`` as the ``DataFrame`` equivalent (i.e. a dict of aligned ``DataArray`` objects). So you can represent a Panel, in two ways: - As a 3-dimensional ``DataArray``, - Or as a ``Dataset`` containing a number of 2-dimensional DataArray objects. Let's take a look: .. ipython:: python panel = pd.Panel(np.random.rand(2, 3, 4), items=list('ab'), major_axis=list('mno'), minor_axis=pd.date_range(start='2000', periods=4, name='date')) panel As a DataArray: .. ipython:: python # or equivalently, with Panel.to_xarray() xr.DataArray(panel) As you can see, there are three dimensions (each is also a coordinate). Two of the axes of the panel were unnamed, so have been assigned ``dim_0`` and ``dim_1`` respectively, while the third retains its name ``date``. As a Dataset: .. ipython:: python xr.Dataset(panel) Here, there are two data variables, each representing a DataFrame on panel's ``items`` axis, and labelled as such. Each variable is a 2D array of the respective values along the ``items`` dimension. While the xarray docs are relatively complete, a few items stand out for Panel users: - A DataArray's data is stored as a numpy array, and so can only contain a single type. As a result, a Panel that contains :py:class:`~pandas.DataFrame` objects with multiple types will be converted to ``dtype=object``. A ``Dataset`` of multiple ``DataArray`` objects each with its own dtype will allow original types to be preserved. - :ref:`Indexing ` is similar to pandas, but more explicit and leverages xarray's naming of dimensions. - Because of those features, making much higher dimensional data is very practical. - Variables in ``Dataset`` objects can use a subset of its dimensions. For example, you can have one dataset with Person x Score x Time, and another with Person x Score. - You can use coordinates are used for both dimensions and for variables which _label_ the data variables, so you could have a coordinate Age, that labelled the Person dimension of a Dataset of Person x Score x Time. While xarray may take some getting used to, it's worth it! If anything is unclear, please post an issue on `GitHub `__ or `StackOverflow `__, and we'll endeavor to respond to the specific case or improve the general docs. xarray-0.9.1/doc/plotting.rst0000644131111500116100000004473513043511243016277 0ustar shoyereng00000000000000.. _plotting: Plotting ======== Introduction ------------ Labeled data enables expressive computations. These same labels can also be used to easily create informative plots. xarray's plotting capabilities are centered around :py:class:`xarray.DataArray` objects. To plot :py:class:`xarray.Dataset` objects simply access the relevant DataArrays, ie ``dset['var1']``. Here we focus mostly on arrays 2d or larger. If your data fits nicely into a pandas DataFrame then you're better off using one of the more developed tools there. xarray plotting functionality is a thin wrapper around the popular `matplotlib `_ library. Matplotlib syntax and function names were copied as much as possible, which makes for an easy transition between the two. Matplotlib must be installed before xarray can plot. For more extensive plotting applications consider the following projects: - `Seaborn `_: "provides a high-level interface for drawing attractive statistical graphics." Integrates well with pandas. - `HoloViews `_ and `GeoViews `_: "Composable, declarative data structures for building even complex visualizations easily." Includes native support for xarray objects. - `Cartopy `_: Provides cartographic tools. Imports ~~~~~~~ .. ipython:: python :suppress: # Use defaults so we don't get gridlines in generated docs import matplotlib as mpl mpl.rcdefaults() The following imports are necessary for all of the examples. .. ipython:: python import numpy as np import pandas as pd import matplotlib.pyplot as plt import xarray as xr For these examples we'll use the North American air temperature dataset. .. ipython:: python airtemps = xr.tutorial.load_dataset('air_temperature') airtemps # Convert to celsius air = airtemps.air - 273.15 One Dimension ------------- Simple Example ~~~~~~~~~~~~~~ xarray uses the coordinate name to label the x axis. .. ipython:: python air1d = air.isel(lat=10, lon=10) @savefig plotting_1d_simple.png width=4in air1d.plot() Additional Arguments ~~~~~~~~~~~~~~~~~~~~~ Additional arguments are passed directly to the matplotlib function which does the work. For example, :py:func:`xarray.plot.line` calls matplotlib.pyplot.plot_ passing in the index and the array values as x and y, respectively. So to make a line plot with blue triangles a matplotlib format string can be used: .. _matplotlib.pyplot.plot: http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.plot .. ipython:: python @savefig plotting_1d_additional_args.png width=4in air1d[:200].plot.line('b-^') .. note:: Not all xarray plotting methods support passing positional arguments to the wrapped matplotlib functions, but they do all support keyword arguments. Keyword arguments work the same way, and are more explicit. .. ipython:: python @savefig plotting_example_sin3.png width=4in air1d[:200].plot.line(color='purple', marker='o') Adding to Existing Axis ~~~~~~~~~~~~~~~~~~~~~~~ To add the plot to an existing axis pass in the axis as a keyword argument ``ax``. This works for all xarray plotting methods. In this example ``axes`` is an array consisting of the left and right axes created by ``plt.subplots``. .. ipython:: python fig, axes = plt.subplots(ncols=2) axes air1d.plot(ax=axes[0]) air1d.plot.hist(ax=axes[1]) plt.tight_layout() @savefig plotting_example_existing_axes.png width=6in plt.show() On the right is a histogram created by :py:func:`xarray.plot.hist`. .. _plotting.figsize: Controlling the figure size ~~~~~~~~~~~~~~~~~~~~~~~~~~~ You can pass a ``figsize`` argument to all xarray's plotting methods to control the figure size. For convenience, xarray's plotting methods also support the ``aspect`` and ``size`` arguments which control the size of the resulting image via the formula ``figsize = (aspect * size, size)``: .. ipython:: python air1d.plot(aspect=2, size=3) @savefig plotting_example_size_and_aspect.png plt.tight_layout() .. ipython:: python :suppress: # create a dummy figure so sphinx plots everything below normally plt.figure() This feature also works with :ref:`plotting.faceting`. For facet plots, ``size`` and ``aspect`` refer to a single panel (so that ``aspect * size`` gives the width of each facet in inches), while ``figsize`` refers to the entire figure (as for matplotlib's ``figsize`` argument). .. note:: If ``figsize`` or ``size`` are used, a new figure is created, so this is mutually exclusive with the ``ax`` argument. .. note:: The convention used by xarray (``figsize = (aspect * size, size)``) is borrowed from seaborn: it is therefore `not equivalent to matplotlib's`_. .. _not equivalent to matplotlib's: https://github.com/mwaskom/seaborn/issues/746 Two Dimensions -------------- Simple Example ~~~~~~~~~~~~~~ The default method :py:meth:`xarray.DataArray.plot` sees that the data is 2 dimensional and calls :py:func:`xarray.plot.pcolormesh`. .. ipython:: python air2d = air.isel(time=500) @savefig 2d_simple.png width=4in air2d.plot() All 2d plots in xarray allow the use of the keyword arguments ``yincrease`` and ``xincrease``. .. ipython:: python @savefig 2d_simple_yincrease.png width=4in air2d.plot(yincrease=False) .. note:: We use :py:func:`xarray.plot.pcolormesh` as the default two-dimensional plot method because it is more flexible than :py:func:`xarray.plot.imshow`. However, for large arrays, ``imshow`` can be much faster than ``pcolormesh``. If speed is important to you and you are plotting a regular mesh, consider using ``imshow``. Missing Values ~~~~~~~~~~~~~~ xarray plots data with :ref:`missing_values`. .. ipython:: python bad_air2d = air2d.copy() bad_air2d[dict(lat=slice(0, 10), lon=slice(0, 25))] = np.nan @savefig plotting_missing_values.png width=4in bad_air2d.plot() Nonuniform Coordinates ~~~~~~~~~~~~~~~~~~~~~~ It's not necessary for the coordinates to be evenly spaced. Both :py:func:`xarray.plot.pcolormesh` (default) and :py:func:`xarray.plot.contourf` can produce plots with nonuniform coordinates. .. ipython:: python b = air2d.copy() # Apply a nonlinear transformation to one of the coords b.coords['lat'] = np.log(b.coords['lat']) @savefig plotting_nonuniform_coords.png width=4in b.plot() Calling Matplotlib ~~~~~~~~~~~~~~~~~~ Since this is a thin wrapper around matplotlib, all the functionality of matplotlib is available. .. ipython:: python air2d.plot(cmap=plt.cm.Blues) plt.title('These colors prove North America\nhas fallen in the ocean') plt.ylabel('latitude') plt.xlabel('longitude') plt.tight_layout() @savefig plotting_2d_call_matplotlib.png width=4in plt.show() .. note:: xarray methods update label information and generally play around with the axes. So any kind of updates to the plot should be done *after* the call to the xarray's plot. In the example below, ``plt.xlabel`` effectively does nothing, since ``d_ylog.plot()`` updates the xlabel. .. ipython:: python plt.xlabel('Never gonna see this.') air2d.plot() @savefig plotting_2d_call_matplotlib2.png width=4in plt.show() Colormaps ~~~~~~~~~ xarray borrows logic from Seaborn to infer what kind of color map to use. For example, consider the original data in Kelvins rather than Celsius: .. ipython:: python @savefig plotting_kelvin.png width=4in airtemps.air.isel(time=0).plot() The Celsius data contain 0, so a diverging color map was used. The Kelvins do not have 0, so the default color map was used. Robust ~~~~~~ Outliers often have an extreme effect on the output of the plot. Here we add two bad data points. This affects the color scale, washing out the plot. .. ipython:: python air_outliers = airtemps.air.isel(time=0).copy() air_outliers[0, 0] = 100 air_outliers[-1, -1] = 400 @savefig plotting_robust1.png width=4in air_outliers.plot() This plot shows that we have outliers. The easy way to visualize the data without the outliers is to pass the parameter ``robust=True``. This will use the 2nd and 98th percentiles of the data to compute the color limits. .. ipython:: python @savefig plotting_robust2.png width=4in air_outliers.plot(robust=True) Observe that the ranges of the color bar have changed. The arrows on the color bar indicate that the colors include data points outside the bounds. Discrete Colormaps ~~~~~~~~~~~~~~~~~~ It is often useful, when visualizing 2d data, to use a discrete colormap, rather than the default continuous colormaps that matplotlib uses. The ``levels`` keyword argument can be used to generate plots with discrete colormaps. For example, to make a plot with 8 discrete color intervals: .. ipython:: python @savefig plotting_discrete_levels.png width=4in air2d.plot(levels=8) It is also possible to use a list of levels to specify the boundaries of the discrete colormap: .. ipython:: python @savefig plotting_listed_levels.png width=4in air2d.plot(levels=[0, 12, 18, 30]) You can also specify a list of discrete colors through the ``colors`` argument: .. ipython:: python flatui = ["#9b59b6", "#3498db", "#95a5a6", "#e74c3c", "#34495e", "#2ecc71"] @savefig plotting_custom_colors_levels.png width=4in air2d.plot(levels=[0, 12, 18, 30], colors=flatui) Finally, if you have `Seaborn `_ installed, you can also specify a seaborn color palette to the ``cmap`` argument. Note that ``levels`` *must* be specified with seaborn color palettes if using ``imshow`` or ``pcolormesh`` (but not with ``contour`` or ``contourf``, since levels are chosen automatically). .. ipython:: python @savefig plotting_seaborn_palette.png width=4in air2d.plot(levels=10, cmap='husl') .. _plotting.faceting: Faceting -------- Faceting here refers to splitting an array along one or two dimensions and plotting each group. xarray's basic plotting is useful for plotting two dimensional arrays. What about three or four dimensional arrays? That's where facets become helpful. Consider the temperature data set. There are 4 observations per day for two years which makes for 2920 values along the time dimension. One way to visualize this data is to make a seperate plot for each time period. The faceted dimension should not have too many values; faceting on the time dimension will produce 2920 plots. That's too much to be helpful. To handle this situation try performing an operation that reduces the size of the data in some way. For example, we could compute the average air temperature for each month and reduce the size of this dimension from 2920 -> 12. A simpler way is to just take a slice on that dimension. So let's use a slice to pick 6 times throughout the first year. .. ipython:: python t = air.isel(time=slice(0, 365 * 4, 250)) t.coords Simple Example ~~~~~~~~~~~~~~ The easiest way to create faceted plots is to pass in ``row`` or ``col`` arguments to the xarray plotting methods/functions. This returns a :py:class:`xarray.plot.FacetGrid` object. .. ipython:: python @savefig plot_facet_dataarray.png height=12in g_simple = t.plot(x='lon', y='lat', col='time', col_wrap=3) 4 dimensional ~~~~~~~~~~~~~ For 4 dimensional arrays we can use the rows and columns of the grids. Here we create a 4 dimensional array by taking the original data and adding a fixed amount. Now we can see how the temperature maps would compare if one were much hotter. .. ipython:: python t2 = t.isel(time=slice(0, 2)) t4d = xr.concat([t2, t2 + 40], pd.Index(['normal', 'hot'], name='fourth_dim')) # This is a 4d array t4d.coords @savefig plot_facet_4d.png height=12in t4d.plot(x='lon', y='lat', col='time', row='fourth_dim') Other features ~~~~~~~~~~~~~~ Faceted plotting supports other arguments common to xarray 2d plots. .. ipython:: python hasoutliers = t.isel(time=slice(0, 5)).copy() hasoutliers[0, 0, 0] = -100 hasoutliers[-1, -1, -1] = 400 @savefig plot_facet_robust.png height=12in g = hasoutliers.plot.pcolormesh('lon', 'lat', col='time', col_wrap=3, robust=True, cmap='viridis') FacetGrid Objects ~~~~~~~~~~~~~~~~~ :py:class:`xarray.plot.FacetGrid` is used to control the behavior of the multiple plots. It borrows an API and code from `Seaborn's FacetGrid `_. The structure is contained within the ``axes`` and ``name_dicts`` attributes, both 2d Numpy object arrays. .. ipython:: python g.axes g.name_dicts It's possible to select the :py:class:`xarray.DataArray` or :py:class:`xarray.Dataset` corresponding to the FacetGrid through the ``name_dicts``. .. ipython:: python g.data.loc[g.name_dicts[0, 0]] Here is an example of using the lower level API and then modifying the axes after they have been plotted. .. ipython:: python g = t.plot.imshow('lon', 'lat', col='time', col_wrap=3, robust=True) for i, ax in enumerate(g.axes.flat): ax.set_title('Air Temperature %d' % i) bottomright = g.axes[-1, -1] bottomright.annotate('bottom right', (240, 40)) @savefig plot_facet_iterator.png height=12in plt.show() TODO: add an example of using the ``map`` method to plot dataset variables (e.g., with ``plt.quiver``). .. _plot-maps: Maps ---- To follow this section you'll need to have Cartopy installed and working. This script will plot the air temperature on a map. .. ipython:: python import cartopy.crs as ccrs air = xr.tutorial.load_dataset('air_temperature').air ax = plt.axes(projection=ccrs.Orthographic(-80, 35)) air.isel(time=0).plot.contourf(ax=ax, transform=ccrs.PlateCarree()); @savefig plotting_maps_cartopy.png width=100% ax.set_global(); ax.coastlines(); When faceting on maps, the projection can be transferred to the ``plot`` function using the ``subplot_kws`` keyword. The axes for the subplots created by faceting are accessible in the object returned by ``plot``: .. ipython:: python p = air.isel(time=[0, 4]).plot(transform=ccrs.PlateCarree(), col='time', subplot_kws={'projection': ccrs.Orthographic(-80, 35)}) for ax in p.axes.flat: ax.coastlines() ax.gridlines() @savefig plotting_maps_cartopy_facetting.png width=100% plt.show(); Details ------- Ways to Use ~~~~~~~~~~~ There are three ways to use the xarray plotting functionality: 1. Use ``plot`` as a convenience method for a DataArray. 2. Access a specific plotting method from the ``plot`` attribute of a DataArray. 3. Directly from the xarray plot submodule. These are provided for user convenience; they all call the same code. .. ipython:: python import xarray.plot as xplt da = xr.DataArray(range(5)) fig, axes = plt.subplots(ncols=2, nrows=2) da.plot(ax=axes[0, 0]) da.plot.line(ax=axes[0, 1]) xplt.plot(da, ax=axes[1, 0]) xplt.line(da, ax=axes[1, 1]) plt.tight_layout() @savefig plotting_ways_to_use.png width=6in plt.show() Here the output is the same. Since the data is 1 dimensional the line plot was used. The convenience method :py:meth:`xarray.DataArray.plot` dispatches to an appropriate plotting function based on the dimensions of the ``DataArray`` and whether the coordinates are sorted and uniformly spaced. This table describes what gets plotted: =============== =========================== Dimensions Plotting function --------------- --------------------------- 1 :py:func:`xarray.plot.line` 2 :py:func:`xarray.plot.pcolormesh` Anything else :py:func:`xarray.plot.hist` =============== =========================== Coordinates ~~~~~~~~~~~ If you'd like to find out what's really going on in the coordinate system, read on. .. ipython:: python a0 = xr.DataArray(np.zeros((4, 3, 2)), dims=('y', 'x', 'z'), name='temperature') a0[0, 0, 0] = 1 a = a0.isel(z=0) a The plot will produce an image corresponding to the values of the array. Hence the top left pixel will be a different color than the others. Before reading on, you may want to look at the coordinates and think carefully about what the limits, labels, and orientation for each of the axes should be. .. ipython:: python @savefig plotting_example_2d_simple.png width=4in a.plot() It may seem strange that the values on the y axis are decreasing with -0.5 on the top. This is because the pixels are centered over their coordinates, and the axis labels and ranges correspond to the values of the coordinates. Multidimensional coordinates ~~~~~~~~~~~~~~~~~~~~~~~~~~~~ See also: :ref:`examples.multidim`. You can plot irregular grids defined by multidimensional coordinates with xarray, but you'll have to tell the plot function to use these coordinates instead of the default ones: .. ipython:: python lon, lat = np.meshgrid(np.linspace(-20, 20, 5), np.linspace(0, 30, 4)) lon += lat/10 lat += lon/10 da = xr.DataArray(np.arange(20).reshape(4, 5), dims=['y', 'x'], coords = {'lat': (('y', 'x'), lat), 'lon': (('y', 'x'), lon)}) @savefig plotting_example_2d_irreg.png width=4in da.plot.pcolormesh('lon', 'lat'); Note that in this case, xarray still follows the pixel centered convention. This might be undesirable in some cases, for example when your data is defined on a polar projection (:issue:`781`). This is why the default is to not follow this convention when plotting on a map: .. ipython:: python import cartopy.crs as ccrs ax = plt.subplot(projection=ccrs.PlateCarree()); da.plot.pcolormesh('lon', 'lat', ax=ax); ax.scatter(lon, lat, transform=ccrs.PlateCarree()); @savefig plotting_example_2d_irreg_map.png width=4in ax.coastlines(); ax.gridlines(draw_labels=True); You can however decide to infer the cell boundaries and use the ``infer_intervals`` keyword: .. ipython:: python ax = plt.subplot(projection=ccrs.PlateCarree()); da.plot.pcolormesh('lon', 'lat', ax=ax, infer_intervals=True); ax.scatter(lon, lat, transform=ccrs.PlateCarree()); @savefig plotting_example_2d_irreg_map_infer.png width=4in ax.coastlines(); ax.gridlines(draw_labels=True); .. note:: The data model of xarray does not support datasets with `cell boundaries`_ yet. If you want to use these coordinates, you'll have to make the plots outside the xarray framework. .. _cell boundaries: http://cfconventions.org/cf-conventions/v1.6.0/cf-conventions.html#cell-boundaries xarray-0.9.1/doc/reshaping.rst0000644131111500116100000001167013043511243016407 0ustar shoyereng00000000000000.. _reshape: ############################### Reshaping and reorganizing data ############################### These methods allow you to reorganize .. ipython:: python :suppress: import numpy as np import pandas as pd import xarray as xr np.random.seed(123456) Reordering dimensions --------------------- To reorder dimensions on a :py:class:`~xarray.DataArray` or across all variables on a :py:class:`~xarray.Dataset`, use :py:meth:`~xarray.DataArray.transpose` or the ``.T`` property: .. ipython:: python ds = xr.Dataset({'foo': (('x', 'y', 'z'), [[[42]]]), 'bar': (('y', 'z'), [[24]])}) ds.transpose('y', 'z', 'x') ds.T Converting between datasets and arrays -------------------------------------- To convert from a Dataset to a DataArray, use :py:meth:`~xarray.Dataset.to_array`: .. ipython:: python arr = ds.to_array() arr This method broadcasts all data variables in the dataset against each other, then concatenates them along a new dimension into a new array while preserving coordinates. To convert back from a DataArray to a Dataset, use :py:meth:`~xarray.DataArray.to_dataset`: .. ipython:: python arr.to_dataset(dim='variable') The broadcasting behavior of ``to_array`` means that the resulting array includes the union of data variable dimensions: .. ipython:: python ds2 = xr.Dataset({'a': 0, 'b': ('x', [3, 4, 5])}) # the input dataset has 4 elements ds2 # the resulting array has 6 elements ds2.to_array() Otherwise, the result could not be represented as an orthogonal array. If you use ``to_dataset`` without supplying the ``dim`` argument, the DataArray will be converted into a Dataset of one variable: .. ipython:: python arr.to_dataset(name='combined') .. _reshape.stack: Stack and unstack ----------------- As part of xarray's nascent support for :py:class:`pandas.MultiIndex`, we have implemented :py:meth:`~xarray.DataArray.stack` and :py:meth:`~xarray.DataArray.unstack` method, for combining or splitting dimensions: .. ipython:: python array = xr.DataArray(np.random.randn(2, 3), coords=[('x', ['a', 'b']), ('y', [0, 1, 2])]) stacked = array.stack(z=('x', 'y')) stacked stacked.unstack('z') These methods are modeled on the :py:class:`pandas.DataFrame` methods of the same name, although in xarray they always create new dimensions rather than adding to the existing index or columns. Like :py:meth:`DataFrame.unstack`, xarray's ``unstack`` always succeeds, even if the multi-index being unstacked does not contain all possible levels. Missing levels are filled in with ``NaN`` in the resulting object: .. ipython:: python stacked2 = stacked[::2] stacked2 stacked2.unstack('z') However, xarray's ``stack`` has an important difference from pandas: unlike pandas, it does not automatically drop missing values. Compare: .. ipython:: python array = xr.DataArray([[np.nan, 1], [2, 3]], dims=['x', 'y']) array.stack(z=('x', 'y')) array.to_pandas().stack() We departed from pandas's behavior here because predictable shapes for new array dimensions is necessary for :ref:`dask`. .. _reshape.set_index: Set and reset index ------------------- Complementary to stack / unstack, xarray's ``.set_index``, ``.reset_index`` and ``.reorder_levels`` allow easy manipulation of ``DataArray`` or ``Dataset`` multi-indexes without modifying the data and its dimensions. You can create a multi-index from several 1-dimensional variables and/or coordinates using :py:meth:`~xarray.DataArray.set_index`: .. ipython:: python da = xr.DataArray(np.random.rand(4), coords={'band': ('x', ['a', 'a', 'b', 'b']), 'wavenumber': ('x', np.linspace(200, 400, 4))}, dims='x') da mda = da.set_index(x=['band', 'wavenumber']) mda These coordinates can now be used for indexing, e.g., .. ipython:: python mda.sel(band='a') Conversely, you can use :py:meth:`~xarray.DataArray.reset_index` to extract multi-index levels as coordinates (this is mainly useful for serialization): .. ipython:: python mda.reset_index('x') :py:meth:`~xarray.DataArray.reorder_levels` allows changing the order of multi-index levels: .. ipython:: python mda.reorder_levels(x=['wavenumber', 'band']) As of xarray v0.9 coordinate labels for each dimension are optional. You can also use ``.set_index`` / ``.reset_index`` to add / remove labels for one or several dimensions: .. ipython:: python array = xr.DataArray([1, 2, 3], dims='x') array array['c'] = ('x', ['a', 'b', 'c']) array.set_index(x='c') array.set_index(x='c', inplace=True) array.reset_index('x', drop=True) Shift and roll -------------- To adjust coordinate labels, you can use the :py:meth:`~xarray.Dataset.shift` and :py:meth:`~xarray.Dataset.roll` methods: .. ipython:: python array = xr.DataArray([1, 2, 3, 4], dims='x') array.shift(x=2) array.roll(x=2) xarray-0.9.1/doc/time-series.rst0000644131111500116100000001112712652753364016673 0ustar shoyereng00000000000000.. _time-series: ================ Time series data ================ A major use case for xarray is multi-dimensional time-series data. Accordingly, we've copied many of features that make working with time-series data in pandas such a joy to xarray. In most cases, we rely on pandas for the core functionality. .. ipython:: python :suppress: import numpy as np import pandas as pd import xarray as xr np.random.seed(123456) Creating datetime64 data ------------------------ xarray uses the numpy dtypes ``datetime64[ns]`` and ``timedelta64[ns]`` to represent datetime data, which offer vectorized (if sometimes buggy) operations with numpy and smooth integration with pandas. To convert to or create regular arrays of ``datetime64`` data, we recommend using :py:func:`pandas.to_datetime` and :py:func:`pandas.date_range`: .. ipython:: python pd.to_datetime(['2000-01-01', '2000-02-02']) pd.date_range('2000-01-01', periods=365) Alternatively, you can supply arrays of Python ``datetime`` objects. These get converted automatically when used as arguments in xarray objects: .. ipython:: python import datetime xr.Dataset({'time': datetime.datetime(2000, 1, 1)}) When reading or writing netCDF files, xarray automatically decodes datetime and timedelta arrays using `CF conventions`_ (that is, by using a ``units`` attribute like ``'days since 2000-01-01'``). .. _CF conventions: http://cfconventions.org You can manual decode arrays in this form by passing a dataset to :py:func:`~xarray.decode_cf`: .. ipython:: python attrs = {'units': 'hours since 2000-01-01'} ds = xr.Dataset({'time': ('time', [0, 1, 2, 3], attrs)}) xr.decode_cf(ds) One unfortunate limitation of using ``datetime64[ns]`` is that it limits the native representation of dates to those that fall between the years 1678 and 2262. When a netCDF file contains dates outside of these bounds, dates will be returned as arrays of ``netcdftime.datetime`` objects. Datetime indexing ----------------- xarray borrows powerful indexing machinery from pandas (see :ref:`indexing`). This allows for several useful and suscinct forms of indexing, particularly for `datetime64` data. For example, we support indexing with strings for single items and with the `slice` object: .. ipython:: python time = pd.date_range('2000-01-01', freq='H', periods=365 * 24) ds = xr.Dataset({'foo': ('time', np.arange(365 * 24)), 'time': time}) ds.sel(time='2000-01') ds.sel(time=slice('2000-06-01', '2000-06-10')) You can also select a particular time by indexing with a :py:class:`datetime.time` object: .. ipython:: python ds.sel(time=datetime.time(12)) For more details, read the pandas documentation. Datetime components ------------------- xarray supports a notion of "virtual" or "derived" coordinates for `datetime components`__ implemented by pandas, including "year", "month", "day", "hour", "minute", "second", "dayofyear", "week", "dayofweek", "weekday" and "quarter": __ http://pandas.pydata.org/pandas-docs/stable/api.html#time-date-components .. ipython:: python ds['time.month'] ds['time.dayofyear'] xarray adds ``'season'`` to the list of datetime components supported by pandas: .. ipython:: python ds['time.season'] The set of valid seasons consists of 'DJF', 'MAM', 'JJA' and 'SON', labeled by the first letters of the corresponding months. You can use these shortcuts with both Datasets and DataArray coordinates. Resampling and grouped operations --------------------------------- Datetime components couple particularly well with grouped operations (see :ref:`groupby`) for analyzing features that repeat over time. Here's how to calculate the mean by time of day: .. ipython:: python ds.groupby('time.hour').mean() For upsampling or downsampling temporal resolutions, xarray offers a :py:meth:`~xarray.Dataset.resample` method building on the core functionality offered by the pandas method of the same name. Resample uses essentialy the same api as ``resample`` `in pandas`_. .. _in pandas: http://pandas.pydata.org/pandas-docs/stable/timeseries.html#up-and-downsampling For example, we can downsample our dataset from hourly to 6-hourly: .. ipython:: python ds.resample('6H', dim='time', how='mean') Resample also works for upsampling, in which case intervals without any values are marked by ``NaN``: .. ipython:: python ds.resample('30Min', 'time') Of course, all of these resampling and groupby operation work on both Dataset and DataArray objects with any number of additional dimensions. For more examples of using grouped operations on a time dimension, see :ref:`toy weather data`. xarray-0.9.1/doc/whats-new.rst0000644131111500116100000020564113043675344016364 0ustar shoyereng00000000000000.. currentmodule:: xarray What's New ========== .. ipython:: python :suppress: import numpy as np import pandas as pd import xarray as xray import xarray import xarray as xr np.random.seed(123456) .. _whats-new.0.9.1: v0.9.1 (30 January 2017) ------------------------ Renamed the "Unindexed dimensions" section in the ``Dataset`` and ``DataArray`` repr (added in v0.9.0) to "Dimensions without coordinates" (:issue:`1199`). .. _whats-new.0.9.0: v0.9.0 (25 January 2017) ------------------------ This major release includes five months worth of enhancements and bug fixes from 24 contributors, including some significant changes that are not fully backwards compatible. Highlights include: - Coordinates are now *optional* in the xarray data model, even for dimensions. - Changes to caching, lazy loading and pickling to improve xarray's experience for parallel computing. - Improvements for accessing and manipulating ``pandas.MultiIndex`` levels. - Many new methods and functions, including :py:meth:`~DataArray.quantile`, :py:meth:`~DataArray.cumsum`, :py:meth:`~DataArray.cumprod` :py:attr:`~DataArray.combine_first` :py:meth:`~DataArray.set_index`, :py:meth:`~DataArray.reset_index`, :py:meth:`~DataArray.reorder_levels`, :py:func:`~xarray.full_like`, :py:func:`~xarray.zeros_like`, :py:func:`~xarray.ones_like` :py:func:`~xarray.open_dataarray`, :py:meth:`~DataArray.compute`, :py:meth:`Dataset.info`, :py:func:`testing.assert_equal`, :py:func:`testing.assert_identical`, and :py:func:`testing.assert_allclose`. Breaking changes ~~~~~~~~~~~~~~~~ - Index coordinates for each dimensions are now optional, and no longer created by default :issue:`1017`. You can identify such dimensions without coordinates by their appearance in list of "Dimensions without coordinates" in the ``Dataset`` or ``DataArray`` repr: .. ipython:: :verbatim: In [1]: xr.Dataset({'foo': (('x', 'y'), [[1, 2]])}) Out[1]: Dimensions: (x: 1, y: 2) Dimensions without coordinates: x, y Data variables: foo (x, y) int64 1 2 This has a number of implications: - :py:func:`~align` and :py:meth:`~Dataset.reindex` can now error, if dimensions labels are missing and dimensions have different sizes. - Because pandas does not support missing indexes, methods such as ``to_dataframe``/``from_dataframe`` and ``stack``/``unstack`` no longer roundtrip faithfully on all inputs. Use :py:meth:`~Dataset.reset_index` to remove undesired indexes. - ``Dataset.__delitem__`` and :py:meth:`~Dataset.drop` no longer delete/drop variables that have dimensions matching a deleted/dropped variable. - ``DataArray.coords.__delitem__`` is now allowed on variables matching dimension names. - ``.sel`` and ``.loc`` now handle indexing along a dimension without coordinate labels by doing integer based indexing. See :ref:`indexing.missing_coordinates` for an example. - :py:attr:`~Dataset.indexes` is no longer guaranteed to include all dimensions names as keys. The new method :py:meth:`~Dataset.get_index` has been added to get an index for a dimension guaranteed, falling back to produce a default ``RangeIndex`` if necessary. - The default behavior of ``merge`` is now ``compat='no_conflicts'``, so some merges will now succeed in cases that previously raised ``xarray.MergeError``. Set ``compat='broadcast_equals'`` to restore the previous default. See :ref:`combining.no_conflicts` for more details. - Reading :py:attr:`~DataArray.values` no longer always caches values in a NumPy array :issue:`1128`. Caching of ``.values`` on variables read from netCDF files on disk is still the default when :py:func:`open_dataset` is called with ``cache=True``. By `Guido Imperiale `_ and `Stephan Hoyer `_. - Pickling a ``Dataset`` or ``DataArray`` linked to a file on disk no longer caches its values into memory before pickling (:issue:`1128`). Instead, pickle stores file paths and restores objects by reopening file references. This enables preliminary, experimental use of xarray for opening files with `dask.distributed `_. By `Stephan Hoyer `_. - Coordinates used to index a dimension are now loaded eagerly into :py:class:`pandas.Index` objects, instead of loading the values lazily. By `Guido Imperiale `_. - Automatic levels for 2d plots are now guaranteed to land on ``vmin`` and ``vmax`` when these kwargs are explicitly provided (:issue:`1191`). The automated level selection logic also slightly changed. By `Fabien Maussion `_. - ``DataArray.rename()`` behavior changed to strictly change the ``DataArray.name`` if called with string argument, or strictly change coordinate names if called with dict-like argument. By `Markus Gonser `_. - By default ``to_netcdf()`` add a ``_FillValue = NaN`` attributes to float types. By `Frederic Laliberte `_. - ``repr`` on ``DataArray`` objects uses an shortened display for NumPy array data that is less likely to overflow onto multiple pages (:issue:`1207`). By `Stephan Hoyer `_. - xarray no longer supports python 3.3, versions of dask prior to v0.9.0, or versions of bottleneck prior to v1.0. Deprecations ~~~~~~~~~~~~ - Renamed the ``Coordinate`` class from xarray's low level API to :py:class:`~xarray.IndexVariable`. ``Variable.to_variable`` and ``Variable.to_coord`` have been renamed to :py:meth:`~xarray.Variable.to_base_variable` and :py:meth:`~xarray.Variable.to_index_variable`. - Deprecated supplying ``coords`` as a dictionary to the ``DataArray`` constructor without also supplying an explicit ``dims`` argument. The old behavior encouraged relying on the iteration order of dictionaries, which is a bad practice (:issue:`727`). - Removed a number of methods deprecated since v0.7.0 or earlier: ``load_data``, ``vars``, ``drop_vars``, ``dump``, ``dumps`` and the ``variables`` keyword argument to ``Dataset``. - Removed the dummy module that enabled ``import xray``. Enhancements ~~~~~~~~~~~~ - Added new method :py:meth:`~DataArray.combine_first` to ``DataArray`` and ``Dataset``, based on the pandas method of the same name (see :ref:`combine`). By `Chun-Wei Yuan `_. - Added the ability to change default automatic alignment (arithmetic_join="inner") for binary operations via :py:func:`~xarray.set_options()` (see :ref:`math automatic alignment`). By `Chun-Wei Yuan `_. - Add checking of ``attr`` names and values when saving to netCDF, raising useful error messages if they are invalid. (:issue:`911`). By `Robin Wilson `_. - Added ability to save ``DataArray`` objects directly to netCDF files using :py:meth:`~xarray.DataArray.to_netcdf`, and to load directly from netCDF files using :py:func:`~xarray.open_dataarray` (:issue:`915`). These remove the need to convert a ``DataArray`` to a ``Dataset`` before saving as a netCDF file, and deals with names to ensure a perfect 'roundtrip' capability. By `Robin Wilson `_. - Multi-index levels are now accessible as "virtual" coordinate variables, e.g., ``ds['time']`` can pull out the ``'time'`` level of a multi-index (see :ref:`coordinates`). ``sel`` also accepts providing multi-index levels as keyword arguments, e.g., ``ds.sel(time='2000-01')`` (see :ref:`multi-level indexing`). By `Benoit Bovy `_. - Added ``set_index``, ``reset_index`` and ``reorder_levels`` methods to easily create and manipulate (multi-)indexes (see :ref:`reshape.set_index`). By `Benoit Bovy `_. - Added the ``compat`` option ``'no_conflicts'`` to ``merge``, allowing the combination of xarray objects with disjoint (:issue:`742`) or overlapping (:issue:`835`) coordinates as long as all present data agrees. By `Johnnie Gray `_. See :ref:`combining.no_conflicts` for more details. - It is now possible to set ``concat_dim=None`` explicitly in :py:func:`~xarray.open_mfdataset` to disable inferring a dimension along which to concatenate. By `Stephan Hoyer `_. - Added methods :py:meth:`DataArray.compute`, :py:meth:`Dataset.compute`, and :py:meth:`Variable.compute` as a non-mutating alternative to :py:meth:`~DataArray.load`. By `Guido Imperiale `_. - Adds DataArray and Dataset methods :py:meth:`~xarray.DataArray.cumsum` and :py:meth:`~xarray.DataArray.cumprod`. By `Phillip J. Wolfram `_. - New properties :py:attr:`Dataset.sizes` and :py:attr:`DataArray.sizes` for providing consistent access to dimension length on both ``Dataset`` and ``DataArray`` (:issue:`921`). By `Stephan Hoyer `_. - New keyword argument ``drop=True`` for :py:meth:`~DataArray.sel`, :py:meth:`~DataArray.isel` and :py:meth:`~DataArray.squeeze` for dropping scalar coordinates that arise from indexing. ``DataArray`` (:issue:`242`). By `Stephan Hoyer `_. - New top-level functions :py:func:`~xarray.full_like`, :py:func:`~xarray.zeros_like`, and :py:func:`~xarray.ones_like` By `Guido Imperiale `_. - Overriding a preexisting attribute with :py:func:`~xarray.register_dataset_accessor` or :py:func:`~xarray.register_dataarray_accessor` now issues a warning instead of raising an error (:issue:`1082`). By `Stephan Hoyer `_. - Options for axes sharing between subplots are exposed to :py:class:`FacetGrid` and :py:func:`~xarray.plot.plot`, so axes sharing can be disabled for polar plots. By `Bas Hoonhout `_. - New utility functions :py:func:`~xarray.testing.assert_equal`, :py:func:`~xarray.testing.assert_identical`, and :py:func:`~xarray.testing.assert_allclose` for asserting relationships between xarray objects, designed for use in a pytest test suite. - ``figsize``, ``size`` and ``aspect`` plot arguments are now supported for all plots (:issue:`897`). See :ref:`plotting.figsize` for more details. By `Stephan Hoyer `_ and `Fabien Maussion `_. - New :py:meth:`~Dataset.info` method to summarize ``Dataset`` variables and attributes. The method prints to a buffer (e.g. ``stdout``) with output similar to what the command line utility ``ncdump -h`` produces (:issue:`1150`). By `Joe Hamman `_. - Added the ability write unlimited netCDF dimensions with the ``scipy`` and ``netcdf4`` backends via the new :py:attr:`~xray.Dataset.encoding` attribute or via the ``unlimited_dims`` argument to :py:meth:`~xray.Dataset.to_netcdf`. By `Joe Hamman `_. - New :py:meth:`~DataArray.quantile` method to calculate quantiles from DataArray objects (:issue:`1187`). By `Joe Hamman `_. Bug fixes ~~~~~~~~~ - ``groupby_bins`` now restores empty bins by default (:issue:`1019`). By `Ryan Abernathey `_. - Fix issues for dates outside the valid range of pandas timestamps (:issue:`975`). By `Mathias Hauser `_. - Unstacking produced flipped array after stacking decreasing coordinate values (:issue:`980`). By `Stephan Hoyer `_. - Setting ``dtype`` via the ``encoding`` parameter of ``to_netcdf`` failed if the encoded dtype was the same as the dtype of the original array (:issue:`873`). By `Stephan Hoyer `_. - Fix issues with variables where both attributes ``_FillValue`` and ``missing_value`` are set to ``NaN`` (:issue:`997`). By `Marco Zühlke `_. - ``.where()`` and ``.fillna()`` now preserve attributes (:issue:`1009`). By `Fabien Maussion `_. - Applying :py:func:`broadcast()` to an xarray object based on the dask backend won't accidentally convert the array from dask to numpy anymore (:issue:`978`). By `Guido Imperiale `_. - ``Dataset.concat()`` now preserves variables order (:issue:`1027`). By `Fabien Maussion `_. - Fixed an issue with pcolormesh (:issue:`781`). A new ``infer_intervals`` keyword gives control on whether the cell intervals should be computed or not. By `Fabien Maussion `_. - Grouping over an dimension with non-unique values with ``groupby`` gives correct groups. By `Stephan Hoyer `_. - Fixed accessing coordinate variables with non-string names from ``.coords``. By `Stephan Hoyer `_. - :py:meth:`~xarray.DataArray.rename` now simultaneously renames the array and any coordinate with the same name, when supplied via a :py:class:`dict` (:issue:`1116`). By `Yves Delley `_. - Fixed sub-optimal performance in certain operations with object arrays (:issue:`1121`). By `Yves Delley `_. - Fix ``.groupby(group)`` when ``group`` has datetime dtype (:issue:`1132`). By `Jonas Sølvsteen `_. - Fixed a bug with facetgrid (the ``norm`` keyword was ignored, :issue:`1159`). By `Fabien Maussion `_. - Resolved a concurrency bug that could cause Python to crash when simultaneously reading and writing netCDF4 files with dask (:issue:`1172`). By `Stephan Hoyer `_. - Fix to make ``.copy()`` actually copy dask arrays, which will be relevant for future releases of dask in which dask arrays will be mutable (:issue:`1180`). By `Stephan Hoyer `_. - Fix opening NetCDF files with multi-dimensional time variables (:issue:`1229`). By `Stephan Hoyer `_. Performance improvements ~~~~~~~~~~~~~~~~~~~~~~~~ - :py:meth:`~xarray.Dataset.isel_points` and :py:meth:`~xarray.Dataset.sel_points` now use vectorised indexing in numpy and dask (:issue:`1161`), which can result in several orders of magnitude speedup. By `Jonathan Chambers `_. .. _whats-new.0.8.2: v0.8.2 (18 August 2016) ----------------------- This release includes a number of bug fixes and minor enhancements. Breaking changes ~~~~~~~~~~~~~~~~ - :py:func:`~xarray.broadcast` and :py:func:`~xarray.concat` now auto-align inputs, using ``join=outer``. Previously, these functions raised ``ValueError`` for non-aligned inputs. By `Guido Imperiale `_. Enhancements ~~~~~~~~~~~~ - New documentation on :ref:`panel transition`. By `Maximilian Roos `_. - New ``Dataset`` and ``DataArray`` methods :py:meth:`~xarray.Dataset.to_dict` and :py:meth:`~xarray.Dataset.from_dict` to allow easy conversion between dictionaries and xarray objects (:issue:`432`). See :ref:`dictionary IO` for more details. By `Julia Signell `_. - Added ``exclude`` and ``indexes`` optional parameters to :py:func:`~xarray.align`, and ``exclude`` optional parameter to :py:func:`~xarray.broadcast`. By `Guido Imperiale `_. - Better error message when assigning variables without dimensions (:issue:`971`). By `Stephan Hoyer `_. - Better error message when reindex/align fails due to duplicate index values (:issue:`956`). By `Stephan Hoyer `_. Bug fixes ~~~~~~~~~ - Ensure xarray works with h5netcdf v0.3.0 for arrays with ``dtype=str`` (:issue:`953`). By `Stephan Hoyer `_. - ``Dataset.__dir__()`` (i.e. the method python calls to get autocomplete options) failed if one of the dataset's keys was not a string (:issue:`852`). By `Maximilian Roos `_. - ``Dataset`` constructor can now take arbitrary objects as values (:issue:`647`). By `Maximilian Roos `_. - Clarified ``copy`` argument for :py:meth:`~xarray.DataArray.reindex` and :py:func:`~xarray.align`, which now consistently always return new xarray objects (:issue:`927`). - Fix ``open_mfdataset`` with ``engine='pynio'`` (:issue:`936`). By `Stephan Hoyer `_. - ``groupby_bins`` sorted bin labels as strings (:issue:`952`). By `Stephan Hoyer `_. - Fix bug introduced by v0.8.0 that broke assignment to datasets when both the left and right side have the same non-unique index values (:issue:`956`). .. _whats-new.0.8.1: v0.8.1 (5 August 2016) ---------------------- Bug fixes ~~~~~~~~~ - Fix bug in v0.8.0 that broke assignment to Datasets with non-unique indexes (:issue:`943`). By `Stephan Hoyer `_. .. _whats-new.0.8.0: v0.8.0 (2 August 2016) ---------------------- This release includes four months of new features and bug fixes, including several breaking changes. .. _v0.8.0.breaking: Breaking changes ~~~~~~~~~~~~~~~~ - Dropped support for Python 2.6 (:issue:`855`). - Indexing on multi-index now drop levels, which is consistent with pandas. It also changes the name of the dimension / coordinate when the multi-index is reduced to a single index (:issue:`802`). - Contour plots no longer add a colorbar per default (:issue:`866`). Filled contour plots are unchanged. - ``DataArray.values`` and ``.data`` now always returns an NumPy array-like object, even for 0-dimensional arrays with object dtype (:issue:`867`). Previously, ``.values`` returned native Python objects in such cases. To convert the values of scalar arrays to Python objects, use the ``.item()`` method. Enhancements ~~~~~~~~~~~~ - Groupby operations now support grouping over multidimensional variables. A new method called :py:meth:`~xarray.Dataset.groupby_bins` has also been added to allow users to specify bins for grouping. The new features are described in :ref:`groupby.multidim` and :ref:`examples.multidim`. By `Ryan Abernathey `_. - DataArray and Dataset method :py:meth:`where` now supports a ``drop=True`` option that clips coordinate elements that are fully masked. By `Phillip J. Wolfram `_. - New top level :py:func:`merge` function allows for combining variables from any number of ``Dataset`` and/or ``DataArray`` variables. See :ref:`merge` for more details. By `Stephan Hoyer `_. - DataArray and Dataset method :py:meth:`resample` now supports the ``keep_attrs=False`` option that determines whether variable and dataset attributes are retained in the resampled object. By `Jeremy McGibbon `_. - Better multi-index support in DataArray and Dataset :py:meth:`sel` and :py:meth:`loc` methods, which now behave more closely to pandas and which also accept dictionaries for indexing based on given level names and labels (see :ref:`multi-level indexing`). By `Benoit Bovy `_. - New (experimental) decorators :py:func:`~xarray.register_dataset_accessor` and :py:func:`~xarray.register_dataarray_accessor` for registering custom xarray extensions without subclassing. They are described in the new documentation page on :ref:`internals`. By `Stephan Hoyer `_. - Round trip boolean datatypes. Previously, writing boolean datatypes to netCDF formats would raise an error since netCDF does not have a `bool` datatype. This feature reads/writes a `dtype` attribute to boolean variables in netCDF files. By `Joe Hamman `_. - 2D plotting methods now have two new keywords (`cbar_ax` and `cbar_kwargs`), allowing more control on the colorbar (:issue:`872`). By `Fabien Maussion `_. - New Dataset method :py:meth:`filter_by_attrs`, akin to ``netCDF4.Dataset.get_variables_by_attributes``, to easily filter data variables using its attributes. `Filipe Fernandes `_. Bug fixes ~~~~~~~~~ - Attributes were being retained by default for some resampling operations when they should not. With the ``keep_attrs=False`` option, they will no longer be retained by default. This may be backwards-incompatible with some scripts, but the attributes may be kept by adding the ``keep_attrs=True`` option. By `Jeremy McGibbon `_. - Concatenating xarray objects along an axis with a MultiIndex or PeriodIndex preserves the nature of the index (:issue:`875`). By `Stephan Hoyer `_. - Fixed bug in arithmetic operations on DataArray objects whose dimensions are numpy structured arrays or recarrays :issue:`861`, :issue:`837`. By `Maciek Swat `_. - ``decode_cf_timedelta`` now accepts arrays with ``ndim`` >1 (:issue:`842`). This fixes issue :issue:`665`. `Filipe Fernandes `_. - Fix a bug where `xarray.ufuncs` that take two arguments would incorrectly use to numpy functions instead of dask.array functions (:issue:`876`). By `Stephan Hoyer `_. - Support for pickling functions from ``xarray.ufuncs`` (:issue:`901`). By `Stephan Hoyer `_. - ``Variable.copy(deep=True)`` no longer converts MultiIndex into a base Index (:issue:`769`). By `Benoit Bovy `_. - Fixes for groupby on dimensions with a multi-index (:issue:`867`). By `Stephan Hoyer `_. - Fix printing datasets with unicode attributes on Python 2 (:issue:`892`). By `Stephan Hoyer `_. - Fixed incorrect test for dask version (:issue:`891`). By `Stephan Hoyer `_. - Fixed `dim` argument for `isel_points`/`sel_points` when a `pandas.Index` is passed. By `Stephan Hoyer `_. - :py:func:`~xarray.plot.contour` now plots the correct number of contours (:issue:`866`). By `Fabien Maussion `_. .. _whats-new.0.7.2: v0.7.2 (13 March 2016) ---------------------- This release includes two new, entirely backwards compatible features and several bug fixes. Enhancements ~~~~~~~~~~~~ - New DataArray method :py:meth:`DataArray.dot` for calculating the dot product of two DataArrays along shared dimensions. By `Dean Pospisil `_. - Rolling window operations on DataArray objects are now supported via a new :py:meth:`DataArray.rolling` method. For example: .. ipython:: :verbatim: In [1]: import xarray as xr; import numpy as np In [2]: arr = xr.DataArray(np.arange(0, 7.5, 0.5).reshape(3, 5), dims=('x', 'y')) In [3]: arr Out[3]: array([[ 0. , 0.5, 1. , 1.5, 2. ], [ 2.5, 3. , 3.5, 4. , 4.5], [ 5. , 5.5, 6. , 6.5, 7. ]]) Coordinates: * x (x) int64 0 1 2 * y (y) int64 0 1 2 3 4 In [4]: arr.rolling(y=3, min_periods=2).mean() Out[4]: array([[ nan, 0.25, 0.5 , 1. , 1.5 ], [ nan, 2.75, 3. , 3.5 , 4. ], [ nan, 5.25, 5.5 , 6. , 6.5 ]]) Coordinates: * x (x) int64 0 1 2 * y (y) int64 0 1 2 3 4 See :ref:`comput.rolling` for more details. By `Joe Hamman `_. Bug fixes ~~~~~~~~~ - Fixed an issue where plots using pcolormesh and Cartopy axes were being distorted by the inference of the axis interval breaks. This change chooses not to modify the coordinate variables when the axes have the attribute ``projection``, allowing Cartopy to handle the extent of pcolormesh plots (:issue:`781`). By `Joe Hamman `_. - 2D plots now better handle additional coordinates which are not ``DataArray`` dimensions (:issue:`788`). By `Fabien Maussion `_. .. _whats-new.0.7.1: v0.7.1 (16 February 2016) ------------------------- This is a bug fix release that includes two small, backwards compatible enhancements. We recommend that all users upgrade. Enhancements ~~~~~~~~~~~~ - Numerical operations now return empty objects on no overlapping labels rather than raising ``ValueError`` (:issue:`739`). - :py:class:`~pd.Series` is now supported as valid input to the ``Dataset`` constructor (:issue:`740`). Bug fixes ~~~~~~~~~ - Restore checks for shape consistency between data and coordinates in the DataArray constructor (:issue:`758`). - Single dimension variables no longer transpose as part of a broader ``.transpose``. This behavior was causing ``pandas.PeriodIndex`` dimensions to lose their type (:issue:`749`) - :py:class:`~xarray.Dataset` labels remain as their native type on ``.to_dataset``. Previously they were coerced to strings (:issue:`745`) - Fixed a bug where replacing a ``DataArray`` index coordinate would improperly align the coordinate (:issue:`725`). - ``DataArray.reindex_like`` now maintains the dtype of complex numbers when reindexing leads to NaN values (:issue:`738`). - ``Dataset.rename`` and ``DataArray.rename`` support the old and new names being the same (:issue:`724`). - Fix :py:meth:`~xarray.Dataset.from_dataset` for DataFrames with Categorical column and a MultiIndex index (:issue:`737`). - Fixes to ensure xarray works properly after the upcoming pandas v0.18 and NumPy v1.11 releases. Acknowledgments ~~~~~~~~~~~~~~~ The following individuals contributed to this release: - Edward Richards - Maximilian Roos - Rafael Guedes - Spencer Hill - Stephan Hoyer .. _whats-new.0.7.0: v0.7.0 (21 January 2016) ------------------------ This major release includes redesign of :py:class:`~xarray.DataArray` internals, as well as new methods for reshaping, rolling and shifting data. It includes preliminary support for :py:class:`pandas.MultiIndex`, as well as a number of other features and bug fixes, several of which offer improved compatibility with pandas. New name ~~~~~~~~ The project formerly known as "xray" is now "xarray", pronounced "x-array"! This avoids a namespace conflict with the entire field of x-ray science. Renaming our project seemed like the right thing to do, especially because some scientists who work with actual x-rays are interested in using this project in their work. Thanks for your understanding and patience in this transition. You can now find our documentation and code repository at new URLs: - http://xarray.pydata.org - http://github.com/pydata/xarray/ To ease the transition, we have simultaneously released v0.7.0 of both ``xray`` and ``xarray`` on the Python Package Index. These packages are identical. For now, ``import xray`` still works, except it issues a deprecation warning. This will be the last xray release. Going forward, we recommend switching your import statements to ``import xarray as xr``. .. _v0.7.0.breaking: Breaking changes ~~~~~~~~~~~~~~~~ - The internal data model used by :py:class:`~xray.DataArray` has been rewritten to fix several outstanding issues (:issue:`367`, :issue:`634`, `this stackoverflow report`_). Internally, ``DataArray`` is now implemented in terms of ``._variable`` and ``._coords`` attributes instead of holding variables in a ``Dataset`` object. This refactor ensures that if a DataArray has the same name as one of its coordinates, the array and the coordinate no longer share the same data. In practice, this means that creating a DataArray with the same ``name`` as one of its dimensions no longer automatically uses that array to label the corresponding coordinate. You will now need to provide coordinate labels explicitly. Here's the old behavior: .. ipython:: :verbatim: In [2]: xray.DataArray([4, 5, 6], dims='x', name='x') Out[2]: array([4, 5, 6]) Coordinates: * x (x) int64 4 5 6 and the new behavior (compare the values of the ``x`` coordinate): .. ipython:: :verbatim: In [2]: xray.DataArray([4, 5, 6], dims='x', name='x') Out[2]: array([4, 5, 6]) Coordinates: * x (x) int64 0 1 2 - It is no longer possible to convert a DataArray to a Dataset with :py:meth:`xray.DataArray.to_dataset` if it is unnamed. This will now raise ``ValueError``. If the array is unnamed, you need to supply the ``name`` argument. .. _this stackoverflow report: http://stackoverflow.com/questions/33158558/python-xray-extract-first-and-last-time-value-within-each-month-of-a-timeseries Enhancements ~~~~~~~~~~~~ - Basic support for :py:class:`~pandas.MultiIndex` coordinates on xray objects, including indexing, :py:meth:`~DataArray.stack` and :py:meth:`~DataArray.unstack`: .. ipython:: :verbatim: In [7]: df = pd.DataFrame({'foo': range(3), ...: 'x': ['a', 'b', 'b'], ...: 'y': [0, 0, 1]}) In [8]: s = df.set_index(['x', 'y'])['foo'] In [12]: arr = xray.DataArray(s, dims='z') In [13]: arr Out[13]: array([0, 1, 2]) Coordinates: * z (z) object ('a', 0) ('b', 0) ('b', 1) In [19]: arr.indexes['z'] Out[19]: MultiIndex(levels=[[u'a', u'b'], [0, 1]], labels=[[0, 1, 1], [0, 0, 1]], names=[u'x', u'y']) In [14]: arr.unstack('z') Out[14]: array([[ 0., nan], [ 1., 2.]]) Coordinates: * x (x) object 'a' 'b' * y (y) int64 0 1 In [26]: arr.unstack('z').stack(z=('x', 'y')) Out[26]: array([ 0., nan, 1., 2.]) Coordinates: * z (z) object ('a', 0) ('a', 1) ('b', 0) ('b', 1) See :ref:`reshape.stack` for more details. .. warning:: xray's MultiIndex support is still experimental, and we have a long to- do list of desired additions (:issue:`719`), including better display of multi-index levels when printing a ``Dataset``, and support for saving datasets with a MultiIndex to a netCDF file. User contributions in this area would be greatly appreciated. - Support for reading GRIB, HDF4 and other file formats via PyNIO_. See :ref:`io.pynio` for more details. - Better error message when a variable is supplied with the same name as one of its dimensions. - Plotting: more control on colormap parameters (:issue:`642`). ``vmin`` and ``vmax`` will not be silently ignored anymore. Setting ``center=False`` prevents automatic selection of a divergent colormap. - New :py:meth:`~xray.Dataset.shift` and :py:meth:`~xray.Dataset.roll` methods for shifting/rotating datasets or arrays along a dimension: .. ipython:: python array = xray.DataArray([5, 6, 7, 8], dims='x') array.shift(x=2) array.roll(x=2) Notice that ``shift`` moves data independently of coordinates, but ``roll`` moves both data and coordinates. - Assigning a ``pandas`` object directly as a ``Dataset`` variable is now permitted. Its index names correspond to the ``dims`` of the ``Dataset``, and its data is aligned. - Passing a :py:class:`pandas.DataFrame` or :py:class:`pandas.Panel` to a Dataset constructor is now permitted. - New function :py:func:`~xray.broadcast` for explicitly broadcasting ``DataArray`` and ``Dataset`` objects against each other. For example: .. ipython:: python a = xray.DataArray([1, 2, 3], dims='x') b = xray.DataArray([5, 6], dims='y') a b a2, b2 = xray.broadcast(a, b) a2 b2 .. _PyNIO: https://www.pyngl.ucar.edu/Nio.shtml Bug fixes ~~~~~~~~~ - Fixes for several issues found on ``DataArray`` objects with the same name as one of their coordinates (see :ref:`v0.7.0.breaking` for more details). - ``DataArray.to_masked_array`` always returns masked array with mask being an array (not a scalar value) (:issue:`684`) - Allows for (imperfect) repr of Coords when underlying index is PeriodIndex (:issue:`645`). - Fixes for several issues found on ``DataArray`` objects with the same name as one of their coordinates (see :ref:`v0.7.0.breaking` for more details). - Attempting to assign a ``Dataset`` or ``DataArray`` variable/attribute using attribute-style syntax (e.g., ``ds.foo = 42``) now raises an error rather than silently failing (:issue:`656`, :issue:`714`). - You can now pass pandas objects with non-numpy dtypes (e.g., ``categorical`` or ``datetime64`` with a timezone) into xray without an error (:issue:`716`). Acknowledgments ~~~~~~~~~~~~~~~ The following individuals contributed to this release: - Antony Lee - Fabien Maussion - Joe Hamman - Maximilian Roos - Stephan Hoyer - Takeshi Kanmae - femtotrader v0.6.1 (21 October 2015) ------------------------ This release contains a number of bug and compatibility fixes, as well as enhancements to plotting, indexing and writing files to disk. Note that the minimum required version of dask for use with xray is now version 0.6. API Changes ~~~~~~~~~~~ - The handling of colormaps and discrete color lists for 2D plots in :py:meth:`~xray.DataArray.plot` was changed to provide more compatibility with matplotlib's ``contour`` and ``contourf`` functions (:issue:`538`). Now discrete lists of colors should be specified using ``colors`` keyword, rather than ``cmap``. Enhancements ~~~~~~~~~~~~ - Faceted plotting through :py:class:`~xray.plot.FacetGrid` and the :py:meth:`~xray.plot.plot` method. See :ref:`plotting.faceting` for more details and examples. - :py:meth:`~xray.Dataset.sel` and :py:meth:`~xray.Dataset.reindex` now support the ``tolerance`` argument for controlling nearest-neighbor selection (:issue:`629`): .. ipython:: :verbatim: In [5]: array = xray.DataArray([1, 2, 3], dims='x') In [6]: array.reindex(x=[0.9, 1.5], method='nearest', tolerance=0.2) Out[6]: array([ 2., nan]) Coordinates: * x (x) float64 0.9 1.5 This feature requires pandas v0.17 or newer. - New ``encoding`` argument in :py:meth:`~xray.Dataset.to_netcdf` for writing netCDF files with compression, as described in the new documentation section on :ref:`io.netcdf.writing_encoded`. - Add :py:attr:`~xray.Dataset.real` and :py:attr:`~xray.Dataset.imag` attributes to Dataset and DataArray (:issue:`553`). - More informative error message with :py:meth:`~xray.Dataset.from_dataframe` if the frame has duplicate columns. - xray now uses deterministic names for dask arrays it creates or opens from disk. This allows xray users to take advantage of dask's nascent support for caching intermediate computation results. See :issue:`555` for an example. Bug fixes ~~~~~~~~~ - Forwards compatibility with the latest pandas release (v0.17.0). We were using some internal pandas routines for datetime conversion, which unfortunately have now changed upstream (:issue:`569`). - Aggregation functions now correctly skip ``NaN`` for data for ``complex128`` dtype (:issue:`554`). - Fixed indexing 0d arrays with unicode dtype (:issue:`568`). - :py:meth:`~xray.DataArray.name` and Dataset keys must be a string or None to be written to netCDF (:issue:`533`). - :py:meth:`~xray.DataArray.where` now uses dask instead of numpy if either the array or ``other`` is a dask array. Previously, if ``other`` was a numpy array the method was evaluated eagerly. - Global attributes are now handled more consistently when loading remote datasets using ``engine='pydap'`` (:issue:`574`). - It is now possible to assign to the ``.data`` attribute of DataArray objects. - ``coordinates`` attribute is now kept in the encoding dictionary after decoding (:issue:`610`). - Compatibility with numpy 1.10 (:issue:`617`). Acknowledgments ~~~~~~~~~~~~~~~ The following individuals contributed to this release: - Ryan Abernathey - Pete Cable - Clark Fitzgerald - Joe Hamman - Stephan Hoyer - Scott Sinclair v0.6.0 (21 August 2015) ----------------------- This release includes numerous bug fixes and enhancements. Highlights include the introduction of a plotting module and the new Dataset and DataArray methods :py:meth:`~xray.Dataset.isel_points`, :py:meth:`~xray.Dataset.sel_points`, :py:meth:`~xray.Dataset.where` and :py:meth:`~xray.Dataset.diff`. There are no breaking changes from v0.5.2. Enhancements ~~~~~~~~~~~~ - Plotting methods have been implemented on DataArray objects :py:meth:`~xray.DataArray.plot` through integration with matplotlib (:issue:`185`). For an introduction, see :ref:`plotting`. - Variables in netCDF files with multiple missing values are now decoded as NaN after issuing a warning if open_dataset is called with mask_and_scale=True. - We clarified our rules for when the result from an xray operation is a copy vs. a view (see :ref:`copies vs views` for more details). - Dataset variables are now written to netCDF files in order of appearance when using the netcdf4 backend (:issue:`479`). - Added :py:meth:`~xray.Dataset.isel_points` and :py:meth:`~xray.Dataset.sel_points` to support pointwise indexing of Datasets and DataArrays (:issue:`475`). .. ipython:: :verbatim: In [1]: da = xray.DataArray(np.arange(56).reshape((7, 8)), ...: coords={'x': list('abcdefg'), ...: 'y': 10 * np.arange(8)}, ...: dims=['x', 'y']) In [2]: da Out[2]: array([[ 0, 1, 2, 3, 4, 5, 6, 7], [ 8, 9, 10, 11, 12, 13, 14, 15], [16, 17, 18, 19, 20, 21, 22, 23], [24, 25, 26, 27, 28, 29, 30, 31], [32, 33, 34, 35, 36, 37, 38, 39], [40, 41, 42, 43, 44, 45, 46, 47], [48, 49, 50, 51, 52, 53, 54, 55]]) Coordinates: * y (y) int64 0 10 20 30 40 50 60 70 * x (x) |S1 'a' 'b' 'c' 'd' 'e' 'f' 'g' # we can index by position along each dimension In [3]: da.isel_points(x=[0, 1, 6], y=[0, 1, 0], dim='points') Out[3]: array([ 0, 9, 48]) Coordinates: y (points) int64 0 10 0 x (points) |S1 'a' 'b' 'g' * points (points) int64 0 1 2 # or equivalently by label In [9]: da.sel_points(x=['a', 'b', 'g'], y=[0, 10, 0], dim='points') Out[9]: array([ 0, 9, 48]) Coordinates: y (points) int64 0 10 0 x (points) |S1 'a' 'b' 'g' * points (points) int64 0 1 2 - New :py:meth:`~xray.Dataset.where` method for masking xray objects according to some criteria. This works particularly well with multi-dimensional data: .. ipython:: python ds = xray.Dataset(coords={'x': range(100), 'y': range(100)}) ds['distance'] = np.sqrt(ds.x ** 2 + ds.y ** 2) @savefig where_example.png width=4in height=4in ds.distance.where(ds.distance < 100).plot() - Added new methods :py:meth:`DataArray.diff ` and :py:meth:`Dataset.diff ` for finite difference calculations along a given axis. - New :py:meth:`~xray.DataArray.to_masked_array` convenience method for returning a numpy.ma.MaskedArray. .. ipython:: python da = xray.DataArray(np.random.random_sample(size=(5, 4))) da.where(da < 0.5) da.where(da < 0.5).to_masked_array(copy=True) - Added new flag "drop_variables" to :py:meth:`~xray.open_dataset` for excluding variables from being parsed. This may be useful to drop variables with problems or inconsistent values. Bug fixes ~~~~~~~~~ - Fixed aggregation functions (e.g., sum and mean) on big-endian arrays when bottleneck is installed (:issue:`489`). - Dataset aggregation functions dropped variables with unsigned integer dtype (:issue:`505`). - ``.any()`` and ``.all()`` were not lazy when used on xray objects containing dask arrays. - Fixed an error when attempting to saving datetime64 variables to netCDF files when the first element is ``NaT`` (:issue:`528`). - Fix pickle on DataArray objects (:issue:`515`). - Fixed unnecessary coercion of float64 to float32 when using netcdf3 and netcdf4_classic formats (:issue:`526`). v0.5.2 (16 July 2015) --------------------- This release contains bug fixes, several additional options for opening and saving netCDF files, and a backwards incompatible rewrite of the advanced options for ``xray.concat``. Backwards incompatible changes ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - The optional arguments ``concat_over`` and ``mode`` in :py:func:`~xray.concat` have been removed and replaced by ``data_vars`` and ``coords``. The new arguments are both more easily understood and more robustly implemented, and allowed us to fix a bug where ``concat`` accidentally loaded data into memory. If you set values for these optional arguments manually, you will need to update your code. The default behavior should be unchanged. Enhancements ~~~~~~~~~~~~ - :py:func:`~xray.open_mfdataset` now supports a ``preprocess`` argument for preprocessing datasets prior to concatenaton. This is useful if datasets cannot be otherwise merged automatically, e.g., if the original datasets have conflicting index coordinates (:issue:`443`). - :py:func:`~xray.open_dataset` and :py:func:`~xray.open_mfdataset` now use a global thread lock by default for reading from netCDF files with dask. This avoids possible segmentation faults for reading from netCDF4 files when HDF5 is not configured properly for concurrent access (:issue:`444`). - Added support for serializing arrays of complex numbers with `engine='h5netcdf'`. - The new :py:func:`~xray.save_mfdataset` function allows for saving multiple datasets to disk simultaneously. This is useful when processing large datasets with dask.array. For example, to save a dataset too big to fit into memory to one file per year, we could write: .. ipython:: :verbatim: In [1]: years, datasets = zip(*ds.groupby('time.year')) In [2]: paths = ['%s.nc' % y for y in years] In [3]: xray.save_mfdataset(datasets, paths) Bug fixes ~~~~~~~~~ - Fixed ``min``, ``max``, ``argmin`` and ``argmax`` for arrays with string or unicode types (:issue:`453`). - :py:func:`~xray.open_dataset` and :py:func:`~xray.open_mfdataset` support supplying chunks as a single integer. - Fixed a bug in serializing scalar datetime variable to netCDF. - Fixed a bug that could occur in serialization of 0-dimensional integer arrays. - Fixed a bug where concatenating DataArrays was not always lazy (:issue:`464`). - When reading datasets with h5netcdf, bytes attributes are decoded to strings. This allows conventions decoding to work properly on Python 3 (:issue:`451`). v0.5.1 (15 June 2015) --------------------- This minor release fixes a few bugs and an inconsistency with pandas. It also adds the ``pipe`` method, copied from pandas. Enhancements ~~~~~~~~~~~~ - Added :py:meth:`~xray.Dataset.pipe`, replicating the `new pandas method`_ in version 0.16.2. See :ref:`transforming datasets` for more details. - :py:meth:`~xray.Dataset.assign` and :py:meth:`~xray.Dataset.assign_coords` now assign new variables in sorted (alphabetical) order, mirroring the behavior in pandas. Previously, the order was arbitrary. .. _new pandas method: http://pandas.pydata.org/pandas-docs/version/0.16.2/whatsnew.html#pipe Bug fixes ~~~~~~~~~ - ``xray.concat`` fails in an edge case involving identical coordinate variables (:issue:`425`) - We now decode variables loaded from netCDF3 files with the scipy engine using native endianness (:issue:`416`). This resolves an issue when aggregating these arrays with bottleneck installed. v0.5 (1 June 2015) ------------------ Highlights ~~~~~~~~~~ The headline feature in this release is experimental support for out-of-core computing (data that doesn't fit into memory) with dask_. This includes a new top-level function :py:func:`~xray.open_mfdataset` that makes it easy to open a collection of netCDF (using dask) as a single ``xray.Dataset`` object. For more on dask, read the `blog post introducing xray + dask`_ and the new documentation section :doc:`dask`. .. _blog post introducing xray + dask: http://continuum.io/blog/xray-dask Dask makes it possible to harness parallelism and manipulate gigantic datasets with xray. It is currently an optional dependency, but it may become required in the future. Backwards incompatible changes ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - The logic used for choosing which variables are concatenated with :py:func:`~xray.concat` has changed. Previously, by default any variables which were equal across a dimension were not concatenated. This lead to some surprising behavior, where the behavior of groupby and concat operations could depend on runtime values (:issue:`268`). For example: .. ipython:: :verbatim: In [1]: ds = xray.Dataset({'x': 0}) In [2]: xray.concat([ds, ds], dim='y') Out[2]: Dimensions: () Coordinates: *empty* Data variables: x int64 0 Now, the default always concatenates data variables: .. ipython:: python :suppress: ds = xray.Dataset({'x': 0}) .. ipython:: python xray.concat([ds, ds], dim='y') To obtain the old behavior, supply the argument ``concat_over=[]``. Enhancements ~~~~~~~~~~~~ - New :py:meth:`~xray.Dataset.to_array` and enhanced :py:meth:`~xray.DataArray.to_dataset` methods make it easy to switch back and forth between arrays and datasets: .. ipython:: python ds = xray.Dataset({'a': 1, 'b': ('x', [1, 2, 3])}, coords={'c': 42}, attrs={'Conventions': 'None'}) ds.to_array() ds.to_array().to_dataset(dim='variable') - New :py:meth:`~xray.Dataset.fillna` method to fill missing values, modeled off the pandas method of the same name: .. ipython:: python array = xray.DataArray([np.nan, 1, np.nan, 3], dims='x') array.fillna(0) ``fillna`` works on both ``Dataset`` and ``DataArray`` objects, and uses index based alignment and broadcasting like standard binary operations. It also can be applied by group, as illustrated in :ref:`fill with climatology`. - New :py:meth:`~xray.Dataset.assign` and :py:meth:`~xray.Dataset.assign_coords` methods patterned off the new :py:meth:`DataFrame.assign ` method in pandas: .. ipython:: python ds = xray.Dataset({'y': ('x', [1, 2, 3])}) ds.assign(z = lambda ds: ds.y ** 2) ds.assign_coords(z = ('x', ['a', 'b', 'c'])) These methods return a new Dataset (or DataArray) with updated data or coordinate variables. - :py:meth:`~xray.Dataset.sel` now supports the ``method`` parameter, which works like the paramter of the same name on :py:meth:`~xray.Dataset.reindex`. It provides a simple interface for doing nearest-neighbor interpolation: .. use verbatim because I can't seem to install pandas 0.16.1 on RTD :( .. ipython:: :verbatim: In [12]: ds.sel(x=1.1, method='nearest') Out[12]: Dimensions: () Coordinates: x int64 1 Data variables: y int64 2 In [13]: ds.sel(x=[1.1, 2.1], method='pad') Out[13]: Dimensions: (x: 2) Coordinates: * x (x) int64 1 2 Data variables: y (x) int64 2 3 See :ref:`nearest neighbor lookups` for more details. - You can now control the underlying backend used for accessing remote datasets (via OPeNDAP) by specifying ``engine='netcdf4'`` or ``engine='pydap'``. - xray now provides experimental support for reading and writing netCDF4 files directly via `h5py`_ with the `h5netcdf`_ package, avoiding the netCDF4-Python package. You will need to install h5netcdf and specify ``engine='h5netcdf'`` to try this feature. - Accessing data from remote datasets now has retrying logic (with exponential backoff) that should make it robust to occasional bad responses from DAP servers. - You can control the width of the Dataset repr with :py:class:`xray.set_options`. It can be used either as a context manager, in which case the default is restored outside the context: .. ipython:: python ds = xray.Dataset({'x': np.arange(1000)}) with xray.set_options(display_width=40): print(ds) Or to set a global option: .. ipython:: :verbatim: In [1]: xray.set_options(display_width=80) The default value for the ``display_width`` option is 80. .. _h5py: http://www.h5py.org/ .. _h5netcdf: https://github.com/shoyer/h5netcdf Deprecations ~~~~~~~~~~~~ - The method ``load_data()`` has been renamed to the more succinct :py:meth:`~xray.Dataset.load`. v0.4.1 (18 March 2015) ---------------------- The release contains bug fixes and several new features. All changes should be fully backwards compatible. Enhancements ~~~~~~~~~~~~ - New documentation sections on :ref:`time-series` and :ref:`combining multiple files`. - :py:meth:`~xray.Dataset.resample` lets you resample a dataset or data array to a new temporal resolution. The syntax is the `same as pandas`_, except you need to supply the time dimension explicitly: .. ipython:: python time = pd.date_range('2000-01-01', freq='6H', periods=10) array = xray.DataArray(np.arange(10), [('time', time)]) array.resample('1D', dim='time') You can specify how to do the resampling with the ``how`` argument and other options such as ``closed`` and ``label`` let you control labeling: .. ipython:: python array.resample('1D', dim='time', how='sum', label='right') If the desired temporal resolution is higher than the original data (upsampling), xray will insert missing values: .. ipython:: python array.resample('3H', 'time') - ``first`` and ``last`` methods on groupby objects let you take the first or last examples from each group along the grouped axis: .. ipython:: python array.groupby('time.day').first() These methods combine well with ``resample``: .. ipython:: python array.resample('1D', dim='time', how='first') - :py:meth:`~xray.Dataset.swap_dims` allows for easily swapping one dimension out for another: .. ipython:: python ds = xray.Dataset({'x': range(3), 'y': ('x', list('abc'))}) ds ds.swap_dims({'x': 'y'}) This was possible in earlier versions of xray, but required some contortions. - :py:func:`~xray.open_dataset` and :py:meth:`~xray.Dataset.to_netcdf` now accept an ``engine`` argument to explicitly select which underlying library (netcdf4 or scipy) is used for reading/writing a netCDF file. .. _same as pandas: http://pandas.pydata.org/pandas-docs/stable/timeseries.html#up-and-downsampling Bug fixes ~~~~~~~~~ - Fixed a bug where data netCDF variables read from disk with ``engine='scipy'`` could still be associated with the file on disk, even after closing the file (:issue:`341`). This manifested itself in warnings about mmapped arrays and segmentation faults (if the data was accessed). - Silenced spurious warnings about all-NaN slices when using nan-aware aggregation methods (:issue:`344`). - Dataset aggregations with ``keep_attrs=True`` now preserve attributes on data variables, not just the dataset itself. - Tests for xray now pass when run on Windows (:issue:`360`). - Fixed a regression in v0.4 where saving to netCDF could fail with the error ``ValueError: could not automatically determine time units``. v0.4 (2 March, 2015) -------------------- This is one of the biggest releases yet for xray: it includes some major changes that may break existing code, along with the usual collection of minor enhancements and bug fixes. On the plus side, this release includes all hitherto planned breaking changes, so the upgrade path for xray should be smoother going forward. Breaking changes ~~~~~~~~~~~~~~~~ - We now automatically align index labels in arithmetic, dataset construction, merging and updating. This means the need for manually invoking methods like :py:func:`~xray.align` and :py:meth:`~xray.Dataset.reindex_like` should be vastly reduced. :ref:`For arithmetic`, we align based on the **intersection** of labels: .. ipython:: python lhs = xray.DataArray([1, 2, 3], [('x', [0, 1, 2])]) rhs = xray.DataArray([2, 3, 4], [('x', [1, 2, 3])]) lhs + rhs :ref:`For dataset construction and merging`, we align based on the **union** of labels: .. ipython:: python xray.Dataset({'foo': lhs, 'bar': rhs}) :ref:`For update and __setitem__`, we align based on the **original** object: .. ipython:: python lhs.coords['rhs'] = rhs lhs - Aggregations like ``mean`` or ``median`` now skip missing values by default: .. ipython:: python xray.DataArray([1, 2, np.nan, 3]).mean() You can turn this behavior off by supplying the keyword arugment ``skipna=False``. These operations are lightning fast thanks to integration with bottleneck_, which is a new optional dependency for xray (numpy is used if bottleneck is not installed). - Scalar coordinates no longer conflict with constant arrays with the same value (e.g., in arithmetic, merging datasets and concat), even if they have different shape (:issue:`243`). For example, the coordinate ``c`` here persists through arithmetic, even though it has different shapes on each DataArray: .. ipython:: python a = xray.DataArray([1, 2], coords={'c': 0}, dims='x') b = xray.DataArray([1, 2], coords={'c': ('x', [0, 0])}, dims='x') (a + b).coords This functionality can be controlled through the ``compat`` option, which has also been added to the :py:class:`~xray.Dataset` constructor. - Datetime shortcuts such as ``'time.month'`` now return a ``DataArray`` with the name ``'month'``, not ``'time.month'`` (:issue:`345`). This makes it easier to index the resulting arrays when they are used with ``groupby``: .. ipython:: python time = xray.DataArray(pd.date_range('2000-01-01', periods=365), dims='time', name='time') counts = time.groupby('time.month').count() counts.sel(month=2) Previously, you would need to use something like ``counts.sel(**{'time.month': 2}})``, which is much more awkward. - The ``season`` datetime shortcut now returns an array of string labels such `'DJF'`: .. ipython:: python ds = xray.Dataset({'t': pd.date_range('2000-01-01', periods=12, freq='M')}) ds['t.season'] Previously, it returned numbered seasons 1 through 4. - We have updated our use of the terms of "coordinates" and "variables". What were known in previous versions of xray as "coordinates" and "variables" are now referred to throughout the documentation as "coordinate variables" and "data variables". This brings xray in closer alignment to `CF Conventions`_. The only visible change besides the documentation is that ``Dataset.vars`` has been renamed ``Dataset.data_vars``. - You will need to update your code if you have been ignoring deprecation warnings: methods and attributes that were deprecated in xray v0.3 or earlier (e.g., ``dimensions``, ``attributes```) have gone away. .. _bottleneck: https://github.com/kwgoodman/bottleneck Enhancements ~~~~~~~~~~~~ - Support for :py:meth:`~xray.Dataset.reindex` with a fill method. This provides a useful shortcut for upsampling: .. ipython:: python data = xray.DataArray([1, 2, 3], [('x', range(3))]) data.reindex(x=[0.5, 1, 1.5, 2, 2.5], method='pad') This will be especially useful once pandas 0.16 is released, at which point xray will immediately support reindexing with `method='nearest' `_. - Use functions that return generic ndarrays with DataArray.groupby.apply and Dataset.apply (:issue:`327` and :issue:`329`). Thanks Jeff Gerard! - Consolidated the functionality of ``dumps`` (writing a dataset to a netCDF3 bytestring) into :py:meth:`~xray.Dataset.to_netcdf` (:issue:`333`). - :py:meth:`~xray.Dataset.to_netcdf` now supports writing to groups in netCDF4 files (:issue:`333`). It also finally has a full docstring -- you should read it! - :py:func:`~xray.open_dataset` and :py:meth:`~xray.Dataset.to_netcdf` now work on netCDF3 files when netcdf4-python is not installed as long as scipy is available (:issue:`333`). - The new :py:meth:`Dataset.drop ` and :py:meth:`DataArray.drop ` methods makes it easy to drop explicitly listed variables or index labels: .. ipython:: python # drop variables ds = xray.Dataset({'x': 0, 'y': 1}) ds.drop('x') # drop index labels arr = xray.DataArray([1, 2, 3], coords=[('x', list('abc'))]) arr.drop(['a', 'c'], dim='x') - :py:meth:`~xray.Dataset.broadcast_equals` has been added to correspond to the new ``compat`` option. - Long attributes are now truncated at 500 characters when printing a dataset (:issue:`338`). This should make things more convenient for working with datasets interactively. - Added a new documentation example, :ref:`monthly means example`. Thanks Joe Hamman! Bug fixes ~~~~~~~~~ - Several bug fixes related to decoding time units from netCDF files (:issue:`316`, :issue:`330`). Thanks Stefan Pfenninger! - xray no longer requires ``decode_coords=False`` when reading datasets with unparseable coordinate attributes (:issue:`308`). - Fixed ``DataArray.loc`` indexing with ``...`` (:issue:`318`). - Fixed an edge case that resulting in an error when reindexing multi-dimensional variables (:issue:`315`). - Slicing with negative step sizes (:issue:`312`). - Invalid conversion of string arrays to numeric dtype (:issue:`305`). - Fixed``repr()`` on dataset objects with non-standard dates (:issue:`347`). Deprecations ~~~~~~~~~~~~ - ``dump`` and ``dumps`` have been deprecated in favor of :py:meth:`~xray.Dataset.to_netcdf`. - ``drop_vars`` has been deprecated in favor of :py:meth:`~xray.Dataset.drop`. Future plans ~~~~~~~~~~~~ The biggest feature I'm excited about working toward in the immediate future is supporting out-of-core operations in xray using Dask_, a part of the Blaze_ project. For a preview of using Dask with weather data, read `this blog post`_ by Matthew Rocklin. See :issue:`328` for more details. .. _Dask: http://dask.pydata.org .. _Blaze: http://blaze.pydata.org .. _this blog post: http://matthewrocklin.com/blog/work/2015/02/13/Towards-OOC-Slicing-and-Stacking/ v0.3.2 (23 December, 2014) -------------------------- This release focused on bug-fixes, speedups and resolving some niggling inconsistencies. There are a few cases where the behavior of xray differs from the previous version. However, I expect that in almost all cases your code will continue to run unmodified. .. warning:: xray now requires pandas v0.15.0 or later. This was necessary for supporting TimedeltaIndex without too many painful hacks. Backwards incompatible changes ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - Arrays of :py:class:`datetime.datetime` objects are now automatically cast to ``datetime64[ns]`` arrays when stored in an xray object, using machinery borrowed from pandas: .. ipython:: python from datetime import datetime xray.Dataset({'t': [datetime(2000, 1, 1)]}) - xray now has support (including serialization to netCDF) for :py:class:`~pandas.TimedeltaIndex`. :py:class:`datetime.timedelta` objects are thus accordingly cast to ``timedelta64[ns]`` objects when appropriate. - Masked arrays are now properly coerced to use ``NaN`` as a sentinel value (:issue:`259`). Enhancements ~~~~~~~~~~~~ - Due to popular demand, we have added experimental attribute style access as a shortcut for dataset variables, coordinates and attributes: .. ipython:: python ds = xray.Dataset({'tmin': ([], 25, {'units': 'celcius'})}) ds.tmin.units Tab-completion for these variables should work in editors such as IPython. However, setting variables or attributes in this fashion is not yet supported because there are some unresolved ambiguities (:issue:`300`). - You can now use a dictionary for indexing with labeled dimensions. This provides a safe way to do assignment with labeled dimensions: .. ipython:: python array = xray.DataArray(np.zeros(5), dims=['x']) array[dict(x=slice(3))] = 1 array - Non-index coordinates can now be faithfully written to and restored from netCDF files. This is done according to CF conventions when possible by using the ``coordinates`` attribute on a data variable. When not possible, xray defines a global ``coordinates`` attribute. - Preliminary support for converting ``xray.DataArray`` objects to and from CDAT_ ``cdms2`` variables. - We sped up any operation that involves creating a new Dataset or DataArray (e.g., indexing, aggregation, arithmetic) by a factor of 30 to 50%. The full speed up requires cyordereddict_ to be installed. .. _CDAT: http://uvcdat.llnl.gov/ .. _cyordereddict: https://github.com/shoyer/cyordereddict Bug fixes ~~~~~~~~~ - Fix for ``to_dataframe()`` with 0d string/object coordinates (:issue:`287`) - Fix for ``to_netcdf`` with 0d string variable (:issue:`284`) - Fix writing datetime64 arrays to netcdf if NaT is present (:issue:`270`) - Fix align silently upcasts data arrays when NaNs are inserted (:issue:`264`) Future plans ~~~~~~~~~~~~ - I am contemplating switching to the terms "coordinate variables" and "data variables" instead of the (currently used) "coordinates" and "variables", following their use in `CF Conventions`_ (:issue:`293`). This would mostly have implications for the documentation, but I would also change the ``Dataset`` attribute ``vars`` to ``data``. - I no longer certain that automatic label alignment for arithmetic would be a good idea for xray -- it is a feature from pandas that I have not missed (:issue:`186`). - The main API breakage that I *do* anticipate in the next release is finally making all aggregation operations skip missing values by default (:issue:`130`). I'm pretty sick of writing ``ds.reduce(np.nanmean, 'time')``. - The next version of xray (0.4) will remove deprecated features and aliases whose use currently raises a warning. If you have opinions about any of these anticipated changes, I would love to hear them -- please add a note to any of the referenced GitHub issues. .. _CF Conventions: http://cfconventions.org/Data/cf-conventions/cf-conventions-1.6/build/cf-conventions.html v0.3.1 (22 October, 2014) ------------------------- This is mostly a bug-fix release to make xray compatible with the latest release of pandas (v0.15). We added several features to better support working with missing values and exporting xray objects to pandas. We also reorganized the internal API for serializing and deserializing datasets, but this change should be almost entirely transparent to users. Other than breaking the experimental DataStore API, there should be no backwards incompatible changes. New features ~~~~~~~~~~~~ - Added :py:meth:`~xray.Dataset.count` and :py:meth:`~xray.Dataset.dropna` methods, copied from pandas, for working with missing values (:issue:`247`, :issue:`58`). - Added :py:meth:`DataArray.to_pandas ` for converting a data array into the pandas object with the same dimensionality (1D to Series, 2D to DataFrame, etc.) (:issue:`255`). - Support for reading gzipped netCDF3 files (:issue:`239`). - Reduced memory usage when writing netCDF files (:issue:`251`). - 'missing_value' is now supported as an alias for the '_FillValue' attribute on netCDF variables (:issue:`245`). - Trivial indexes, equivalent to ``range(n)`` where ``n`` is the length of the dimension, are no longer written to disk (:issue:`245`). Bug fixes ~~~~~~~~~ - Compatibility fixes for pandas v0.15 (:issue:`262`). - Fixes for display and indexing of ``NaT`` (not-a-time) (:issue:`238`, :issue:`240`) - Fix slicing by label was an argument is a data array (:issue:`250`). - Test data is now shipped with the source distribution (:issue:`253`). - Ensure order does not matter when doing arithmetic with scalar data arrays (:issue:`254`). - Order of dimensions preserved with ``DataArray.to_dataframe`` (:issue:`260`). v0.3 (21 September 2014) ------------------------ New features ~~~~~~~~~~~~ - **Revamped coordinates**: "coordinates" now refer to all arrays that are not used to index a dimension. Coordinates are intended to allow for keeping track of arrays of metadata that describe the grid on which the points in "variable" arrays lie. They are preserved (when unambiguous) even though mathematical operations. - **Dataset math** :py:class:`~xray.Dataset` objects now support all arithmetic operations directly. Dataset-array operations map across all dataset variables; dataset-dataset operations act on each pair of variables with the same name. - **GroupBy math**: This provides a convenient shortcut for normalizing by the average value of a group. - The dataset ``__repr__`` method has been entirely overhauled; dataset objects now show their values when printed. - You can now index a dataset with a list of variables to return a new dataset: ``ds[['foo', 'bar']]``. Backwards incompatible changes ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - ``Dataset.__eq__`` and ``Dataset.__ne__`` are now element-wise operations instead of comparing all values to obtain a single boolean. Use the method :py:meth:`~xray.Dataset.equals` instead. Deprecations ~~~~~~~~~~~~ - ``Dataset.noncoords`` is deprecated: use ``Dataset.vars`` instead. - ``Dataset.select_vars`` deprecated: index a ``Dataset`` with a list of variable names instead. - ``DataArray.select_vars`` and ``DataArray.drop_vars`` deprecated: use :py:meth:`~xray.DataArray.reset_coords` instead. v0.2 (14 August 2014) --------------------- This is major release that includes some new features and quite a few bug fixes. Here are the highlights: - There is now a direct constructor for ``DataArray`` objects, which makes it possible to create a DataArray without using a Dataset. This is highlighted in the refreshed :doc:`tutorial`. - You can perform aggregation operations like ``mean`` directly on :py:class:`~xray.Dataset` objects, thanks to Joe Hamman. These aggregation methods also worked on grouped datasets. - xray now works on Python 2.6, thanks to Anna Kuznetsova. - A number of methods and attributes were given more sensible (usually shorter) names: ``labeled`` -> ``sel``, ``indexed`` -> ``isel``, ``select`` -> ``select_vars``, ``unselect`` -> ``drop_vars``, ``dimensions`` -> ``dims``, ``coordinates`` -> ``coords``, ``attributes`` -> ``attrs``. - New :py:meth:`~xray.Dataset.load_data` and :py:meth:`~xray.Dataset.close` methods for datasets facilitate lower level of control of data loaded from disk. v0.1.1 (20 May 2014) -------------------- xray 0.1.1 is a bug-fix release that includes changes that should be almost entirely backwards compatible with v0.1: - Python 3 support (:issue:`53`) - Required numpy version relaxed to 1.7 (:issue:`129`) - Return numpy.datetime64 arrays for non-standard calendars (:issue:`126`) - Support for opening datasets associated with NetCDF4 groups (:issue:`127`) - Bug-fixes for concatenating datetime arrays (:issue:`134`) Special thanks to new contributors Thomas Kluyver, Joe Hamman and Alistair Miles. v0.1 (2 May 2014) ----------------- Initial release. �����������������������������������������������������������������������������������������������xarray-0.9.1/doc/why-xarray.rst���������������������������������������������������������������������0000644�1311115�0011610�00000010402�12652753364�016553� 0����������������������������������������������������������������������������������������������������ustar �shoyer��������������������������eng�����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������Overview: Why xarray? ===================== Features -------- Adding dimensions names and coordinate indexes to numpy's ndarray_ makes many powerful array operations possible: - Apply operations over dimensions by name: ``x.sum('time')``. - Select values by label instead of integer location: ``x.loc['2014-01-01']`` or ``x.sel(time='2014-01-01')``. - Mathematical operations (e.g., ``x - y``) vectorize across multiple dimensions (array broadcasting) based on dimension names, not shape. - Flexible split-apply-combine operations with groupby: ``x.groupby('time.dayofyear').mean()``. - Database like alignment based on coordinate labels that smoothly handles missing values: ``x, y = xr.align(x, y, join='outer')``. - Keep track of arbitrary metadata in the form of a Python dictionary: ``x.attrs``. pandas_ provides many of these features, but it does not make use of dimension names, and its core data structures are fixed dimensional arrays. The N-dimensional nature of xarray's data structures makes it suitable for dealing with multi-dimensional scientific data, and its use of dimension names instead of axis labels (``dim='time'`` instead of ``axis=0``) makes such arrays much more manageable than the raw numpy ndarray: with xarray, you don't need to keep track of the order of arrays dimensions or insert dummy dimensions (e.g., ``np.newaxis``) to align arrays. Core data structures -------------------- xarray has two core data structures. Both are fundamentally N-dimensional: - :py:class:`~xarray.DataArray` is our implementation of a labeled, N-dimensional array. It is an N-D generalization of a :py:class:`pandas.Series`. The name ``DataArray`` itself is borrowed from Fernando Perez's datarray_ project, which prototyped a similar data structure. - :py:class:`~xarray.Dataset` is a multi-dimensional, in-memory array database. It is a dict-like container of ``DataArray`` objects aligned along any number of shared dimensions, and serves a similar purpose in xarray to the :py:class:`pandas.DataFrame`. .. _datarray: https://github.com/fperez/datarray The value of attaching labels to numpy's :py:class:`numpy.ndarray` may be fairly obvious, but the dataset may need more motivation. The power of the dataset over a plain dictionary is that, in addition to pulling out arrays by name, it is possible to select or combine data along a dimension across all arrays simultaneously. Like a :py:class:`~pandas.DataFrame`, datasets facilitate array operations with heterogeneous data -- the difference is that the arrays in a dataset can not only have different data types, but can also have different numbers of dimensions. This data model is borrowed from the netCDF_ file format, which also provides xarray with a natural and portable serialization format. NetCDF is very popular in the geosciences, and there are existing libraries for reading and writing netCDF in many programming languages, including Python. xarray distinguishes itself from many tools for working with netCDF data in-so-far as it provides data structures for in-memory analytics that both utilize and preserve labels. You only need to do the tedious work of adding metadata once, not every time you save a file. Goals and aspirations --------------------- pandas_ excels at working with tabular data. That suffices for many statistical analyses, but physical scientists rely on N-dimensional arrays -- which is where xarray comes in. xarray aims to provide a data analysis toolkit as powerful as pandas_ but designed for working with homogeneous N-dimensional arrays instead of tabular data. When possible, we copy the pandas API and rely on pandas's highly optimized internals (in particular, for fast indexing). Importantly, xarray has robust support for converting its objects to and from a numpy ``ndarray`` or a pandas ``DataFrame`` or ``Series``, providing compatibility with the full `PyData ecosystem `__. Our target audience is anyone who needs N-dimensional labeled arrays, but we are particularly focused on the data analysis needs of physical scientists -- especially geoscientists who already know and love netCDF_. .. _ndarray: http://docs.scipy.org/doc/numpy/reference/arrays.ndarray.html .. _netCDF: http://www.unidata.ucar.edu/software/netcdf .. _pandas: http://pandas.pydata.org ��������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������xarray-0.9.1/LICENSE��������������������������������������������������������������������������������0000644�1311115�0011610�00000024041�12652753364�014152� 0����������������������������������������������������������������������������������������������������ustar �shoyer��������������������������eng�����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. 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See the License for the specific language governing permissions and limitations under the License. �����������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������xarray-0.9.1/licenses/������������������������������������������������������������������������������0000755�1311115�0011610�00000000000�13043675421�014741� 5����������������������������������������������������������������������������������������������������ustar �shoyer��������������������������eng�����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������xarray-0.9.1/licenses/NUMPY_LICENSE�����������������������������������������������������������������0000644�1311115�0011610�00000003007�12652753364�016746� 0����������������������������������������������������������������������������������������������������ustar �shoyer��������������������������eng�����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������Copyright (c) 2005-2011, NumPy Developers. 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IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. �������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������xarray-0.9.1/licenses/PANDAS_LICENSE����������������������������������������������������������������0000644�1311115�0011610�00000003216�12652753364�017006� 0����������������������������������������������������������������������������������������������������ustar �shoyer��������������������������eng�����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������pandas license ============== Copyright (c) 2011-2012, Lambda Foundry, Inc. and PyData Development Team All rights reserved. Copyright (c) 2008-2011 AQR Capital Management, LLC All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * Neither the name of the copyright holder nor the names of any contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDER AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. ����������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������xarray-0.9.1/licenses/PYTHON_LICENSE����������������������������������������������������������������0000644�1311115�0011610�00000030730�12652753364�017062� 0����������������������������������������������������������������������������������������������������ustar �shoyer��������������������������eng�����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������A. HISTORY OF THE SOFTWARE ========================== Python was created in the early 1990s by Guido van Rossum at Stichting Mathematisch Centrum (CWI, see http://www.cwi.nl) in the Netherlands as a successor of a language called ABC. Guido remains Python's principal author, although it includes many contributions from others. In 1995, Guido continued his work on Python at the Corporation for National Research Initiatives (CNRI, see http://www.cnri.reston.va.us) in Reston, Virginia where he released several versions of the software. In May 2000, Guido and the Python core development team moved to BeOpen.com to form the BeOpen PythonLabs team. In October of the same year, the PythonLabs team moved to Digital Creations (now Zope Corporation, see http://www.zope.com). In 2001, the Python Software Foundation (PSF, see http://www.python.org/psf/) was formed, a non-profit organization created specifically to own Python-related Intellectual Property. Zope Corporation is a sponsoring member of the PSF. All Python releases are Open Source (see http://www.opensource.org for the Open Source Definition). Historically, most, but not all, Python releases have also been GPL-compatible; the table below summarizes the various releases. Release Derived Year Owner GPL- from compatible? (1) 0.9.0 thru 1.2 1991-1995 CWI yes 1.3 thru 1.5.2 1.2 1995-1999 CNRI yes 1.6 1.5.2 2000 CNRI no 2.0 1.6 2000 BeOpen.com no 1.6.1 1.6 2001 CNRI yes (2) 2.1 2.0+1.6.1 2001 PSF no 2.0.1 2.0+1.6.1 2001 PSF yes 2.1.1 2.1+2.0.1 2001 PSF yes 2.1.2 2.1.1 2002 PSF yes 2.1.3 2.1.2 2002 PSF yes 2.2 and above 2.1.1 2001-now PSF yes Footnotes: (1) GPL-compatible doesn't mean that we're distributing Python under the GPL. All Python licenses, unlike the GPL, let you distribute a modified version without making your changes open source. The GPL-compatible licenses make it possible to combine Python with other software that is released under the GPL; the others don't. (2) According to Richard Stallman, 1.6.1 is not GPL-compatible, because its license has a choice of law clause. According to CNRI, however, Stallman's lawyer has told CNRI's lawyer that 1.6.1 is "not incompatible" with the GPL. Thanks to the many outside volunteers who have worked under Guido's direction to make these releases possible. B. TERMS AND CONDITIONS FOR ACCESSING OR OTHERWISE USING PYTHON =============================================================== PYTHON SOFTWARE FOUNDATION LICENSE VERSION 2 -------------------------------------------- 1. This LICENSE AGREEMENT is between the Python Software Foundation ("PSF"), and the Individual or Organization ("Licensee") accessing and otherwise using this software ("Python") in source or binary form and its associated documentation. 2. Subject to the terms and conditions of this License Agreement, PSF hereby grants Licensee a nonexclusive, royalty-free, world-wide license to reproduce, analyze, test, perform and/or display publicly, prepare derivative works, distribute, and otherwise use Python alone or in any derivative version, provided, however, that PSF's License Agreement and PSF's notice of copyright, i.e., "Copyright (c) 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015 Python Software Foundation; All Rights Reserved" are retained in Python alone or in any derivative version prepared by Licensee. 3. In the event Licensee prepares a derivative work that is based on or incorporates Python or any part thereof, and wants to make the derivative work available to others as provided herein, then Licensee hereby agrees to include in any such work a brief summary of the changes made to Python. 4. PSF is making Python available to Licensee on an "AS IS" basis. PSF MAKES NO REPRESENTATIONS OR WARRANTIES, EXPRESS OR IMPLIED. BY WAY OF EXAMPLE, BUT NOT LIMITATION, PSF MAKES NO AND DISCLAIMS ANY REPRESENTATION OR WARRANTY OF MERCHANTABILITY OR FITNESS FOR ANY PARTICULAR PURPOSE OR THAT THE USE OF PYTHON WILL NOT INFRINGE ANY THIRD PARTY RIGHTS. 5. PSF SHALL NOT BE LIABLE TO LICENSEE OR ANY OTHER USERS OF PYTHON FOR ANY INCIDENTAL, SPECIAL, OR CONSEQUENTIAL DAMAGES OR LOSS AS A RESULT OF MODIFYING, DISTRIBUTING, OR OTHERWISE USING PYTHON, OR ANY DERIVATIVE THEREOF, EVEN IF ADVISED OF THE POSSIBILITY THEREOF. 6. This License Agreement will automatically terminate upon a material breach of its terms and conditions. 7. Nothing in this License Agreement shall be deemed to create any relationship of agency, partnership, or joint venture between PSF and Licensee. This License Agreement does not grant permission to use PSF trademarks or trade name in a trademark sense to endorse or promote products or services of Licensee, or any third party. 8. By copying, installing or otherwise using Python, Licensee agrees to be bound by the terms and conditions of this License Agreement. BEOPEN.COM LICENSE AGREEMENT FOR PYTHON 2.0 ------------------------------------------- BEOPEN PYTHON OPEN SOURCE LICENSE AGREEMENT VERSION 1 1. This LICENSE AGREEMENT is between BeOpen.com ("BeOpen"), having an office at 160 Saratoga Avenue, Santa Clara, CA 95051, and the Individual or Organization ("Licensee") accessing and otherwise using this software in source or binary form and its associated documentation ("the Software"). 2. Subject to the terms and conditions of this BeOpen Python License Agreement, BeOpen hereby grants Licensee a non-exclusive, royalty-free, world-wide license to reproduce, analyze, test, perform and/or display publicly, prepare derivative works, distribute, and otherwise use the Software alone or in any derivative version, provided, however, that the BeOpen Python License is retained in the Software, alone or in any derivative version prepared by Licensee. 3. BeOpen is making the Software available to Licensee on an "AS IS" basis. BEOPEN MAKES NO REPRESENTATIONS OR WARRANTIES, EXPRESS OR IMPLIED. BY WAY OF EXAMPLE, BUT NOT LIMITATION, BEOPEN MAKES NO AND DISCLAIMS ANY REPRESENTATION OR WARRANTY OF MERCHANTABILITY OR FITNESS FOR ANY PARTICULAR PURPOSE OR THAT THE USE OF THE SOFTWARE WILL NOT INFRINGE ANY THIRD PARTY RIGHTS. 4. BEOPEN SHALL NOT BE LIABLE TO LICENSEE OR ANY OTHER USERS OF THE SOFTWARE FOR ANY INCIDENTAL, SPECIAL, OR CONSEQUENTIAL DAMAGES OR LOSS AS A RESULT OF USING, MODIFYING OR DISTRIBUTING THE SOFTWARE, OR ANY DERIVATIVE THEREOF, EVEN IF ADVISED OF THE POSSIBILITY THEREOF. 5. This License Agreement will automatically terminate upon a material breach of its terms and conditions. 6. This License Agreement shall be governed by and interpreted in all respects by the law of the State of California, excluding conflict of law provisions. Nothing in this License Agreement shall be deemed to create any relationship of agency, partnership, or joint venture between BeOpen and Licensee. This License Agreement does not grant permission to use BeOpen trademarks or trade names in a trademark sense to endorse or promote products or services of Licensee, or any third party. As an exception, the "BeOpen Python" logos available at http://www.pythonlabs.com/logos.html may be used according to the permissions granted on that web page. 7. By copying, installing or otherwise using the software, Licensee agrees to be bound by the terms and conditions of this License Agreement. CNRI LICENSE AGREEMENT FOR PYTHON 1.6.1 --------------------------------------- 1. This LICENSE AGREEMENT is between the Corporation for National Research Initiatives, having an office at 1895 Preston White Drive, Reston, VA 20191 ("CNRI"), and the Individual or Organization ("Licensee") accessing and otherwise using Python 1.6.1 software in source or binary form and its associated documentation. 2. Subject to the terms and conditions of this License Agreement, CNRI hereby grants Licensee a nonexclusive, royalty-free, world-wide license to reproduce, analyze, test, perform and/or display publicly, prepare derivative works, distribute, and otherwise use Python 1.6.1 alone or in any derivative version, provided, however, that CNRI's License Agreement and CNRI's notice of copyright, i.e., "Copyright (c) 1995-2001 Corporation for National Research Initiatives; All Rights Reserved" are retained in Python 1.6.1 alone or in any derivative version prepared by Licensee. Alternately, in lieu of CNRI's License Agreement, Licensee may substitute the following text (omitting the quotes): "Python 1.6.1 is made available subject to the terms and conditions in CNRI's License Agreement. This Agreement together with Python 1.6.1 may be located on the Internet using the following unique, persistent identifier (known as a handle): 1895.22/1013. This Agreement may also be obtained from a proxy server on the Internet using the following URL: http://hdl.handle.net/1895.22/1013". 3. In the event Licensee prepares a derivative work that is based on or incorporates Python 1.6.1 or any part thereof, and wants to make the derivative work available to others as provided herein, then Licensee hereby agrees to include in any such work a brief summary of the changes made to Python 1.6.1. 4. CNRI is making Python 1.6.1 available to Licensee on an "AS IS" basis. CNRI MAKES NO REPRESENTATIONS OR WARRANTIES, EXPRESS OR IMPLIED. BY WAY OF EXAMPLE, BUT NOT LIMITATION, CNRI MAKES NO AND DISCLAIMS ANY REPRESENTATION OR WARRANTY OF MERCHANTABILITY OR FITNESS FOR ANY PARTICULAR PURPOSE OR THAT THE USE OF PYTHON 1.6.1 WILL NOT INFRINGE ANY THIRD PARTY RIGHTS. 5. CNRI SHALL NOT BE LIABLE TO LICENSEE OR ANY OTHER USERS OF PYTHON 1.6.1 FOR ANY INCIDENTAL, SPECIAL, OR CONSEQUENTIAL DAMAGES OR LOSS AS A RESULT OF MODIFYING, DISTRIBUTING, OR OTHERWISE USING PYTHON 1.6.1, OR ANY DERIVATIVE THEREOF, EVEN IF ADVISED OF THE POSSIBILITY THEREOF. 6. This License Agreement will automatically terminate upon a material breach of its terms and conditions. 7. This License Agreement shall be governed by the federal intellectual property law of the United States, including without limitation the federal copyright law, and, to the extent such U.S. federal law does not apply, by the law of the Commonwealth of Virginia, excluding Virginia's conflict of law provisions. Notwithstanding the foregoing, with regard to derivative works based on Python 1.6.1 that incorporate non-separable material that was previously distributed under the GNU General Public License (GPL), the law of the Commonwealth of Virginia shall govern this License Agreement only as to issues arising under or with respect to Paragraphs 4, 5, and 7 of this License Agreement. Nothing in this License Agreement shall be deemed to create any relationship of agency, partnership, or joint venture between CNRI and Licensee. This License Agreement does not grant permission to use CNRI trademarks or trade name in a trademark sense to endorse or promote products or services of Licensee, or any third party. 8. By clicking on the "ACCEPT" button where indicated, or by copying, installing or otherwise using Python 1.6.1, Licensee agrees to be bound by the terms and conditions of this License Agreement. ACCEPT CWI LICENSE AGREEMENT FOR PYTHON 0.9.0 THROUGH 1.2 -------------------------------------------------- Copyright (c) 1991 - 1995, Stichting Mathematisch Centrum Amsterdam, The Netherlands. All rights reserved. Permission to use, copy, modify, and distribute this software and its documentation for any purpose and without fee is hereby granted, provided that the above copyright notice appear in all copies and that both that copyright notice and this permission notice appear in supporting documentation, and that the name of Stichting Mathematisch Centrum or CWI not be used in advertising or publicity pertaining to distribution of the software without specific, written prior permission. STICHTING MATHEMATISCH CENTRUM DISCLAIMS ALL WARRANTIES WITH REGARD TO THIS SOFTWARE, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS, IN NO EVENT SHALL STICHTING MATHEMATISCH CENTRUM BE LIABLE FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE.����������������������������������������xarray-0.9.1/licenses/SEABORN_LICENSE���������������������������������������������������������������0000644�1311115�0011610�00000002732�12652753364�017133� 0����������������������������������������������������������������������������������������������������ustar �shoyer��������������������������eng�����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������Copyright (c) 2012-2013, Michael L. Waskom All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * Neither the name of the {organization} nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. ��������������������������������������xarray-0.9.1/MANIFEST.in����������������������������������������������������������������������������0000644�1311115�0011610�00000000152�13043511243�014657� 0����������������������������������������������������������������������������������������������������ustar �shoyer��������������������������eng�����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������include LICENSE recursive-include licenses * recursive-include doc * prune doc/_build prune doc/generated ����������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������xarray-0.9.1/PKG-INFO�������������������������������������������������������������������������������0000644�1311115�0011610�00000004040�13043675421�014227� 0����������������������������������������������������������������������������������������������������ustar �shoyer��������������������������eng�����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������Metadata-Version: 1.1 Name: xarray Version: 0.9.1 Summary: N-D labeled arrays and datasets in Python Home-page: https://github.com/pydata/xarray Author: xarray Developers Author-email: xarray@googlegroups.com License: Apache Description: **xarray** (formerly **xray**) is an open source project and Python package that aims to bring the labeled data power of pandas_ to the physical sciences, by providing N-dimensional variants of the core pandas data structures. Our goal is to provide a pandas-like and pandas-compatible toolkit for analytics on multi-dimensional arrays, rather than the tabular data for which pandas excels. Our approach adopts the `Common Data Model`_ for self- describing scientific data in widespread use in the Earth sciences: ``xarray.Dataset`` is an in-memory representation of a netCDF file. .. _pandas: http://pandas.pydata.org .. _Common Data Model: http://www.unidata.ucar.edu/software/thredds/current/netcdf-java/CDM .. _netCDF: http://www.unidata.ucar.edu/software/netcdf .. _OPeNDAP: http://www.opendap.org/ Important links --------------- - HTML documentation: http://xarray.pydata.org - Issue tracker: http://github.com/pydata/xarray/issues - Source code: http://github.com/pydata/xarray - SciPy2015 talk: https://www.youtube.com/watch?v=X0pAhJgySxk Platform: UNKNOWN Classifier: Development Status :: 4 - Beta Classifier: License :: OSI Approved :: Apache Software License Classifier: Operating System :: OS Independent Classifier: Intended Audience :: Science/Research Classifier: Programming Language :: Python Classifier: Programming Language :: Python :: 2 Classifier: Programming Language :: Python :: 2.7 Classifier: Programming Language :: Python :: 3 Classifier: Programming Language :: Python :: 3.4 Classifier: Programming Language :: Python :: 3.5 Classifier: Programming Language :: Python :: 3.6 Classifier: Topic :: Scientific/Engineering ������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������xarray-0.9.1/README.rst�����������������������������������������������������������������������������0000644�1311115�0011610�00000012540�12755117657�014640� 0����������������������������������������������������������������������������������������������������ustar �shoyer��������������������������eng�����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������xarray: N-D labeled arrays and datasets ======================================= .. image:: https://travis-ci.org/pydata/xarray.svg?branch=master :target: https://travis-ci.org/pydata/xarray .. image:: https://ci.appveyor.com/api/projects/status/github/pydata/xarray?svg=true&passingText=passing&failingText=failing&pendingText=pending :target: https://ci.appveyor.com/project/shoyer/xray .. image:: https://coveralls.io/repos/pydata/xarray/badge.svg :target: https://coveralls.io/r/pydata/xarray .. image:: https://img.shields.io/pypi/v/xarray.svg :target: https://pypi.python.org/pypi/xarray/ .. image:: https://badges.gitter.im/Join%20Chat.svg :target: https://gitter.im/pydata/xarray .. image:: https://zenodo.org/badge/doi/10.5281/zenodo.59499.svg :target: http://dx.doi.org/10.5281/zenodo.59499 **xarray** (formerly **xray**) is an open source project and Python package that aims to bring the labeled data power of pandas_ to the physical sciences, by providing N-dimensional variants of the core pandas data structures. Our goal is to provide a pandas-like and pandas-compatible toolkit for analytics on multi-dimensional arrays, rather than the tabular data for which pandas excels. Our approach adopts the `Common Data Model`_ for self- describing scientific data in widespread use in the Earth sciences: ``xarray.Dataset`` is an in-memory representation of a netCDF file. .. _pandas: http://pandas.pydata.org .. _Common Data Model: http://www.unidata.ucar.edu/software/thredds/current/netcdf-java/CDM .. _netCDF: http://www.unidata.ucar.edu/software/netcdf .. _OPeNDAP: http://www.opendap.org/ Why xarray? ----------- Adding dimensions names and coordinate indexes to numpy's ndarray_ makes many powerful array operations possible: - Apply operations over dimensions by name: ``x.sum('time')``. - Select values by label instead of integer location: ``x.loc['2014-01-01']`` or ``x.sel(time='2014-01-01')``. - Mathematical operations (e.g., ``x - y``) vectorize across multiple dimensions (array broadcasting) based on dimension names, not shape. - Flexible split-apply-combine operations with groupby: ``x.groupby('time.dayofyear').mean()``. - Database like alignment based on coordinate labels that smoothly handles missing values: ``x, y = xr.align(x, y, join='outer')``. - Keep track of arbitrary metadata in the form of a Python dictionary: ``x.attrs``. pandas_ provides many of these features, but it does not make use of dimension names, and its core data structures are fixed dimensional arrays. Why isn't pandas enough? ------------------------ pandas_ excels at working with tabular data. That suffices for many statistical analyses, but physical scientists rely on N-dimensional arrays -- which is where xarray comes in. xarray aims to provide a data analysis toolkit as powerful as pandas_ but designed for working with homogeneous N-dimensional arrays instead of tabular data. When possible, we copy the pandas API and rely on pandas's highly optimized internals (in particular, for fast indexing). Why netCDF? ----------- Because xarray implements the same data model as the netCDF_ file format, xarray datasets have a natural and portable serialization format. But it is also easy to robustly convert an xarray ``DataArray`` to and from a numpy ``ndarray`` or a pandas ``DataFrame`` or ``Series``, providing compatibility with the full `PyData ecosystem `__. Our target audience is anyone who needs N-dimensional labeled arrays, but we are particularly focused on the data analysis needs of physical scientists -- especially geoscientists who already know and love netCDF_. .. _ndarray: http://docs.scipy.org/doc/numpy/reference/arrays.ndarray.html .. _pandas: http://pandas.pydata.org .. _netCDF: http://www.unidata.ucar.edu/software/netcdf Documentation ------------- The official documentation is hosted on ReadTheDocs at http://xarray.pydata.org/ Get in touch ------------ - Ask usage questions on `StackOverflow`_. - Report bugs, suggest features or view the source code `on GitHub`_. - For less well defined questions or ideas, use the `mailing list`_. - You can also try our chatroom `on Gitter`_. .. _StackOverFlow: http://stackoverflow.com/questions/tagged/python-xarray .. _mailing list: https://groups.google.com/forum/#!forum/xarray .. _on Gitter: https://gitter.im/pydata/xarray .. _on GitHub: http://github.com/pydata/xarray History ------- xarray is an evolution of an internal tool developed at `The Climate Corporation`__. It was originally written by Climate Corp researchers Stephan Hoyer, Alex Kleeman and Eugene Brevdo and was released as open source in May 2014. The project was renamed from "xray" in January 2016. __ http://climate.com/ License ------- Copyright 2014-2016, xarray Developers Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. xarray includes portions of pandas, NumPy, Seaborn and Python itself. These licenses are included in the licenses directory. ����������������������������������������������������������������������������������������������������������������������������������������������������������������xarray-0.9.1/setup.cfg������������������������������������������������������������������������������0000644�1311115�0011610�00000000172�13043675421�014755� 0����������������������������������������������������������������������������������������������������ustar �shoyer��������������������������eng�����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������[wheel] universal = 1 [tool:pytest] python_files = test_*.py [egg_info] tag_svn_revision = 0 tag_date = 0 tag_build = ������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������xarray-0.9.1/setup.py�������������������������������������������������������������������������������0000644�1311115�0011610�00000010737�13043675354�014663� 0����������������������������������������������������������������������������������������������������ustar �shoyer��������������������������eng�����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������#!/usr/bin/env python import os import re import sys import warnings from setuptools import setup, find_packages from setuptools import Command MAJOR = 0 MINOR = 9 MICRO = 1 ISRELEASED = True VERSION = '%d.%d.%d' % (MAJOR, MINOR, MICRO) QUALIFIER = '' DISTNAME = 'xarray' LICENSE = 'Apache' AUTHOR = 'xarray Developers' AUTHOR_EMAIL = 'xarray@googlegroups.com' URL = 'https://github.com/pydata/xarray' CLASSIFIERS = [ 'Development Status :: 4 - Beta', 'License :: OSI Approved :: Apache Software License', 'Operating System :: OS Independent', 'Intended Audience :: Science/Research', 'Programming Language :: Python', 'Programming Language :: Python :: 2', 'Programming Language :: Python :: 2.7', 'Programming Language :: Python :: 3', 'Programming Language :: Python :: 3.4', 'Programming Language :: Python :: 3.5', 'Programming Language :: Python :: 3.6', 'Topic :: Scientific/Engineering', ] INSTALL_REQUIRES = ['numpy >= 1.7', 'pandas >= 0.15.0'] TESTS_REQUIRE = ['pytest >= 2.7.1'] DESCRIPTION = "N-D labeled arrays and datasets in Python" LONG_DESCRIPTION = """ **xarray** (formerly **xray**) is an open source project and Python package that aims to bring the labeled data power of pandas_ to the physical sciences, by providing N-dimensional variants of the core pandas data structures. Our goal is to provide a pandas-like and pandas-compatible toolkit for analytics on multi-dimensional arrays, rather than the tabular data for which pandas excels. Our approach adopts the `Common Data Model`_ for self- describing scientific data in widespread use in the Earth sciences: ``xarray.Dataset`` is an in-memory representation of a netCDF file. .. _pandas: http://pandas.pydata.org .. _Common Data Model: http://www.unidata.ucar.edu/software/thredds/current/netcdf-java/CDM .. _netCDF: http://www.unidata.ucar.edu/software/netcdf .. _OPeNDAP: http://www.opendap.org/ Important links --------------- - HTML documentation: http://xarray.pydata.org - Issue tracker: http://github.com/pydata/xarray/issues - Source code: http://github.com/pydata/xarray - SciPy2015 talk: https://www.youtube.com/watch?v=X0pAhJgySxk """ # code to extract and write the version copied from pandas FULLVERSION = VERSION write_version = True if not ISRELEASED: import subprocess FULLVERSION += '.dev' pipe = None for cmd in ['git', 'git.cmd']: try: pipe = subprocess.Popen( [cmd, "describe", "--always", "--match", "v[0-9]*"], stdout=subprocess.PIPE) (so, serr) = pipe.communicate() if pipe.returncode == 0: break except: pass if pipe is None or pipe.returncode != 0: # no git, or not in git dir if os.path.exists('xarray/version.py'): warnings.warn("WARNING: Couldn't get git revision, using existing xarray/version.py") write_version = False else: warnings.warn("WARNING: Couldn't get git revision, using generic version string") else: # have git, in git dir, but may have used a shallow clone (travis does this) rev = so.strip() # makes distutils blow up on Python 2.7 if sys.version_info[0] >= 3: rev = rev.decode('ascii') if not rev.startswith('v') and re.match("[a-zA-Z0-9]{7,9}", rev): # partial clone, manually construct version string # this is the format before we started using git-describe # to get an ordering on dev version strings. rev = "v%s.dev-%s" % (VERSION, rev) # Strip leading v from tags format "vx.y.z" to get th version string FULLVERSION = rev.lstrip('v') else: FULLVERSION += QUALIFIER def write_version_py(filename=None): cnt = """\ version = '%s' short_version = '%s' """ if not filename: filename = os.path.join( os.path.dirname(__file__), 'xarray', 'version.py') a = open(filename, 'w') try: a.write(cnt % (FULLVERSION, VERSION)) finally: a.close() if write_version: write_version_py() setup(name=DISTNAME, version=FULLVERSION, license=LICENSE, author=AUTHOR, author_email=AUTHOR_EMAIL, classifiers=CLASSIFIERS, description=DESCRIPTION, long_description=LONG_DESCRIPTION, install_requires=INSTALL_REQUIRES, tests_require=TESTS_REQUIRE, url=URL, packages=find_packages(), package_data={'xarray': ['tests/data/*', 'plot/default_colormap.csv']}) ���������������������������������xarray-0.9.1/xarray/��������������������������������������������������������������������������������0000755�1311115�0011610�00000000000�13043675421�014442� 5����������������������������������������������������������������������������������������������������ustar �shoyer��������������������������eng�����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������xarray-0.9.1/xarray/__init__.py���������������������������������������������������������������������0000644�1311115�0011610�00000002320�13043511243�016537� 0����������������������������������������������������������������������������������������������������ustar �shoyer��������������������������eng�����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������from __future__ import absolute_import from __future__ import division from __future__ import print_function from .core.alignment import align, broadcast, broadcast_arrays from .core.common import full_like, zeros_like, ones_like from .core.combine import concat, auto_combine from .core.extensions import (register_dataarray_accessor, register_dataset_accessor) from .core.variable import Variable, IndexVariable, Coordinate from .core.dataset import Dataset from .core.dataarray import DataArray from .core.merge import merge, MergeError from .core.options import set_options from .backends.api import (open_dataset, open_dataarray, open_mfdataset, save_mfdataset) from .conventions import decode_cf try: from .version import version as __version__ except ImportError: # pragma: no cover raise ImportError('xarray not properly installed. If you are running from ' 'the source directory, please instead create a new ' 'virtual environment (using conda or virtualenv) and ' 'then install it in-place by running: pip install -e .') from . import tutorial from . import ufuncs from . import testing ����������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������xarray-0.9.1/xarray/backends/�����������������������������������������������������������������������0000755�1311115�0011610�00000000000�13043675421�016214� 5����������������������������������������������������������������������������������������������������ustar �shoyer��������������������������eng�����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������xarray-0.9.1/xarray/backends/__init__.py������������������������������������������������������������0000644�1311115�0011610�00000000722�12652753364�020336� 0����������������������������������������������������������������������������������������������������ustar �shoyer��������������������������eng�����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������"""Backend objects for saving and loading data DataStores provide a uniform interface for saving and loading data in different formats. They should not be used directly, but rather through Dataset objects. """ from .common import AbstractDataStore from .memory import InMemoryDataStore from .netCDF4_ import NetCDF4DataStore from .pydap_ import PydapDataStore from .pynio_ import NioDataStore from .scipy_ import ScipyDataStore from .h5netcdf_ import H5NetCDFStore ����������������������������������������������xarray-0.9.1/xarray/backends/api.py�����������������������������������������������������������������0000644�1311115�0011610�00000066010�13043511243�017331� 0����������������������������������������������������������������������������������������������������ustar �shoyer��������������������������eng�����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������from __future__ import absolute_import from __future__ import division from __future__ import print_function import gzip import os.path from distutils.version import StrictVersion from glob import glob from io import BytesIO from numbers import Number import numpy as np from .. import backends, conventions from .common import ArrayWriter, GLOBAL_LOCK from ..core import indexing from ..core.combine import auto_combine from ..core.utils import close_on_error, is_remote_uri from ..core.pycompat import basestring DATAARRAY_NAME = '__xarray_dataarray_name__' DATAARRAY_VARIABLE = '__xarray_dataarray_variable__' def _get_default_engine(path, allow_remote=False): if allow_remote and is_remote_uri(path): # pragma: no cover try: import netCDF4 engine = 'netcdf4' except ImportError: try: import pydap engine = 'pydap' except ImportError: raise ValueError('netCDF4 or pydap is required for accessing ' 'remote datasets via OPeNDAP') else: try: import netCDF4 engine = 'netcdf4' except ImportError: # pragma: no cover try: import scipy.io.netcdf engine = 'scipy' except ImportError: raise ValueError('cannot read or write netCDF files without ' 'netCDF4-python or scipy installed') return engine def _normalize_path(path): if is_remote_uri(path): return path else: return os.path.abspath(os.path.expanduser(path)) def _default_lock(filename, engine): if filename.endswith('.gz'): lock = False else: if engine is None: engine = _get_default_engine(filename, allow_remote=True) if engine == 'netcdf4': if is_remote_uri(filename): lock = False else: # TODO: identify netcdf3 files and don't use the global lock # for them lock = GLOBAL_LOCK elif engine in {'h5netcdf', 'pynio'}: lock = GLOBAL_LOCK else: lock = False return lock def _validate_dataset_names(dataset): """DataArray.name and Dataset keys must be a string or None""" def check_name(name): if isinstance(name, basestring): if not name: raise ValueError('Invalid name for DataArray or Dataset key: ' 'string must be length 1 or greater for ' 'serialization to netCDF files') elif name is not None: raise TypeError('DataArray.name or Dataset key must be either a ' 'string or None for serialization to netCDF files') for k in dataset: check_name(k) def _validate_attrs(dataset): """`attrs` must have a string key and a value which is either: a number a string, an ndarray or a list/tuple of numbers/strings. """ def check_attr(name, value): if isinstance(name, basestring): if not name: raise ValueError('Invalid name for attr: string must be length ' '1 or greater for serialization to netCDF ' 'files') else: raise TypeError("Invalid name for attr: {} must be a string for " "serialization to netCDF files".format(name)) if not isinstance(value, (basestring, Number, np.ndarray, np.number, list, tuple)): raise TypeError('Invalid value for attr: {} must be a number ' 'string, ndarray or a list/tuple of numbers/strings ' 'for serialization to netCDF ' 'files'.format(value)) # Check attrs on the dataset itself for k, v in dataset.attrs.items(): check_attr(k, v) # Check attrs on each variable within the dataset for variable in dataset.variables.values(): for k, v in variable.attrs.items(): check_attr(k, v) def _protect_dataset_variables_inplace(dataset, cache): for name, variable in dataset.variables.items(): if name not in variable.dims: # no need to protect IndexVariable objects data = indexing.CopyOnWriteArray(variable._data) if cache: data = indexing.MemoryCachedArray(data) variable.data = data def open_dataset(filename_or_obj, group=None, decode_cf=True, mask_and_scale=True, decode_times=True, concat_characters=True, decode_coords=True, engine=None, chunks=None, lock=None, cache=None, drop_variables=None): """Load and decode a dataset from a file or file-like object. Parameters ---------- filename_or_obj : str, file or xarray.backends.*DataStore Strings are interpreted as a path to a netCDF file or an OpenDAP URL and opened with python-netCDF4, unless the filename ends with .gz, in which case the file is gunzipped and opened with scipy.io.netcdf (only netCDF3 supported). File-like objects are opened with scipy.io.netcdf (only netCDF3 supported). group : str, optional Path to the netCDF4 group in the given file to open (only works for netCDF4 files). decode_cf : bool, optional Whether to decode these variables, assuming they were saved according to CF conventions. mask_and_scale : bool, optional If True, replace array values equal to `_FillValue` with NA and scale values according to the formula `original_values * scale_factor + add_offset`, where `_FillValue`, `scale_factor` and `add_offset` are taken from variable attributes (if they exist). If the `_FillValue` or `missing_value` attribute contains multiple values a warning will be issued and all array values matching one of the multiple values will be replaced by NA. decode_times : bool, optional If True, decode times encoded in the standard NetCDF datetime format into datetime objects. Otherwise, leave them encoded as numbers. concat_characters : bool, optional If True, concatenate along the last dimension of character arrays to form string arrays. Dimensions will only be concatenated over (and removed) if they have no corresponding variable and if they are only used as the last dimension of character arrays. decode_coords : bool, optional If True, decode the 'coordinates' attribute to identify coordinates in the resulting dataset. engine : {'netcdf4', 'scipy', 'pydap', 'h5netcdf', 'pynio'}, optional Engine to use when reading files. If not provided, the default engine is chosen based on available dependencies, with a preference for 'netcdf4'. chunks : int or dict, optional If chunks is provided, it used to load the new dataset into dask arrays. ``chunks={}`` loads the dataset with dask using a single chunk for all arrays. This is an experimental feature; see the documentation for more details. lock : False, True or threading.Lock, optional If chunks is provided, this argument is passed on to :py:func:`dask.array.from_array`. By default, a per-variable lock is used when reading data from netCDF files with the netcdf4 and h5netcdf engines to avoid issues with concurrent access when using dask's multithreaded backend. cache : bool, optional If True, cache data loaded from the underlying datastore in memory as NumPy arrays when accessed to avoid reading from the underlying data- store multiple times. Defaults to True unless you specify the `chunks` argument to use dask, in which case it defaults to False. Does not change the behavior of coordinates corresponding to dimensions, which always load their data from disk into a ``pandas.Index``. drop_variables: string or iterable, optional A variable or list of variables to exclude from being parsed from the dataset. This may be useful to drop variables with problems or inconsistent values. Returns ------- dataset : Dataset The newly created dataset. See Also -------- open_mfdataset """ if not decode_cf: mask_and_scale = False decode_times = False concat_characters = False decode_coords = False if cache is None: cache = chunks is None def maybe_decode_store(store, lock=False): ds = conventions.decode_cf( store, mask_and_scale=mask_and_scale, decode_times=decode_times, concat_characters=concat_characters, decode_coords=decode_coords, drop_variables=drop_variables) _protect_dataset_variables_inplace(ds, cache) if chunks is not None: try: from dask.base import tokenize except ImportError: import dask # raise the usual error if dask is entirely missing if StrictVersion(dask.__version__) < StrictVersion('0.6'): raise ImportError('xarray requires dask version 0.6 or newer') else: raise if (isinstance(filename_or_obj, basestring) and not is_remote_uri(filename_or_obj)): file_arg = os.path.getmtime(filename_or_obj) else: file_arg = filename_or_obj token = tokenize(file_arg, group, decode_cf, mask_and_scale, decode_times, concat_characters, decode_coords, engine, chunks, drop_variables) name_prefix = '%s:%s/' % (filename_or_obj, group or '') ds2 = ds.chunk(chunks, name_prefix=name_prefix, token=token, lock=lock) ds2._file_obj = ds._file_obj else: ds2 = ds return ds2 if isinstance(filename_or_obj, backends.AbstractDataStore): store = filename_or_obj elif isinstance(filename_or_obj, basestring): if (isinstance(filename_or_obj, bytes) and filename_or_obj.startswith(b'\x89HDF')): raise ValueError('cannot read netCDF4/HDF5 file images') elif (isinstance(filename_or_obj, bytes) and filename_or_obj.startswith(b'CDF')): # netCDF3 file images are handled by scipy pass elif isinstance(filename_or_obj, basestring): filename_or_obj = _normalize_path(filename_or_obj) if filename_or_obj.endswith('.gz'): if engine is not None and engine != 'scipy': raise ValueError('can only read gzipped netCDF files with ' "default engine or engine='scipy'") # if the string ends with .gz, then gunzip and open as netcdf file try: store = backends.ScipyDataStore(gzip.open(filename_or_obj)) except TypeError as e: # TODO: gzipped loading only works with NetCDF3 files. if 'is not a valid NetCDF 3 file' in e.message: raise ValueError('gzipped file loading only supports ' 'NetCDF 3 files.') else: raise else: if engine is None: engine = _get_default_engine(filename_or_obj, allow_remote=True) if engine == 'netcdf4': store = backends.NetCDF4DataStore(filename_or_obj, group=group) elif engine == 'scipy': store = backends.ScipyDataStore(filename_or_obj) elif engine == 'pydap': store = backends.PydapDataStore(filename_or_obj) elif engine == 'h5netcdf': store = backends.H5NetCDFStore(filename_or_obj, group=group) elif engine == 'pynio': store = backends.NioDataStore(filename_or_obj) else: raise ValueError('unrecognized engine for open_dataset: %r' % engine) if lock is None: lock = _default_lock(filename_or_obj, engine) with close_on_error(store): return maybe_decode_store(store, lock) else: if engine is not None and engine != 'scipy': raise ValueError('can only read file-like objects with ' "default engine or engine='scipy'") # assume filename_or_obj is a file-like object store = backends.ScipyDataStore(filename_or_obj) return maybe_decode_store(store) def open_dataarray(filename_or_obj, group=None, decode_cf=True, mask_and_scale=True, decode_times=True, concat_characters=True, decode_coords=True, engine=None, chunks=None, lock=None, cache=None, drop_variables=None): """ Opens an DataArray from a netCDF file containing a single data variable. This is designed to read netCDF files with only one data variable. If multiple variables are present then a ValueError is raised. Parameters ---------- filename_or_obj : str, file or xarray.backends.*DataStore Strings are interpreted as a path to a netCDF file or an OpenDAP URL and opened with python-netCDF4, unless the filename ends with .gz, in which case the file is gunzipped and opened with scipy.io.netcdf (only netCDF3 supported). File-like objects are opened with scipy.io.netcdf (only netCDF3 supported). group : str, optional Path to the netCDF4 group in the given file to open (only works for netCDF4 files). decode_cf : bool, optional Whether to decode these variables, assuming they were saved according to CF conventions. mask_and_scale : bool, optional If True, replace array values equal to `_FillValue` with NA and scale values according to the formula `original_values * scale_factor + add_offset`, where `_FillValue`, `scale_factor` and `add_offset` are taken from variable attributes (if they exist). If the `_FillValue` or `missing_value` attribute contains multiple values a warning will be issued and all array values matching one of the multiple values will be replaced by NA. decode_times : bool, optional If True, decode times encoded in the standard NetCDF datetime format into datetime objects. Otherwise, leave them encoded as numbers. concat_characters : bool, optional If True, concatenate along the last dimension of character arrays to form string arrays. Dimensions will only be concatenated over (and removed) if they have no corresponding variable and if they are only used as the last dimension of character arrays. decode_coords : bool, optional If True, decode the 'coordinates' attribute to identify coordinates in the resulting dataset. engine : {'netcdf4', 'scipy', 'pydap', 'h5netcdf', 'pynio'}, optional Engine to use when reading files. If not provided, the default engine is chosen based on available dependencies, with a preference for 'netcdf4'. chunks : int or dict, optional If chunks is provided, it used to load the new dataset into dask arrays. This is an experimental feature; see the documentation for more details. lock : False, True or threading.Lock, optional If chunks is provided, this argument is passed on to :py:func:`dask.array.from_array`. By default, a per-variable lock is used when reading data from netCDF files with the netcdf4 and h5netcdf engines to avoid issues with concurrent access when using dask's multithreaded backend. cache : bool, optional If True, cache data loaded from the underlying datastore in memory as NumPy arrays when accessed to avoid reading from the underlying data- store multiple times. Defaults to True unless you specify the `chunks` argument to use dask, in which case it defaults to False. Does not change the behavior of coordinates corresponding to dimensions, which always load their data from disk into a ``pandas.Index``. drop_variables: string or iterable, optional A variable or list of variables to exclude from being parsed from the dataset. This may be useful to drop variables with problems or inconsistent values. Notes ----- This is designed to be fully compatible with `DataArray.to_netcdf`. Saving using `DataArray.to_netcdf` and then loading with this function will produce an identical result. All parameters are passed directly to `xarray.open_dataset`. See that documentation for further details. See also -------- open_dataset """ dataset = open_dataset(filename_or_obj, group, decode_cf, mask_and_scale, decode_times, concat_characters, decode_coords, engine, chunks, lock, cache, drop_variables) if len(dataset.data_vars) != 1: raise ValueError('Given file dataset contains more than one data ' 'variable. Please read with xarray.open_dataset and ' 'then select the variable you want.') else: data_array, = dataset.data_vars.values() data_array._file_obj = dataset._file_obj # Reset names if they were changed during saving # to ensure that we can 'roundtrip' perfectly if DATAARRAY_NAME in dataset.attrs: data_array.name = dataset.attrs[DATAARRAY_NAME] del dataset.attrs[DATAARRAY_NAME] if data_array.name == DATAARRAY_VARIABLE: data_array.name = None return data_array class _MultiFileCloser(object): def __init__(self, file_objs): self.file_objs = file_objs def close(self): for f in self.file_objs: f.close() _CONCAT_DIM_DEFAULT = '__infer_concat_dim__' def open_mfdataset(paths, chunks=None, concat_dim=_CONCAT_DIM_DEFAULT, compat='no_conflicts', preprocess=None, engine=None, lock=None, **kwargs): """Open multiple files as a single dataset. Experimental. Requires dask to be installed. Parameters ---------- paths : str or sequence Either a string glob in the form "path/to/my/files/*.nc" or an explicit list of files to open. chunks : int or dict, optional Dictionary with keys given by dimension names and values given by chunk sizes. In general, these should divide the dimensions of each dataset. If int, chunk each dimension by ``chunks``. By default, chunks will be chosen to load entire input files into memory at once. This has a major impact on performance: please see the full documentation for more details. concat_dim : None, str, DataArray or Index, optional Dimension to concatenate files along. This argument is passed on to :py:func:`xarray.auto_combine` along with the dataset objects. You only need to provide this argument if the dimension along which you want to concatenate is not a dimension in the original datasets, e.g., if you want to stack a collection of 2D arrays along a third dimension. By default, xarray attempts to infer this argument by examining component files. Set ``concat_dim=None`` explicitly to disable concatenation. compat : {'identical', 'equals', 'broadcast_equals', 'no_conflicts'}, optional String indicating how to compare variables of the same name for potential conflicts when merging: - 'broadcast_equals': all values must be equal when variables are broadcast against each other to ensure common dimensions. - 'equals': all values and dimensions must be the same. - 'identical': all values, dimensions and attributes must be the same. - 'no_conflicts': only values which are not null in both datasets must be equal. The returned dataset then contains the combination of all non-null values. preprocess : callable, optional If provided, call this function on each dataset prior to concatenation. engine : {'netcdf4', 'scipy', 'pydap', 'h5netcdf', 'pynio'}, optional Engine to use when reading files. If not provided, the default engine is chosen based on available dependencies, with a preference for 'netcdf4'. lock : False, True or threading.Lock, optional This argument is passed on to :py:func:`dask.array.from_array`. By default, a per-variable lock is used when reading data from netCDF files with the netcdf4 and h5netcdf engines to avoid issues with concurrent access when using dask's multithreaded backend. **kwargs : optional Additional arguments passed on to :py:func:`xarray.open_dataset`. Returns ------- xarray.Dataset See Also -------- auto_combine open_dataset """ if isinstance(paths, basestring): paths = sorted(glob(paths)) if not paths: raise IOError('no files to open') if lock is None: lock = _default_lock(paths[0], engine) datasets = [open_dataset(p, engine=engine, chunks=chunks or {}, lock=lock, **kwargs) for p in paths] file_objs = [ds._file_obj for ds in datasets] if preprocess is not None: datasets = [preprocess(ds) for ds in datasets] if concat_dim is _CONCAT_DIM_DEFAULT: combined = auto_combine(datasets, compat=compat) else: combined = auto_combine(datasets, concat_dim=concat_dim, compat=compat) combined._file_obj = _MultiFileCloser(file_objs) return combined WRITEABLE_STORES = {'netcdf4': backends.NetCDF4DataStore, 'scipy': backends.ScipyDataStore, 'h5netcdf': backends.H5NetCDFStore} def to_netcdf(dataset, path=None, mode='w', format=None, group=None, engine=None, writer=None, encoding=None, unlimited_dims=None): """This function creates an appropriate datastore for writing a dataset to disk as a netCDF file See `Dataset.to_netcdf` for full API docs. The ``writer`` argument is only for the private use of save_mfdataset. """ if encoding is None: encoding = {} if path is None: path = BytesIO() if engine is None: engine = 'scipy' elif engine is not None: raise ValueError('invalid engine for creating bytes with ' 'to_netcdf: %r. Only the default engine ' "or engine='scipy' is supported" % engine) else: if engine is None: engine = _get_default_engine(path) path = _normalize_path(path) # validate Dataset keys, DataArray names, and attr keys/values _validate_dataset_names(dataset) _validate_attrs(dataset) try: store_cls = WRITEABLE_STORES[engine] except KeyError: raise ValueError('unrecognized engine for to_netcdf: %r' % engine) if format is not None: format = format.upper() # if a writer is provided, store asynchronously sync = writer is None store = store_cls(path, mode, format, group, writer) if unlimited_dims is None: unlimited_dims = dataset.encoding.get('unlimited_dims', None) try: dataset.dump_to_store(store, sync=sync, encoding=encoding, unlimited_dims=unlimited_dims) if isinstance(path, BytesIO): return path.getvalue() finally: if sync: store.close() if not sync: return store def save_mfdataset(datasets, paths, mode='w', format=None, groups=None, engine=None): """Write multiple datasets to disk as netCDF files simultaneously. This function is intended for use with datasets consisting of dask.array objects, in which case it can write the multiple datasets to disk simultaneously using a shared thread pool. When not using dask, it is no different than calling ``to_netcdf`` repeatedly. Parameters ---------- datasets : list of xarray.Dataset List of datasets to save. paths : list of str List of paths to which to save each corresponding dataset. mode : {'w', 'a'}, optional Write ('w') or append ('a') mode. If mode='w', any existing file at these locations will be overwritten. format : {'NETCDF4', 'NETCDF4_CLASSIC', 'NETCDF3_64BIT', 'NETCDF3_CLASSIC'}, optional File format for the resulting netCDF file: * NETCDF4: Data is stored in an HDF5 file, using netCDF4 API features. * NETCDF4_CLASSIC: Data is stored in an HDF5 file, using only netCDF 3 compatible API features. * NETCDF3_64BIT: 64-bit offset version of the netCDF 3 file format, which fully supports 2+ GB files, but is only compatible with clients linked against netCDF version 3.6.0 or later. * NETCDF3_CLASSIC: The classic netCDF 3 file format. It does not handle 2+ GB files very well. All formats are supported by the netCDF4-python library. scipy.io.netcdf only supports the last two formats. The default format is NETCDF4 if you are saving a file to disk and have the netCDF4-python library available. Otherwise, xarray falls back to using scipy to write netCDF files and defaults to the NETCDF3_64BIT format (scipy does not support netCDF4). groups : list of str, optional Paths to the netCDF4 group in each corresponding file to which to save datasets (only works for format='NETCDF4'). The groups will be created if necessary. engine : {'netcdf4', 'scipy', 'h5netcdf'}, optional Engine to use when writing netCDF files. If not provided, the default engine is chosen based on available dependencies, with a preference for 'netcdf4' if writing to a file on disk. Examples -------- Save a dataset into one netCDF per year of data: >>> years, datasets = zip(*ds.groupby('time.year')) >>> paths = ['%s.nc' % y for y in years] >>> xr.save_mfdataset(datasets, paths) """ if mode == 'w' and len(set(paths)) < len(paths): raise ValueError("cannot use mode='w' when writing multiple " 'datasets to the same path') if groups is None: groups = [None] * len(datasets) if len(set([len(datasets), len(paths), len(groups)])) > 1: raise ValueError('must supply lists of the same length for the ' 'datasets, paths and groups arguments to ' 'save_mfdataset') writer = ArrayWriter() stores = [to_netcdf(ds, path, mode, format, group, engine, writer) for ds, path, group in zip(datasets, paths, groups)] try: writer.sync() for store in stores: store.sync() finally: for store in stores: store.close() ������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������xarray-0.9.1/xarray/backends/common.py��������������������������������������������������������������0000644�1311115�0011610�00000017741�13043511243�020057� 0����������������������������������������������������������������������������������������������������ustar �shoyer��������������������������eng�����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������from __future__ import absolute_import from __future__ import division from __future__ import print_function import numpy as np import logging import time import traceback from collections import Mapping from distutils.version import StrictVersion from ..conventions import cf_encoder from ..core.utils import FrozenOrderedDict from ..core.pycompat import iteritems, dask_array_type try: from dask.utils import SerializableLock as Lock except ImportError: from threading import Lock # Create a logger object, but don't add any handlers. Leave that to user code. logger = logging.getLogger(__name__) NONE_VAR_NAME = '__values__' # dask.utils.SerializableLock if available, otherwise just a threading.Lock GLOBAL_LOCK = Lock() def _encode_variable_name(name): if name is None: name = NONE_VAR_NAME return name def _decode_variable_name(name): if name == NONE_VAR_NAME: name = None return name def robust_getitem(array, key, catch=Exception, max_retries=6, initial_delay=500): """ Robustly index an array, using retry logic with exponential backoff if any of the errors ``catch`` are raised. The initial_delay is measured in ms. With the default settings, the maximum delay will be in the range of 32-64 seconds. """ assert max_retries >= 0 for n in range(max_retries + 1): try: return array[key] except catch: if n == max_retries: raise base_delay = initial_delay * 2 ** n next_delay = base_delay + np.random.randint(base_delay) msg = ('getitem failed, waiting %s ms before trying again ' '(%s tries remaining). Full traceback: %s' % (next_delay, max_retries - n, traceback.format_exc())) logger.debug(msg) time.sleep(1e-3 * next_delay) class AbstractDataStore(Mapping): def __iter__(self): return iter(self.variables) def __getitem__(self, key): return self.variables[key] def __len__(self): return len(self.variables) def get_attrs(self): # pragma: no cover raise NotImplementedError def get_variables(self): # pragma: no cover raise NotImplementedError def get_encoding(self): return {} def load(self): """ This loads the variables and attributes simultaneously. A centralized loading function makes it easier to create data stores that do automatic encoding/decoding. For example: class SuffixAppendingDataStore(AbstractDataStore): def load(self): variables, attributes = AbstractDataStore.load(self) variables = {'%s_suffix' % k: v for k, v in iteritems(variables)} attributes = {'%s_suffix' % k: v for k, v in iteritems(attributes)} return variables, attributes This function will be called anytime variables or attributes are requested, so care should be taken to make sure its fast. """ variables = FrozenOrderedDict((_decode_variable_name(k), v) for k, v in iteritems(self.get_variables())) attributes = FrozenOrderedDict(self.get_attrs()) return variables, attributes @property def variables(self): # Because encoding/decoding might happen which may require both the # attributes and the variables, and because a store may be updated # we need to load both the attributes and variables # anytime either one is requested. variables, _ = self.load() return variables @property def attrs(self): # Because encoding/decoding might happen which may require both the # attributes and the variables, and because a store may be updated # we need to load both the attributes and variables # anytime either one is requested. _, attributes = self.load() return attributes @property def dimensions(self): return self.get_dimensions() def close(self): pass def __enter__(self): return self def __exit__(self, exception_type, exception_value, traceback): self.close() class ArrayWriter(object): def __init__(self): self.sources = [] self.targets = [] def add(self, source, target): if isinstance(source, dask_array_type): self.sources.append(source) self.targets.append(target) else: try: target[...] = source except TypeError: # workaround for GH: scipy/scipy#6880 target[:] = source def sync(self): if self.sources: import dask.array as da import dask if StrictVersion(dask.__version__) > StrictVersion('0.8.1'): da.store(self.sources, self.targets, lock=GLOBAL_LOCK) else: da.store(self.sources, self.targets) self.sources = [] self.targets = [] class AbstractWritableDataStore(AbstractDataStore): def __init__(self, writer=None): if writer is None: writer = ArrayWriter() self.writer = writer def set_dimension(self, d, l): # pragma: no cover raise NotImplementedError def set_attribute(self, k, v): # pragma: no cover raise NotImplementedError def set_variable(self, k, v): # pragma: no cover raise NotImplementedError def sync(self): self.writer.sync() def store_dataset(self, dataset): # in stores variables are all variables AND coordinates # in xarray.Dataset variables are variables NOT coordinates, # so here we pass the whole dataset in instead of doing # dataset.variables self.store(dataset, dataset.attrs) def store(self, variables, attributes, check_encoding_set=frozenset(), unlimited_dims=None): self.set_attributes(attributes) self.set_variables(variables, check_encoding_set, unlimited_dims=unlimited_dims) def set_attributes(self, attributes): for k, v in iteritems(attributes): self.set_attribute(k, v) def set_variables(self, variables, check_encoding_set, unlimited_dims=None): for vn, v in iteritems(variables): name = _encode_variable_name(vn) check = vn in check_encoding_set target, source = self.prepare_variable( name, v, check, unlimited_dims=unlimited_dims) self.writer.add(source, target) def set_necessary_dimensions(self, variable, unlimited_dims=None): if unlimited_dims is None: unlimited_dims = set() for d, l in zip(variable.dims, variable.shape): if d not in self.dimensions: if d in unlimited_dims: l = None self.set_dimension(d, l) class WritableCFDataStore(AbstractWritableDataStore): def store(self, variables, attributes, *args, **kwargs): # All NetCDF files get CF encoded by default, without this attempting # to write times, for example, would fail. cf_variables, cf_attrs = cf_encoder(variables, attributes) AbstractWritableDataStore.store(self, cf_variables, cf_attrs, *args, **kwargs) class DataStorePickleMixin(object): """Subclasses must define `ds`, `_opener` and `_mode` attributes. Do not subclass this class: it is not part of xarray's external API. """ def __getstate__(self): state = self.__dict__.copy() del state['ds'] if self._mode == 'w': # file has already been created, don't override when restoring state['_mode'] = 'a' return state def __setstate__(self, state): self.__dict__.update(state) self.ds = self._opener(mode=self._mode) �������������������������������xarray-0.9.1/xarray/backends/h5netcdf_.py�����������������������������������������������������������0000644�1311115�0011610�00000011206�13043511243�020414� 0����������������������������������������������������������������������������������������������������ustar �shoyer��������������������������eng�����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������from __future__ import absolute_import from __future__ import division from __future__ import print_function import functools import warnings from .. import Variable from ..core import indexing from ..core.utils import FrozenOrderedDict, close_on_error, Frozen from ..core.pycompat import iteritems, bytes_type, unicode_type, OrderedDict from .common import WritableCFDataStore, DataStorePickleMixin from .netCDF4_ import (_nc4_group, _nc4_values_and_dtype, _extract_nc4_variable_encoding, BaseNetCDF4Array) def maybe_decode_bytes(txt): if isinstance(txt, bytes_type): return txt.decode('utf-8') else: return txt def _read_attributes(h5netcdf_var): # GH451 # to ensure conventions decoding works properly on Python 3, decode all # bytes attributes to strings attrs = OrderedDict() for k in h5netcdf_var.ncattrs(): v = h5netcdf_var.getncattr(k) if k not in ['_FillValue', 'missing_value']: v = maybe_decode_bytes(v) attrs[k] = v return attrs _extract_h5nc_encoding = functools.partial(_extract_nc4_variable_encoding, lsd_okay=False, backend='h5netcdf') def _open_h5netcdf_group(filename, mode, group): import h5netcdf.legacyapi ds = h5netcdf.legacyapi.Dataset(filename, mode=mode) with close_on_error(ds): return _nc4_group(ds, group, mode) class H5NetCDFStore(WritableCFDataStore, DataStorePickleMixin): """Store for reading and writing data via h5netcdf """ def __init__(self, filename, mode='r', format=None, group=None, writer=None): if format not in [None, 'NETCDF4']: raise ValueError('invalid format for h5netcdf backend') opener = functools.partial(_open_h5netcdf_group, filename, mode=mode, group=group) self.ds = opener() self.format = format self._opener = opener self._filename = filename self._mode = mode super(H5NetCDFStore, self).__init__(writer) def open_store_variable(self, name, var): dimensions = var.dimensions data = indexing.LazilyIndexedArray(BaseNetCDF4Array(name, self)) attrs = _read_attributes(var) # netCDF4 specific encoding encoding = dict(var.filters()) chunking = var.chunking() encoding['chunksizes'] = chunking if chunking != 'contiguous' else None # save source so __repr__ can detect if it's local or not encoding['source'] = self._filename encoding['original_shape'] = var.shape return Variable(dimensions, data, attrs, encoding) def get_variables(self): return FrozenOrderedDict((k, self.open_store_variable(k, v)) for k, v in iteritems(self.ds.variables)) def get_attrs(self): return Frozen(_read_attributes(self.ds)) def get_dimensions(self): return self.ds.dimensions def set_dimension(self, name, length): self.ds.createDimension(name, size=length) def set_attribute(self, key, value): self.ds.setncattr(key, value) def prepare_variable(self, name, variable, check_encoding=False, unlimited_dims=None): import h5py attrs = variable.attrs.copy() variable, dtype = _nc4_values_and_dtype(variable) if dtype is str: dtype = h5py.special_dtype(vlen=unicode_type) if unlimited_dims is not None: warnings.warn('h5netcdf does not support unlimited dimensions, ' 'got: %s.' % unlimited_dims) unlimited_dims = None self.set_necessary_dimensions(variable, unlimited_dims=unlimited_dims) fill_value = attrs.pop('_FillValue', None) if fill_value in ['\x00']: fill_value = None encoding = _extract_h5nc_encoding(variable, raise_on_invalid=check_encoding) kwargs = {} for key in ['zlib', 'complevel', 'shuffle', 'chunksizes', 'fletcher32']: if key in encoding: kwargs[key] = encoding[key] nc4_var = self.ds.createVariable(name, dtype, variable.dims, fill_value=fill_value, **kwargs) for k, v in iteritems(attrs): nc4_var.setncattr(k, v) return nc4_var, variable.data def sync(self): super(H5NetCDFStore, self).sync() self.ds.sync() def close(self): ds = self.ds # netCDF4 only allows closing the root group while ds.parent is not None: ds = ds.parent ds.close() ������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������xarray-0.9.1/xarray/backends/memory.py��������������������������������������������������������������0000644�1311115�0011610�00000003002�13043511243�020060� 0����������������������������������������������������������������������������������������������������ustar �shoyer��������������������������eng�����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������from __future__ import absolute_import from __future__ import division from __future__ import print_function import copy import numpy as np from ..core.variable import Variable from ..core.pycompat import OrderedDict from .common import AbstractWritableDataStore class InMemoryDataStore(AbstractWritableDataStore): """ Stores dimensions, variables and attributes in ordered dictionaries, making this store fast compared to stores which save to disk. This store exists purely for internal testing purposes. """ def __init__(self, variables=None, attributes=None, writer=None): self._variables = OrderedDict() if variables is None else variables self._attributes = OrderedDict() if attributes is None else attributes super(InMemoryDataStore, self).__init__(writer) def get_attrs(self): return self._attributes def get_variables(self): return self._variables def prepare_variable(self, k, v, *args, **kwargs): new_var = Variable(v.dims, np.empty_like(v), v.attrs) # we copy the variable and stuff all encodings in the # attributes to imitate what happens when writing to disk. new_var.attrs.update(v.encoding) self._variables[k] = new_var return new_var, v.data def set_attribute(self, k, v): # copy to imitate writing to disk. self._attributes[k] = copy.deepcopy(v) def set_dimension(self, d, l): # in this model, dimensions are accounted for in the variables pass ������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������xarray-0.9.1/xarray/backends/netcdf3.py�������������������������������������������������������������0000644�1311115�0011610�00000010677�13043511243�020116� 0����������������������������������������������������������������������������������������������������ustar �shoyer��������������������������eng�����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������from __future__ import absolute_import from __future__ import division from __future__ import print_function import unicodedata import numpy as np from .. import conventions, Variable from ..core import ops from ..core.pycompat import basestring, unicode_type, OrderedDict # Special characters that are permitted in netCDF names except in the # 0th position of the string _specialchars = '_.@+- !"#$%&\()*,:;<=>?[]^`{|}~' # The following are reserved names in CDL and may not be used as names of # variables, dimension, attributes _reserved_names = set(['byte', 'char', 'short', 'ushort', 'int', 'uint', 'int64', 'uint64', 'float' 'real', 'double', 'bool', 'string']) # These data-types aren't supported by netCDF3, so they are automatically # coerced instead as indicated by the "coerce_nc3_dtype" function _nc3_dtype_coercions = {'int64': 'int32', 'bool': 'int8'} def coerce_nc3_dtype(arr): """Coerce an array to a data type that can be stored in a netCDF-3 file This function performs the following dtype conversions: int64 -> int32 float64 -> float32 bool -> int8 unicode -> string Data is checked for equality, or equivalence (non-NaN values) with `np.allclose` with the default keyword arguments. """ dtype = str(arr.dtype) if dtype in _nc3_dtype_coercions: new_dtype = _nc3_dtype_coercions[dtype] # TODO: raise a warning whenever casting the data-type instead? cast_arr = arr.astype(new_dtype) if ((('int' in dtype or 'U' in dtype) and not (cast_arr == arr).all()) or ('float' in dtype and not ops.allclose_or_equiv(cast_arr, arr))): raise ValueError('could not safely cast array from dtype %s to %s' % (dtype, new_dtype)) arr = cast_arr elif arr.dtype.kind == 'U': arr = np.core.defchararray.encode(arr, 'utf-8') return arr def maybe_convert_to_char_array(data, dims): if data.dtype.kind == 'S' and data.dtype.itemsize > 1: data = conventions.string_to_char(data) dims = dims + ('string%s' % data.shape[-1],) return data, dims def encode_nc3_attr_value(value): if isinstance(value, basestring): if not isinstance(value, unicode_type): value = value.decode('utf-8') else: value = coerce_nc3_dtype(np.atleast_1d(value)) if value.ndim > 1: raise ValueError("netCDF attributes must be 1-dimensional") return value def encode_nc3_attrs(attrs): return OrderedDict([(k, encode_nc3_attr_value(v)) for k, v in attrs.items()]) def encode_nc3_variable(var): data = coerce_nc3_dtype(var.data) data, dims = maybe_convert_to_char_array(data, var.dims) attrs = encode_nc3_attrs(var.attrs) return Variable(dims, data, attrs, var.encoding) def _isalnumMUTF8(c): """Return True if the given UTF-8 encoded character is alphanumeric or multibyte. Input is not checked! """ return c.isalnum() or (len(c.encode('utf-8')) > 1) def is_valid_nc3_name(s): """Test whether an object can be validly converted to a netCDF-3 dimension, variable or attribute name Earlier versions of the netCDF C-library reference implementation enforced a more restricted set of characters in creating new names, but permitted reading names containing arbitrary bytes. This specification extends the permitted characters in names to include multi-byte UTF-8 encoded Unicode and additional printing characters from the US-ASCII alphabet. The first character of a name must be alphanumeric, a multi-byte UTF-8 character, or '_' (reserved for special names with meaning to implementations, such as the "_FillValue" attribute). Subsequent characters may also include printing special characters, except for '/' which is not allowed in names. Names that have trailing space characters are also not permitted. """ if not isinstance(s, basestring): return False if not isinstance(s, unicode_type): s = s.decode('utf-8') num_bytes = len(s.encode('utf-8')) return ((unicodedata.normalize('NFC', s) == s) and (s not in _reserved_names) and (num_bytes >= 0) and ('/' not in s) and (s[-1] != ' ') and (_isalnumMUTF8(s[0]) or (s[0] == '_')) and all((_isalnumMUTF8(c) or c in _specialchars for c in s))) �����������������������������������������������������������������xarray-0.9.1/xarray/backends/netCDF4_.py������������������������������������������������������������0000644�1311115�0011610�00000027364�13043511243�020117� 0����������������������������������������������������������������������������������������������������ustar �shoyer��������������������������eng�����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������from __future__ import absolute_import from __future__ import division from __future__ import print_function import functools import operator import numpy as np from .. import Variable from ..conventions import pop_to from ..core import indexing from ..core.utils import (FrozenOrderedDict, NDArrayMixin, close_on_error, is_remote_uri) from ..core.pycompat import iteritems, basestring, OrderedDict, PY3 from .common import WritableCFDataStore, robust_getitem, DataStorePickleMixin from .netcdf3 import (encode_nc3_attr_value, encode_nc3_variable, maybe_convert_to_char_array) # This lookup table maps from dtype.byteorder to a readable endian # string used by netCDF4. _endian_lookup = {'=': 'native', '>': 'big', '<': 'little', '|': 'native'} class BaseNetCDF4Array(NDArrayMixin): def __init__(self, variable_name, datastore): self.datastore = datastore self.variable_name = variable_name @property def array(self): return self.datastore.ds.variables[self.variable_name] @property def dtype(self): dtype = self.array.dtype if dtype is str: # return object dtype because that's the only way in numpy to # represent variable length strings; it also prevents automatic # string concatenation via conventions.decode_cf_variable dtype = np.dtype('O') return dtype class NetCDF4ArrayWrapper(BaseNetCDF4Array): def __getitem__(self, key): if self.datastore.is_remote: # pragma: no cover getitem = functools.partial(robust_getitem, catch=RuntimeError) else: getitem = operator.getitem try: data = getitem(self.array, key) except IndexError: # Catch IndexError in netCDF4 and return a more informative error # message. This is most often called when an unsorted indexer is # used before the data is loaded from disk. msg = ('The indexing operation you are attempting to perform is ' 'not valid on netCDF4.Variable object. Try loading your ' 'data into memory first by calling .load().') if not PY3: import traceback msg += '\n\nOriginal traceback:\n' + traceback.format_exc() raise IndexError(msg) if self.ndim == 0: # work around for netCDF4-python's broken handling of 0-d # arrays (slicing them always returns a 1-dimensional array): # https://github.com/Unidata/netcdf4-python/pull/220 data = np.asscalar(data) return data def _nc4_values_and_dtype(var): if var.dtype.kind == 'U': # this entire clause should not be necessary with netCDF4>=1.0.9 if len(var) > 0: var = var.astype('O') dtype = str elif var.dtype.kind == 'S': # use character arrays instead of unicode, because unicode support in # netCDF4 is still rather buggy data, dims = maybe_convert_to_char_array(var.data, var.dims) var = Variable(dims, data, var.attrs, var.encoding) dtype = var.dtype elif var.dtype.kind in ['i', 'u', 'f', 'c']: dtype = var.dtype else: raise ValueError('cannot infer dtype for netCDF4 variable') return var, dtype def _nc4_group(ds, group, mode): if group in set([None, '', '/']): # use the root group return ds else: # make sure it's a string if not isinstance(group, basestring): raise ValueError('group must be a string or None') # support path-like syntax path = group.strip('/').split('/') for key in path: try: ds = ds.groups[key] except KeyError as e: if mode != 'r': ds = ds.createGroup(key) else: # wrap error to provide slightly more helpful message raise IOError('group not found: %s' % key, e) return ds def _ensure_fill_value_valid(data, attributes): # work around for netCDF4/scipy issue where _FillValue has the wrong type: # https://github.com/Unidata/netcdf4-python/issues/271 if data.dtype.kind == 'S' and '_FillValue' in attributes: attributes['_FillValue'] = np.string_(attributes['_FillValue']) def _force_native_endianness(var): # possible values for byteorder are: # = native # < little-endian # > big-endian # | not applicable # Below we check if the data type is not native or NA if var.dtype.byteorder not in ['=', '|']: # if endianness is specified explicitly, convert to the native type data = var.data.astype(var.dtype.newbyteorder('=')) var = Variable(var.dims, data, var.attrs, var.encoding) # if endian exists, remove it from the encoding. var.encoding.pop('endian', None) # check to see if encoding has a value for endian its 'native' if not var.encoding.get('endian', 'native') is 'native': raise NotImplementedError("Attempt to write non-native endian type, " "this is not supported by the netCDF4 " "python library.") return var def _extract_nc4_variable_encoding(variable, raise_on_invalid=False, lsd_okay=True, backend='netCDF4'): encoding = variable.encoding.copy() safe_to_drop = set(['source', 'original_shape']) valid_encodings = set(['zlib', 'complevel', 'fletcher32', 'contiguous', 'chunksizes']) if lsd_okay: valid_encodings.add('least_significant_digit') if (encoding.get('chunksizes') is not None and (encoding.get('original_shape', variable.shape) != variable.shape) and not raise_on_invalid): del encoding['chunksizes'] for k in safe_to_drop: if k in encoding: del encoding[k] if raise_on_invalid: invalid = [k for k in encoding if k not in valid_encodings] if invalid: raise ValueError('unexpected encoding parameters for %r backend: ' ' %r' % (backend, invalid)) else: for k in list(encoding): if k not in valid_encodings: del encoding[k] return encoding def _open_netcdf4_group(filename, mode, group=None, **kwargs): import netCDF4 as nc4 ds = nc4.Dataset(filename, mode=mode, **kwargs) with close_on_error(ds): ds = _nc4_group(ds, group, mode) for var in ds.variables.values(): # we handle masking and scaling ourselves var.set_auto_maskandscale(False) return ds class NetCDF4DataStore(WritableCFDataStore, DataStorePickleMixin): """Store for reading and writing data via the Python-NetCDF4 library. This store supports NetCDF3, NetCDF4 and OpenDAP datasets. """ def __init__(self, filename, mode='r', format='NETCDF4', group=None, writer=None, clobber=True, diskless=False, persist=False): if format is None: format = 'NETCDF4' opener = functools.partial(_open_netcdf4_group, filename, mode=mode, group=group, clobber=clobber, diskless=diskless, persist=persist, format=format) self.ds = opener() self.format = format self.is_remote = is_remote_uri(filename) self._opener = opener self._filename = filename self._mode = 'a' if mode == 'w' else mode super(NetCDF4DataStore, self).__init__(writer) def open_store_variable(self, name, var): dimensions = var.dimensions data = indexing.LazilyIndexedArray(NetCDF4ArrayWrapper(name, self)) attributes = OrderedDict((k, var.getncattr(k)) for k in var.ncattrs()) _ensure_fill_value_valid(data, attributes) # netCDF4 specific encoding; save _FillValue for later encoding = {} filters = var.filters() if filters is not None: encoding.update(filters) chunking = var.chunking() if chunking is not None: if chunking == 'contiguous': encoding['contiguous'] = True encoding['chunksizes'] = None else: encoding['contiguous'] = False encoding['chunksizes'] = tuple(chunking) # TODO: figure out how to round-trip "endian-ness" without raising # warnings from netCDF4 # encoding['endian'] = var.endian() pop_to(attributes, encoding, 'least_significant_digit') # save source so __repr__ can detect if it's local or not encoding['source'] = self._filename encoding['original_shape'] = var.shape return Variable(dimensions, data, attributes, encoding) def get_variables(self): return FrozenOrderedDict((k, self.open_store_variable(k, v)) for k, v in iteritems(self.ds.variables)) def get_attrs(self): return FrozenOrderedDict((k, self.ds.getncattr(k)) for k in self.ds.ncattrs()) def get_dimensions(self): return FrozenOrderedDict((k, len(v)) for k, v in iteritems(self.ds.dimensions)) def get_encoding(self): encoding = {} encoding['unlimited_dims'] = { k for k, v in self.ds.dimensions.items() if v.isunlimited()} return encoding def set_dimension(self, name, length): self.ds.createDimension(name, size=length) def set_attribute(self, key, value): if self.format != 'NETCDF4': value = encode_nc3_attr_value(value) self.ds.setncattr(key, value) def prepare_variable(self, name, variable, check_encoding=False, unlimited_dims=None): attrs = variable.attrs.copy() variable = _force_native_endianness(variable) if self.format == 'NETCDF4': variable, datatype = _nc4_values_and_dtype(variable) else: variable = encode_nc3_variable(variable) datatype = variable.dtype self.set_necessary_dimensions(variable, unlimited_dims=unlimited_dims) fill_value = attrs.pop('_FillValue', None) if fill_value in ['', '\x00']: # these are equivalent to the default FillValue, but netCDF4 # doesn't like setting fill_value to an empty string fill_value = None encoding = _extract_nc4_variable_encoding( variable, raise_on_invalid=check_encoding) nc4_var = self.ds.createVariable( varname=name, datatype=datatype, dimensions=variable.dims, zlib=encoding.get('zlib', False), complevel=encoding.get('complevel', 4), shuffle=encoding.get('shuffle', True), fletcher32=encoding.get('fletcher32', False), contiguous=encoding.get('contiguous', False), chunksizes=encoding.get('chunksizes'), endian='native', least_significant_digit=encoding.get('least_significant_digit'), fill_value=fill_value) nc4_var.set_auto_maskandscale(False) for k, v in iteritems(attrs): # set attributes one-by-one since netCDF4<1.0.10 can't handle # OrderedDict as the input to setncatts nc4_var.setncattr(k, v) return nc4_var, variable.data def sync(self): super(NetCDF4DataStore, self).sync() self.ds.sync() def close(self): ds = self.ds # netCDF4 only allows closing the root group while ds.parent is not None: ds = ds.parent ds.close() ����������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������xarray-0.9.1/xarray/backends/pydap_.py��������������������������������������������������������������0000644�1311115�0011610�00000005406�13043511243�020036� 0����������������������������������������������������������������������������������������������������ustar �shoyer��������������������������eng�����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������from __future__ import absolute_import from __future__ import division from __future__ import print_function import numpy as np from .. import Variable from ..core.utils import FrozenOrderedDict, Frozen, NDArrayMixin from ..core import indexing from .common import AbstractDataStore, robust_getitem class PydapArrayWrapper(NDArrayMixin): def __init__(self, array): self.array = array @property def dtype(self): t = self.array.type if t.size is None and t.typecode == 'S': # return object dtype because that's the only way in numpy to # represent variable length strings; it also prevents automatic # string concatenation via conventions.decode_cf_variable return np.dtype('O') else: return np.dtype(t.typecode + str(t.size)) def __getitem__(self, key): if not isinstance(key, tuple): key = (key,) for k in key: if not (isinstance(k, (int, np.integer, slice)) or k is Ellipsis): raise IndexError('pydap only supports indexing with int, ' 'slice and Ellipsis objects') # pull the data from the array attribute if possible, to avoid # downloading coordinate data twice array = getattr(self.array, 'array', self.array) result = robust_getitem(array, key, catch=ValueError) # pydap doesn't squeeze axes automatically like numpy axis = tuple(n for n, k in enumerate(key) if isinstance(k, (int, np.integer))) result = np.squeeze(result, axis) return result def _fix_global_attributes(attributes): attributes = dict(attributes) for k in list(attributes): if k.lower() == 'global' or k.lower().endswith('_global'): # move global attributes to the top level, like the netcdf-C # DAP client attributes.update(attributes.pop(k)) return attributes class PydapDataStore(AbstractDataStore): """Store for accessing OpenDAP datasets with pydap. This store provides an alternative way to access OpenDAP datasets that may be useful if the netCDF4 library is not available. """ def __init__(self, url): import pydap.client self.ds = pydap.client.open_url(url) def open_store_variable(self, var): data = indexing.LazilyIndexedArray(PydapArrayWrapper(var)) return Variable(var.dimensions, data, var.attributes) def get_variables(self): return FrozenOrderedDict((k, self.open_store_variable(v)) for k, v in self.ds.iteritems()) def get_attrs(self): return Frozen(_fix_global_attributes(self.ds.attributes)) def get_dimensions(self): return Frozen(self.ds.dimensions) ����������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������xarray-0.9.1/xarray/backends/pynio_.py��������������������������������������������������������������0000644�1311115�0011610�00000003536�13043511243�020061� 0����������������������������������������������������������������������������������������������������ustar �shoyer��������������������������eng�����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������from __future__ import absolute_import from __future__ import division from __future__ import print_function import functools import numpy as np from .. import Variable from ..core.utils import FrozenOrderedDict, Frozen, NDArrayMixin from ..core import indexing from .common import AbstractDataStore, DataStorePickleMixin class NioArrayWrapper(NDArrayMixin): def __init__(self, variable_name, datastore): self.datastore = datastore self.variable_name = variable_name @property def array(self): return self.datastore.ds.variables[self.variable_name] @property def dtype(self): return np.dtype(self.array.typecode()) def __getitem__(self, key): if key == () and self.ndim == 0: return self.array.get_value() return self.array[key] class NioDataStore(AbstractDataStore, DataStorePickleMixin): """Store for accessing datasets via PyNIO """ def __init__(self, filename, mode='r'): import Nio opener = functools.partial(Nio.open_file, filename, mode=mode) self.ds = opener() self._opener = opener self._mode = mode def open_store_variable(self, name, var): data = indexing.LazilyIndexedArray(NioArrayWrapper(name, self)) return Variable(var.dimensions, data, var.attributes) def get_variables(self): return FrozenOrderedDict((k, self.open_store_variable(k, v)) for k, v in self.ds.variables.iteritems()) def get_attrs(self): return Frozen(self.ds.attributes) def get_dimensions(self): return Frozen(self.ds.dimensions) def get_encoding(self): encoding = {} encoding['unlimited_dims'] = set( [k for k in self.ds.dimensions if self.ds.unlimited(k)]) return encoding def close(self): self.ds.close() ������������������������������������������������������������������������������������������������������������������������������������������������������������������xarray-0.9.1/xarray/backends/scipy_.py��������������������������������������������������������������0000644�1311115�0011610�00000014570�13043511243�020052� 0����������������������������������������������������������������������������������������������������ustar �shoyer��������������������������eng�����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������from __future__ import absolute_import from __future__ import division from __future__ import print_function import functools from io import BytesIO import numpy as np import warnings from .. import Variable from ..core.pycompat import iteritems, OrderedDict from ..core.utils import Frozen, FrozenOrderedDict from ..core.indexing import NumpyIndexingAdapter from .common import WritableCFDataStore, DataStorePickleMixin from .netcdf3 import (is_valid_nc3_name, encode_nc3_attr_value, encode_nc3_variable) def _decode_string(s): if isinstance(s, bytes): return s.decode('utf-8', 'replace') return s def _decode_attrs(d): # don't decode _FillValue from bytes -> unicode, because we want to ensure # that its type matches the data exactly return OrderedDict((k, v if k == '_FillValue' else _decode_string(v)) for (k, v) in iteritems(d)) class ScipyArrayWrapper(NumpyIndexingAdapter): def __init__(self, variable_name, datastore): self.datastore = datastore self.variable_name = variable_name @property def array(self): return self.datastore.ds.variables[self.variable_name].data @property def dtype(self): # always use native endianness return np.dtype(self.array.dtype.kind + str(self.array.dtype.itemsize)) def __getitem__(self, key): data = super(ScipyArrayWrapper, self).__getitem__(key) # Copy data if the source file is mmapped. This makes things consistent # with the netCDF4 library by ensuring we can safely read arrays even # after closing associated files. copy = self.datastore.ds.use_mmap data = np.array(data, dtype=self.dtype, copy=copy) return data def _open_scipy_netcdf(filename, mode, mmap, version): import scipy.io if isinstance(filename, bytes) and filename.startswith(b'CDF'): # it's a NetCDF3 bytestring filename = BytesIO(filename) return scipy.io.netcdf_file(filename, mode=mode, mmap=mmap, version=version) class ScipyDataStore(WritableCFDataStore, DataStorePickleMixin): """Store for reading and writing data via scipy.io.netcdf. This store has the advantage of being able to be initialized with a StringIO object, allow for serialization without writing to disk. It only supports the NetCDF3 file-format. """ def __init__(self, filename_or_obj, mode='r', format=None, group=None, writer=None, mmap=None): import scipy import scipy.io if mode != 'r' and scipy.__version__ < '0.13': # pragma: no cover warnings.warn('scipy %s detected; ' 'the minimal recommended version is 0.13. ' 'Older version of this library do not reliably ' 'read and write files.' % scipy.__version__, ImportWarning) if group is not None: raise ValueError('cannot save to a group with the ' 'scipy.io.netcdf backend') if format is None or format == 'NETCDF3_64BIT': version = 2 elif format == 'NETCDF3_CLASSIC': version = 1 else: raise ValueError('invalid format for scipy.io.netcdf backend: %r' % format) opener = functools.partial(_open_scipy_netcdf, filename=filename_or_obj, mode=mode, mmap=mmap, version=version) self.ds = opener() self._opener = opener self._mode = mode super(ScipyDataStore, self).__init__(writer) def open_store_variable(self, name, var): return Variable(var.dimensions, ScipyArrayWrapper(name, self), _decode_attrs(var._attributes)) def get_variables(self): return FrozenOrderedDict((k, self.open_store_variable(k, v)) for k, v in iteritems(self.ds.variables)) def get_attrs(self): return Frozen(_decode_attrs(self.ds._attributes)) def get_dimensions(self): return Frozen(self.ds.dimensions) def get_encoding(self): encoding = {} encoding['unlimited_dims'] = { k for k, v in self.ds.dimensions.items() if v is None} return encoding def set_dimension(self, name, length): if name in self.dimensions: raise ValueError('%s does not support modifying dimensions' % type(self).__name__) self.ds.createDimension(name, length) def _validate_attr_key(self, key): if not is_valid_nc3_name(key): raise ValueError("Not a valid attribute name") def set_attribute(self, key, value): self._validate_attr_key(key) value = encode_nc3_attr_value(value) setattr(self.ds, key, value) def prepare_variable(self, name, variable, check_encoding=False, unlimited_dims=None): variable = encode_nc3_variable(variable) if check_encoding and variable.encoding: raise ValueError('unexpected encoding for scipy backend: %r' % list(variable.encoding)) if unlimited_dims is not None and len(unlimited_dims) > 1: raise ValueError('NETCDF3 only supports one unlimited dimension') self.set_necessary_dimensions(variable, unlimited_dims=unlimited_dims) data = variable.data # nb. this still creates a numpy array in all memory, even though we # don't write the data yet; scipy.io.netcdf does not not support # incremental writes. self.ds.createVariable(name, data.dtype, variable.dims) scipy_var = self.ds.variables[name] for k, v in iteritems(variable.attrs): self._validate_attr_key(k) setattr(scipy_var, k, v) return scipy_var, data def sync(self): super(ScipyDataStore, self).sync() self.ds.flush() def close(self): self.ds.close() def __exit__(self, type, value, tb): self.close() def __setstate__(self, state): filename = state['_opener'].keywords['filename'] if hasattr(filename, 'seek'): # it's a file-like object # seek to the start of the file so scipy can read it filename.seek(0) super(ScipyDataStore, self).__setstate__(state) ����������������������������������������������������������������������������������������������������������������������������������������xarray-0.9.1/xarray/conventions.py������������������������������������������������������������������0000644�1311115�0011610�00000113420�13043511243�017351� 0����������������������������������������������������������������������������������������������������ustar �shoyer��������������������������eng�����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������from __future__ import absolute_import from __future__ import division from __future__ import print_function from datetime import datetime import re import traceback import warnings import numpy as np import pandas as pd from collections import defaultdict from pandas.tslib import OutOfBoundsDatetime from .core import indexing, ops, utils from .core.formatting import format_timestamp, first_n_items, last_item from .core.variable import as_variable, Variable from .core.pycompat import iteritems, OrderedDict, PY3, basestring # standard calendars recognized by netcdftime _STANDARD_CALENDARS = set(['standard', 'gregorian', 'proleptic_gregorian']) def mask_and_scale(array, fill_value=None, scale_factor=None, add_offset=None, dtype=float): """Scale and mask array values according to CF conventions for packed and missing values First, values equal to the fill_value are replaced by NaN. Then, new values are given by the formula: original_values * scale_factor + add_offset Parameters ---------- array : array-like Original array of values to wrap fill_value : number, optional All values equal to fill_value in the original array are replaced by NaN. If an array of multiple values is provided a warning will be issued and all array elements matching an value in the fill_value array will be replaced by NaN. scale_factor : number, optional Multiply entries in the original array by this number. add_offset : number, optional After applying scale_factor, add this number to entries in the original array. Returns ------- scaled : np.ndarray Array of masked and scaled values. References ---------- http://www.unidata.ucar.edu/software/netcdf/docs/BestPractices.html """ # by default, cast to float to ensure NaN is meaningful values = np.array(array, dtype=dtype, copy=True) if fill_value is not None and not np.all(pd.isnull(fill_value)): if getattr(fill_value, 'size', 1) > 1: fill_values = fill_value # multiple fill values else: fill_values = [fill_value] for f_value in fill_values: if values.ndim > 0: values[values == f_value] = np.nan elif values == f_value: values = np.array(np.nan) if scale_factor is not None: values *= scale_factor if add_offset is not None: values += add_offset return values def _netcdf_to_numpy_timeunit(units): units = units.lower() if not units.endswith('s'): units = '%ss' % units return {'microseconds': 'us', 'milliseconds': 'ms', 'seconds': 's', 'minutes': 'm', 'hours': 'h', 'days': 'D'}[units] def _unpack_netcdf_time_units(units): # CF datetime units follow the format: "UNIT since DATE" # this parses out the unit and date allowing for extraneous # whitespace. matches = re.match('(.+) since (.+)', units) if not matches: raise ValueError('invalid time units: %s' % units) delta_units, ref_date = [s.strip() for s in matches.groups()] return delta_units, ref_date def _decode_datetime_with_netcdf4(num_dates, units, calendar): import netCDF4 as nc4 dates = np.asarray(nc4.num2date(num_dates, units, calendar)) if (dates[np.nanargmin(num_dates)].year < 1678 or dates[np.nanargmax(num_dates)].year >= 2262): warnings.warn('Unable to decode time axis into full ' 'numpy.datetime64 objects, continuing using dummy ' 'netCDF4.datetime objects instead, reason: dates out' ' of range', RuntimeWarning, stacklevel=3) else: try: dates = nctime_to_nptime(dates) except ValueError as e: warnings.warn('Unable to decode time axis into full ' 'numpy.datetime64 objects, continuing using ' 'dummy netCDF4.datetime objects instead, reason:' '{0}'.format(e), RuntimeWarning, stacklevel=3) return dates def decode_cf_datetime(num_dates, units, calendar=None): """Given an array of numeric dates in netCDF format, convert it into a numpy array of date time objects. For standard (Gregorian) calendars, this function uses vectorized operations, which makes it much faster than netCDF4.num2date. In such a case, the returned array will be of type np.datetime64. See also -------- netCDF4.num2date """ num_dates = np.asarray(num_dates, dtype=float) flat_num_dates = num_dates.ravel() if calendar is None: calendar = 'standard' delta, ref_date = _unpack_netcdf_time_units(units) try: if calendar not in _STANDARD_CALENDARS: raise OutOfBoundsDatetime delta = _netcdf_to_numpy_timeunit(delta) try: ref_date = pd.Timestamp(ref_date) except ValueError: # ValueError is raised by pd.Timestamp for non-ISO timestamp # strings, in which case we fall back to using netCDF4 raise OutOfBoundsDatetime # fixes: https://github.com/pydata/pandas/issues/14068 # these lines check if the the lowest or the highest value in dates # cause an OutOfBoundsDatetime (Overflow) error pd.to_timedelta(flat_num_dates.min(), delta) + ref_date pd.to_timedelta(flat_num_dates.max(), delta) + ref_date dates = (pd.to_timedelta(flat_num_dates, delta) + ref_date).values except (OutOfBoundsDatetime, OverflowError): dates = _decode_datetime_with_netcdf4(flat_num_dates, units, calendar) return dates.reshape(num_dates.shape) def decode_cf_timedelta(num_timedeltas, units): """Given an array of numeric timedeltas in netCDF format, convert it into a numpy timedelta64[ns] array. """ num_timedeltas = np.asarray(num_timedeltas) units = _netcdf_to_numpy_timeunit(units) shape = num_timedeltas.shape num_timedeltas = num_timedeltas.ravel() result = pd.to_timedelta(num_timedeltas, unit=units, box=False) # NaT is returned unboxed with wrong units; this should be fixed in pandas if result.dtype != 'timedelta64[ns]': result = result.astype('timedelta64[ns]') return result.reshape(shape) TIME_UNITS = frozenset(['days', 'hours', 'minutes', 'seconds', 'milliseconds', 'microseconds']) def _infer_time_units_from_diff(unique_timedeltas): for time_unit, delta in [('days', 86400), ('hours', 3600), ('minutes', 60), ('seconds', 1)]: unit_delta = np.timedelta64(10 ** 9 * delta, 'ns') diffs = unique_timedeltas / unit_delta if np.all(diffs == diffs.astype(int)): return time_unit return 'seconds' def infer_datetime_units(dates): """Given an array of datetimes, returns a CF compatible time-unit string of the form "{time_unit} since {date[0]}", where `time_unit` is 'days', 'hours', 'minutes' or 'seconds' (the first one that can evenly divide all unique time deltas in `dates`) """ dates = pd.to_datetime(np.asarray(dates).ravel(), box=False) dates = dates[pd.notnull(dates)] unique_timedeltas = np.unique(np.diff(dates)) units = _infer_time_units_from_diff(unique_timedeltas) reference_date = dates[0] if len(dates) > 0 else '1970-01-01' return '%s since %s' % (units, pd.Timestamp(reference_date)) def infer_timedelta_units(deltas): """Given an array of timedeltas, returns a CF compatible time-unit from {'days', 'hours', 'minutes' 'seconds'} (the first one that can evenly divide all unique time deltas in `deltas`) """ deltas = pd.to_timedelta(np.asarray(deltas).ravel(), box=False) unique_timedeltas = np.unique(deltas[pd.notnull(deltas)]) units = _infer_time_units_from_diff(unique_timedeltas) return units def nctime_to_nptime(times): """Given an array of netCDF4.datetime objects, return an array of numpy.datetime64 objects of the same size""" times = np.asarray(times) new = np.empty(times.shape, dtype='M8[ns]') for i, t in np.ndenumerate(times): dt = datetime(t.year, t.month, t.day, t.hour, t.minute, t.second) new[i] = np.datetime64(dt) return new def _cleanup_netcdf_time_units(units): delta, ref_date = _unpack_netcdf_time_units(units) try: units = '%s since %s' % (delta, format_timestamp(ref_date)) except OutOfBoundsDatetime: # don't worry about reifying the units if they're out of bounds pass return units def _encode_datetime_with_netcdf4(dates, units, calendar): """Fallback method for encoding dates using netCDF4-python. This method is more flexible than xarray's parsing using datetime64[ns] arrays but also slower because it loops over each element. """ import netCDF4 as nc4 if np.issubdtype(dates.dtype, np.datetime64): # numpy's broken datetime conversion only works for us precision dates = dates.astype('M8[us]').astype(datetime) def encode_datetime(d): return np.nan if d is None else nc4.date2num(d, units, calendar) return np.vectorize(encode_datetime)(dates) def cast_to_int_if_safe(num): int_num = np.array(num, dtype=np.int64) if (num == int_num).all(): num = int_num return num def encode_cf_datetime(dates, units=None, calendar=None): """Given an array of datetime objects, returns the tuple `(num, units, calendar)` suitable for a CF compliant time variable. Unlike `date2num`, this function can handle datetime64 arrays. See also -------- netCDF4.date2num """ dates = np.asarray(dates) if units is None: units = infer_datetime_units(dates) else: units = _cleanup_netcdf_time_units(units) if calendar is None: calendar = 'proleptic_gregorian' delta, ref_date = _unpack_netcdf_time_units(units) try: if calendar not in _STANDARD_CALENDARS or dates.dtype.kind == 'O': # parse with netCDF4 instead raise OutOfBoundsDatetime assert dates.dtype == 'datetime64[ns]' delta_units = _netcdf_to_numpy_timeunit(delta) time_delta = np.timedelta64(1, delta_units).astype('timedelta64[ns]') ref_date = np.datetime64(pd.Timestamp(ref_date)) num = (dates - ref_date) / time_delta except (OutOfBoundsDatetime, OverflowError): num = _encode_datetime_with_netcdf4(dates, units, calendar) num = cast_to_int_if_safe(num) return (num, units, calendar) def encode_cf_timedelta(timedeltas, units=None): if units is None: units = infer_timedelta_units(timedeltas) np_unit = _netcdf_to_numpy_timeunit(units) num = 1.0 * timedeltas / np.timedelta64(1, np_unit) num = np.where(pd.isnull(timedeltas), np.nan, num) num = cast_to_int_if_safe(num) return (num, units) class MaskedAndScaledArray(utils.NDArrayMixin): """Wrapper around array-like objects to create a new indexable object where values, when accessed, are automatically scaled and masked according to CF conventions for packed and missing data values. New values are given by the formula: original_values * scale_factor + add_offset Values can only be accessed via `__getitem__`: >>> x = MaskedAndScaledArray(np.array([-99, -1, 0, 1, 2]), -99, 0.01, 1) >>> x MaskedAndScaledArray(array([-99, -1, 0, 1, 2]), fill_value=-99, scale_factor=0.01, add_offset=1) >>> x[:] array([ nan, 0.99, 1. , 1.01, 1.02] References ---------- http://www.unidata.ucar.edu/software/netcdf/docs/BestPractices.html """ def __init__(self, array, fill_value=None, scale_factor=None, add_offset=None, dtype=float): """ Parameters ---------- array : array-like Original array of values to wrap fill_value : number, optional All values equal to fill_value in the original array are replaced by NaN. scale_factor : number, optional Multiply entries in the original array by this number. add_offset : number, optional After applying scale_factor, add this number to entries in the original array. """ self.array = array self.fill_value = fill_value self.scale_factor = scale_factor self.add_offset = add_offset self._dtype = dtype @property def dtype(self): return np.dtype(self._dtype) def __getitem__(self, key): return mask_and_scale(self.array[key], self.fill_value, self.scale_factor, self.add_offset, self._dtype) def __repr__(self): return ("%s(%r, fill_value=%r, scale_factor=%r, add_offset=%r, " "dtype=%r)" % (type(self).__name__, self.array, self.fill_value, self.scale_factor, self.add_offset, self._dtype)) class DecodedCFDatetimeArray(utils.NDArrayMixin): """Wrapper around array-like objects to create a new indexable object where values, when accessed, are automatically converted into datetime objects using decode_cf_datetime. """ def __init__(self, array, units, calendar=None): self.array = array self.units = units self.calendar = calendar # Verify that at least the first and last date can be decoded # successfully. Otherwise, tracebacks end up swallowed by # Dataset.__repr__ when users try to view their lazily decoded array. example_value = np.concatenate([first_n_items(array, 1) or [0], last_item(array) or [0]]) try: result = decode_cf_datetime(example_value, units, calendar) except Exception: calendar_msg = ('the default calendar' if calendar is None else 'calendar %r' % calendar) msg = ('unable to decode time units %r with %s. Try ' 'opening your dataset with decode_times=False.' % (units, calendar_msg)) if not PY3: msg += ' Full traceback:\n' + traceback.format_exc() raise ValueError(msg) else: self._dtype = getattr(result, 'dtype', np.dtype('object')) @property def dtype(self): return self._dtype def __getitem__(self, key): return decode_cf_datetime(self.array[key], units=self.units, calendar=self.calendar) class DecodedCFTimedeltaArray(utils.NDArrayMixin): """Wrapper around array-like objects to create a new indexable object where values, when accessed, are automatically converted into timedelta objects using decode_cf_timedelta. """ def __init__(self, array, units): self.array = array self.units = units @property def dtype(self): return np.dtype('timedelta64[ns]') def __getitem__(self, key): return decode_cf_timedelta(self.array[key], units=self.units) class CharToStringArray(utils.NDArrayMixin): """Wrapper around array-like objects to create a new indexable object where values, when accessed, are automatically concatenated along the last dimension. >>> CharToStringArray(np.array(['a', 'b', 'c']))[:] array('abc', dtype='|S3') """ def __init__(self, array): """ Parameters ---------- array : array-like Original array of values to wrap. """ self.array = array @property def dtype(self): return np.dtype('S' + str(self.array.shape[-1])) @property def shape(self): return self.array.shape[:-1] def __str__(self): if self.ndim == 0: # always return a unicode str if it's a single item for py3 compat return self[...].item().decode('utf-8') else: return repr(self) def __repr__(self): return '%s(%r)' % (type(self).__name__, self.array) def __getitem__(self, key): if self.array.ndim == 0: values = self.array[key] else: # require slicing the last dimension completely key = indexing.expanded_indexer(key, self.array.ndim) if key[-1] != slice(None): raise IndexError('too many indices') values = char_to_string(self.array[key]) return values class NativeEndiannessArray(utils.NDArrayMixin): """Decode arrays on the fly from non-native to native endianness This is useful for decoding arrays from netCDF3 files (which are all big endian) into native endianness, so they can be used with Cython functions, such as those found in bottleneck and pandas. >>> x = np.arange(5, dtype='>i2') >>> x.dtype dtype('>i2') >>> NativeEndianArray(x).dtype dtype('int16') >>> NativeEndianArray(x)[:].dtype dtype('int16') """ def __init__(self, array): self.array = array @property def dtype(self): return np.dtype(self.array.dtype.kind + str(self.array.dtype.itemsize)) def __getitem__(self, key): return np.asarray(self.array[key], dtype=self.dtype) class BoolTypeArray(utils.NDArrayMixin): """Decode arrays on the fly from integer to boolean datatype This is useful for decoding boolean arrays from integer typed netCDF variables. >>> x = np.array([1, 0, 1, 1, 0], dtype='i1') >>> x.dtype dtype('>i2') >>> BoolTypeArray(x).dtype dtype('bool') >>> BoolTypeArray(x)[:].dtype dtype('bool') """ def __init__(self, array): self.array = array @property def dtype(self): return np.dtype('bool') def __getitem__(self, key): return np.asarray(self.array[key], dtype=self.dtype) def string_to_char(arr): """Like netCDF4.stringtochar, but faster and more flexible. """ # ensure the array is contiguous arr = np.array(arr, copy=False, order='C') kind = arr.dtype.kind if kind not in ['U', 'S']: raise ValueError('argument must be a string') return arr.reshape(arr.shape + (1,)).view(kind + '1') def char_to_string(arr): """Like netCDF4.chartostring, but faster and more flexible. """ # based on: http://stackoverflow.com/a/10984878/809705 arr = np.array(arr, copy=False, order='C') kind = arr.dtype.kind if kind not in ['U', 'S']: raise ValueError('argument must be a string') return arr.view(kind + str(arr.shape[-1]))[..., 0] def safe_setitem(dest, key, value): if key in dest: raise ValueError('Failed hard to prevent overwriting key %r' % key) dest[key] = value def pop_to(source, dest, key, default=None): """ A convenience function which pops a key k from source to dest. None values are not passed on. If k already exists in dest an error is raised. """ value = source.pop(key, None) if value is not None: safe_setitem(dest, key, value) return value def _var_as_tuple(var): return var.dims, var.data, var.attrs.copy(), var.encoding.copy() def maybe_encode_datetime(var): if np.issubdtype(var.dtype, np.datetime64): dims, data, attrs, encoding = _var_as_tuple(var) (data, units, calendar) = encode_cf_datetime( data, encoding.pop('units', None), encoding.pop('calendar', None)) safe_setitem(attrs, 'units', units) safe_setitem(attrs, 'calendar', calendar) var = Variable(dims, data, attrs, encoding) return var def maybe_encode_timedelta(var): if np.issubdtype(var.dtype, np.timedelta64): dims, data, attrs, encoding = _var_as_tuple(var) data, units = encode_cf_timedelta( data, encoding.pop('units', None)) safe_setitem(attrs, 'units', units) var = Variable(dims, data, attrs, encoding) return var def maybe_encode_offset_and_scale(var, needs_copy=True): if any(k in var.encoding for k in ['add_offset', 'scale_factor']): dims, data, attrs, encoding = _var_as_tuple(var) data = data.astype(dtype=float, copy=needs_copy) needs_copy = False if 'add_offset' in encoding: data -= pop_to(encoding, attrs, 'add_offset') if 'scale_factor' in encoding: data /= pop_to(encoding, attrs, 'scale_factor') var = Variable(dims, data, attrs, encoding) return var, needs_copy def maybe_encode_fill_value(var, needs_copy=True): # replace NaN with the fill value if '_FillValue' in var.encoding: dims, data, attrs, encoding = _var_as_tuple(var) fill_value = pop_to(encoding, attrs, '_FillValue') if not pd.isnull(fill_value): data = ops.fillna(data, fill_value) needs_copy = False var = Variable(dims, data, attrs, encoding) return var, needs_copy def maybe_encode_dtype(var, name=None): if 'dtype' in var.encoding: dims, data, attrs, encoding = _var_as_tuple(var) dtype = np.dtype(encoding.pop('dtype')) if dtype != var.dtype: if np.issubdtype(dtype, np.integer): if (np.issubdtype(var.dtype, np.floating) and '_FillValue' not in var.attrs): warnings.warn('saving variable %s with floating ' 'point data as an integer dtype without ' 'any _FillValue to use for NaNs' % name, RuntimeWarning, stacklevel=3) data = ops.around(data)[...] if dtype == 'S1' and data.dtype != 'S1': data = string_to_char(np.asarray(data, 'S')) dims = dims + ('string%s' % data.shape[-1],) data = data.astype(dtype=dtype) var = Variable(dims, data, attrs, encoding) return var def maybe_default_fill_value(var): # make NaN the fill value for float types: if ('_FillValue' not in var.attrs and np.issubdtype(var.dtype, np.floating)): var.attrs['_FillValue'] = np.nan return var def maybe_encode_bools(var): if ((var.dtype == np.bool) and ('dtype' not in var.encoding) and ('dtype' not in var.attrs)): dims, data, attrs, encoding = _var_as_tuple(var) attrs['dtype'] = 'bool' data = data.astype(dtype='i1', copy=True) var = Variable(dims, data, attrs, encoding) return var def _infer_dtype(array): """Given an object array with no missing values, infer its dtype from its first element """ if array.size == 0: dtype = np.dtype(float) else: dtype = np.array(array[(0,) * array.ndim]).dtype if dtype.kind in ['S', 'U']: # don't just use inferred dtype to avoid truncating arrays to # the length of their first element dtype = np.dtype(dtype.kind) elif dtype.kind == 'O': raise ValueError('unable to infer dtype; xarray cannot ' 'serialize arbitrary Python objects') return dtype def ensure_dtype_not_object(var): # TODO: move this from conventions to backends? (it's not CF related) if var.dtype.kind == 'O': dims, data, attrs, encoding = _var_as_tuple(var) missing = pd.isnull(data) if missing.any(): # nb. this will fail for dask.array data non_missing_values = data[~missing] inferred_dtype = _infer_dtype(non_missing_values) if inferred_dtype.kind in ['S', 'U']: # There is no safe bit-pattern for NA in typical binary string # formats, we so can't set a fill_value. Unfortunately, this # means we won't be able to restore string arrays with missing # values. fill_value = '' else: # insist on using float for numeric values if not np.issubdtype(inferred_dtype, float): inferred_dtype = np.dtype(float) fill_value = np.nan data = np.array(data, dtype=inferred_dtype, copy=True) data[missing] = fill_value else: data = data.astype(dtype=_infer_dtype(data)) var = Variable(dims, data, attrs, encoding) return var def encode_cf_variable(var, needs_copy=True, name=None): """ Converts an Variable into an Variable which follows some of the CF conventions: - Nans are masked using _FillValue (or the deprecated missing_value) - Rescaling via: scale_factor and add_offset - datetimes are converted to the CF 'units since time' format - dtype encodings are enforced. Parameters ---------- var : xarray.Variable A variable holding un-encoded data. Returns ------- out : xarray.Variable A variable which has been encoded as described above. """ var = maybe_encode_datetime(var) var = maybe_encode_timedelta(var) var, needs_copy = maybe_encode_offset_and_scale(var, needs_copy) var, needs_copy = maybe_encode_fill_value(var, needs_copy) var = maybe_encode_dtype(var, name) var = maybe_default_fill_value(var) var = maybe_encode_bools(var) var = ensure_dtype_not_object(var) return var def decode_cf_variable(var, concat_characters=True, mask_and_scale=True, decode_times=True, decode_endianness=True): """ Decodes a variable which may hold CF encoded information. This includes variables that have been masked and scaled, which hold CF style time variables (this is almost always the case if the dataset has been serialized) and which have strings encoded as character arrays. Parameters ---------- var : Variable A variable holding potentially CF encoded information. concat_characters : bool Should character arrays be concatenated to strings, for example: ['h', 'e', 'l', 'l', 'o'] -> 'hello' mask_and_scale: bool Lazily scale (using scale_factor and add_offset) and mask (using _FillValue). decode_times : bool Decode cf times ('hours since 2000-01-01') to np.datetime64. decode_endianness : bool Decode arrays from non-native to native endianness. Returns ------- out : Variable A variable holding the decoded equivalent of var """ # use _data instead of data so as not to trigger loading data var = as_variable(var) data = var._data dimensions = var.dims attributes = var.attrs.copy() encoding = var.encoding.copy() original_dtype = data.dtype if concat_characters: if data.dtype.kind == 'S' and data.dtype.itemsize == 1: dimensions = dimensions[:-1] data = CharToStringArray(data) if mask_and_scale: if 'missing_value' in attributes: # missing_value is deprecated, but we still want to support it as # an alias for _FillValue. if ('_FillValue' in attributes and not utils.equivalent(attributes['_FillValue'], attributes['missing_value'])): raise ValueError("Discovered conflicting _FillValue " "and missing_value. Considering " "opening the offending dataset using " "decode_cf=False, corrected the attributes", "and decoding explicitly using " "xarray.conventions.decode_cf(ds)") attributes['_FillValue'] = attributes.pop('missing_value') fill_value = np.array(pop_to(attributes, encoding, '_FillValue')) if fill_value.size > 1: warnings.warn("variable has multiple fill values {0}, decoding " "all values to NaN.".format(str(fill_value)), RuntimeWarning, stacklevel=3) scale_factor = pop_to(attributes, encoding, 'scale_factor') add_offset = pop_to(attributes, encoding, 'add_offset') if ((fill_value is not None and not np.any(pd.isnull(fill_value))) or scale_factor is not None or add_offset is not None): if fill_value.dtype.kind in ['U', 'S']: dtype = object else: dtype = float data = MaskedAndScaledArray(data, fill_value, scale_factor, add_offset, dtype) if decode_times and 'units' in attributes: if 'since' in attributes['units']: # datetime units = pop_to(attributes, encoding, 'units') calendar = pop_to(attributes, encoding, 'calendar') data = DecodedCFDatetimeArray(data, units, calendar) elif attributes['units'] in TIME_UNITS: # timedelta units = pop_to(attributes, encoding, 'units') data = DecodedCFTimedeltaArray(data, units) if decode_endianness and not data.dtype.isnative: # do this last, so it's only done if we didn't already unmask/scale data = NativeEndiannessArray(data) original_dtype = data.dtype if 'dtype' in encoding: if original_dtype != encoding['dtype']: warnings.warn("CF decoding is overwriting dtype") else: encoding['dtype'] = original_dtype if 'dtype' in attributes and attributes['dtype'] == 'bool': del attributes['dtype'] data = BoolTypeArray(data) return Variable(dimensions, indexing.LazilyIndexedArray(data), attributes, encoding=encoding) def decode_cf_variables(variables, attributes, concat_characters=True, mask_and_scale=True, decode_times=True, decode_coords=True, drop_variables=None): """ Decode a several CF encoded variables. See: decode_cf_variable """ dimensions_used_by = defaultdict(list) for v in variables.values(): for d in v.dims: dimensions_used_by[d].append(v) def stackable(dim): # figure out if a dimension can be concatenated over if dim in variables: return False for v in dimensions_used_by[dim]: if v.dtype.kind != 'S' or dim != v.dims[-1]: return False return True coord_names = set() if isinstance(drop_variables, basestring): drop_variables = [drop_variables] elif drop_variables is None: drop_variables = [] drop_variables = set(drop_variables) new_vars = OrderedDict() for k, v in iteritems(variables): if k in drop_variables: continue concat = (concat_characters and v.dtype.kind == 'S' and v.ndim > 0 and stackable(v.dims[-1])) new_vars[k] = decode_cf_variable( v, concat_characters=concat, mask_and_scale=mask_and_scale, decode_times=decode_times) if decode_coords: var_attrs = new_vars[k].attrs if 'coordinates' in var_attrs: coord_str = var_attrs['coordinates'] var_coord_names = coord_str.split() if all(k in variables for k in var_coord_names): new_vars[k].encoding['coordinates'] = coord_str del var_attrs['coordinates'] coord_names.update(var_coord_names) if decode_coords and 'coordinates' in attributes: attributes = OrderedDict(attributes) coord_names.update(attributes.pop('coordinates').split()) return new_vars, attributes, coord_names def decode_cf(obj, concat_characters=True, mask_and_scale=True, decode_times=True, decode_coords=True, drop_variables=None): """Decode the given Dataset or Datastore according to CF conventions into a new Dataset. Parameters ---------- obj : Dataset or DataStore Object to decode. concat_characters : bool, optional Should character arrays be concatenated to strings, for example: ['h', 'e', 'l', 'l', 'o'] -> 'hello' mask_and_scale: bool, optional Lazily scale (using scale_factor and add_offset) and mask (using _FillValue). decode_times : bool, optional Decode cf times (e.g., integers since 'hours since 2000-01-01') to np.datetime64. decode_coords : bool, optional Use the 'coordinates' attribute on variable (or the dataset itself) to identify coordinates. drop_variables: string or iterable, optional A variable or list of variables to exclude from being parsed from the dataset. This may be useful to drop variables with problems or inconsistent values. Returns ------- decoded : Dataset """ from .core.dataset import Dataset from .backends.common import AbstractDataStore if isinstance(obj, Dataset): vars = obj._variables attrs = obj.attrs extra_coords = set(obj.coords) file_obj = obj._file_obj encoding = obj.encoding elif isinstance(obj, AbstractDataStore): vars, attrs = obj.load() extra_coords = set() file_obj = obj encoding = obj.get_encoding() else: raise TypeError('can only decode Dataset or DataStore objects') vars, attrs, coord_names = decode_cf_variables( vars, attrs, concat_characters, mask_and_scale, decode_times, decode_coords, drop_variables=drop_variables) ds = Dataset(vars, attrs=attrs) ds = ds.set_coords(coord_names.union(extra_coords).intersection(vars)) ds._file_obj = file_obj ds.encoding = encoding return ds def cf_decoder(variables, attributes, concat_characters=True, mask_and_scale=True, decode_times=True): """ Decode a set of CF encoded variables and attributes. See Also, decode_cf_variable Parameters ---------- variables : dict A dictionary mapping from variable name to xarray.Variable attributes : dict A dictionary mapping from attribute name to value concat_characters : bool Should character arrays be concatenated to strings, for example: ['h', 'e', 'l', 'l', 'o'] -> 'hello' mask_and_scale: bool Lazily scale (using scale_factor and add_offset) and mask (using _FillValue). decode_times : bool Decode cf times ('hours since 2000-01-01') to np.datetime64. Returns ------- decoded_variables : dict A dictionary mapping from variable name to xarray.Variable objects. decoded_attributes : dict A dictionary mapping from attribute name to values. """ variables, attributes, _ = decode_cf_variables( variables, attributes, concat_characters, mask_and_scale, decode_times) return variables, attributes def _encode_coordinates(variables, attributes, non_dim_coord_names): # calculate global and variable specific coordinates non_dim_coord_names = set(non_dim_coord_names) global_coordinates = non_dim_coord_names.copy() variable_coordinates = defaultdict(set) for coord_name in non_dim_coord_names: target_dims = variables[coord_name].dims for k, v in variables.items(): if (k not in non_dim_coord_names and k not in v.dims and any(d in target_dims for d in v.dims)): variable_coordinates[k].add(coord_name) global_coordinates.discard(coord_name) variables = OrderedDict((k, v.copy(deep=False)) for k, v in variables.items()) # These coordinates are saved according to CF conventions for var_name, coord_names in variable_coordinates.items(): attrs = variables[var_name].attrs if 'coordinates' in attrs: raise ValueError('cannot serialize coordinates because variable ' "%s already has an attribute 'coordinates'" % var_name) attrs['coordinates'] = ' '.join(map(str, coord_names)) # These coordinates are not associated with any particular variables, so we # save them under a global 'coordinates' attribute so xarray can roundtrip # the dataset faithfully. Because this serialization goes beyond CF # conventions, only do it if necessary. # Reference discussion: # http://mailman.cgd.ucar.edu/pipermail/cf-metadata/2014/057771.html if global_coordinates: attributes = OrderedDict(attributes) if 'coordinates' in attributes: raise ValueError('cannot serialize coordinates because the global ' "attribute 'coordinates' already exists") attributes['coordinates'] = ' '.join(map(str, global_coordinates)) return variables, attributes def encode_dataset_coordinates(dataset): """Encode coordinates on the given dataset object into variable specific and global attributes. When possible, this is done according to CF conventions. Parameters ---------- dataset : Dataset Object to encode. Returns ------- variables : dict attrs : dict """ non_dim_coord_names = set(dataset.coords) - set(dataset.dims) return _encode_coordinates(dataset._variables, dataset.attrs, non_dim_coord_names=non_dim_coord_names) def cf_encoder(variables, attributes): """ A function which takes a dicts of variables and attributes and encodes them to conform to CF conventions as much as possible. This includes masking, scaling, character array handling, and CF-time encoding. Decode a set of CF encoded variables and attributes. See Also, decode_cf_variable Parameters ---------- variables : dict A dictionary mapping from variable name to xarray.Variable attributes : dict A dictionary mapping from attribute name to value Returns ------- encoded_variables : dict A dictionary mapping from variable name to xarray.Variable, encoded_attributes : dict A dictionary mapping from attribute name to value See also: encode_cf_variable """ new_vars = OrderedDict((k, encode_cf_variable(v, name=k)) for k, v in iteritems(variables)) return new_vars, attributes ������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������xarray-0.9.1/xarray/convert.py����������������������������������������������������������������������0000644�1311115�0011610�00000003170�13043511243�016464� 0����������������������������������������������������������������������������������������������������ustar �shoyer��������������������������eng�����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������"""Functions for converting to and from xarray objects """ from __future__ import absolute_import from __future__ import division from __future__ import print_function import numpy as np from .core.dataarray import DataArray from .conventions import ( maybe_encode_timedelta, maybe_encode_datetime, decode_cf) ignored_attrs = set(['name', 'tileIndex']) def from_cdms2(variable): """Convert a cdms2 variable into an DataArray """ def get_cdms2_attrs(var): return dict((k, v) for k, v in var.attributes.items() if k not in ignored_attrs) values = np.asarray(variable) name = variable.id coords = [(v.id, np.asarray(v), get_cdms2_attrs(v)) for v in variable.getAxisList()] attrs = get_cdms2_attrs(variable) dataarray = DataArray(values, coords=coords, name=name, attrs=attrs) return decode_cf(dataarray.to_dataset())[dataarray.name] def to_cdms2(dataarray): """Convert a DataArray into a cdms2 variable """ # we don't want cdms2 to be a hard dependency import cdms2 def encode(var): return maybe_encode_timedelta(maybe_encode_datetime(var.variable)) def set_cdms2_attrs(var, attrs): for k, v in attrs.items(): setattr(var, k, v) axes = [] for dim in dataarray.dims: coord = encode(dataarray.coords[dim]) axis = cdms2.createAxis(coord.values, id=dim) set_cdms2_attrs(axis, coord.attrs) axes.append(axis) var = encode(dataarray) cdms2_var = cdms2.createVariable(var.values, axes=axes, id=dataarray.name) set_cdms2_attrs(cdms2_var, var.attrs) return cdms2_var ��������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������xarray-0.9.1/xarray/core/���������������������������������������������������������������������������0000755�1311115�0011610�00000000000�13043675421�015372� 5����������������������������������������������������������������������������������������������������ustar �shoyer��������������������������eng�����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������xarray-0.9.1/xarray/core/__init__.py����������������������������������������������������������������0000644�1311115�0011610�00000000000�12652753364�017501� 0����������������������������������������������������������������������������������������������������ustar �shoyer��������������������������eng�����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������xarray-0.9.1/xarray/core/alignment.py���������������������������������������������������������������0000644�1311115�0011610�00000046375�13043511243�017730� 0����������������������������������������������������������������������������������������������������ustar �shoyer��������������������������eng�����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������from __future__ import absolute_import from __future__ import division from __future__ import print_function import functools import operator from collections import defaultdict import numpy as np import pandas as pd from . import ops, utils from .common import _maybe_promote from .indexing import get_indexer from .pycompat import iteritems, OrderedDict, suppress from .utils import is_full_slice, is_dict_like from .variable import Variable, IndexVariable def _get_joiner(join): if join == 'outer': return functools.partial(functools.reduce, operator.or_) elif join == 'inner': return functools.partial(functools.reduce, operator.and_) elif join == 'left': return operator.itemgetter(0) elif join == 'right': return operator.itemgetter(-1) else: raise ValueError('invalid value for join: %s' % join) _DEFAULT_EXCLUDE = frozenset() def align(*objects, **kwargs): """align(*objects, join='inner', copy=True, indexes=None, exclude=frozenset()) Given any number of Dataset and/or DataArray objects, returns new objects with aligned indexes and dimension sizes. Array from the aligned objects are suitable as input to mathematical operators, because along each dimension they have the same index and size. Missing values (if ``join != 'inner'``) are filled with NaN. Parameters ---------- *objects : Dataset or DataArray Objects to align. join : {'outer', 'inner', 'left', 'right'}, optional Method for joining the indexes of the passed objects along each dimension: - 'outer': use the union of object indexes - 'inner': use the intersection of object indexes - 'left': use indexes from the first object with each dimension - 'right': use indexes from the last object with each dimension copy : bool, optional If ``copy=True``, data in the return values is always copied. If ``copy=False`` and reindexing is unnecessary, or can be performed with only slice operations, then the output may share memory with the input. In either case, new xarray objects are always returned. exclude : sequence of str, optional Dimensions that must be excluded from alignment indexes : dict-like, optional Any indexes explicitly provided with the `indexes` argument should be used in preference to the aligned indexes. Returns ------- aligned : same as *objects Tuple of objects with aligned coordinates. Raises ------ ValueError If any dimensions without labels on the arguments have different sizes, or a different size than the size of the aligned dimension labels. """ join = kwargs.pop('join', 'inner') copy = kwargs.pop('copy', True) indexes = kwargs.pop('indexes', None) exclude = kwargs.pop('exclude', _DEFAULT_EXCLUDE) if indexes is None: indexes = {} if kwargs: raise TypeError('align() got unexpected keyword arguments: %s' % list(kwargs)) if not indexes and len(objects) == 1: # fast path for the trivial case obj, = objects return (obj.copy(deep=copy),) all_indexes = defaultdict(list) unlabeled_dim_sizes = defaultdict(set) for obj in objects: for dim in obj.dims: if dim not in exclude: try: index = obj.indexes[dim] except KeyError: unlabeled_dim_sizes[dim].add(obj.sizes[dim]) else: all_indexes[dim].append(index) # We don't reindex over dimensions with all equal indexes for two reasons: # - It's faster for the usual case (already aligned objects). # - It ensures it's possible to do operations that don't require alignment # on indexes with duplicate values (which cannot be reindexed with # pandas). This is useful, e.g., for overwriting such duplicate indexes. joiner = _get_joiner(join) joined_indexes = {} for dim, matching_indexes in iteritems(all_indexes): if dim in indexes: index = utils.safe_cast_to_index(indexes[dim]) if (any(not index.equals(other) for other in matching_indexes) or dim in unlabeled_dim_sizes): joined_indexes[dim] = index else: if (any(not matching_indexes[0].equals(other) for other in matching_indexes[1:]) or dim in unlabeled_dim_sizes): index = joiner(matching_indexes) joined_indexes[dim] = index else: index = matching_indexes[0] if dim in unlabeled_dim_sizes: unlabeled_sizes = unlabeled_dim_sizes[dim] labeled_size = index.size if len(unlabeled_sizes | {labeled_size}) > 1: raise ValueError( 'arguments without labels along dimension %r cannot be ' 'aligned because they have different dimension size(s) %r ' 'than the size of the aligned dimension labels: %r' % (dim, unlabeled_sizes, labeled_size)) for dim in unlabeled_dim_sizes: if dim not in all_indexes: sizes = unlabeled_dim_sizes[dim] if len(sizes) > 1: raise ValueError( 'arguments without labels along dimension %r cannot be ' 'aligned because they have different dimension sizes: %r' % (dim, sizes)) result = [] for obj in objects: valid_indexers = {k: v for k, v in joined_indexes.items() if k in obj.dims} if not valid_indexers: # fast path for no reindexing necessary new_obj = obj.copy(deep=copy) else: new_obj = obj.reindex(copy=copy, **valid_indexers) result.append(new_obj) return tuple(result) def deep_align(objects, join='inner', copy=True, indexes=None, exclude=frozenset(), raise_on_invalid=True): """Align objects for merging, recursing into dictionary values. This function is not public API. """ if indexes is None: indexes = {} def is_alignable(obj): return hasattr(obj, 'indexes') and hasattr(obj, 'reindex') positions = [] keys = [] out = [] targets = [] no_key = object() not_replaced = object() for n, variables in enumerate(objects): if is_alignable(variables): positions.append(n) keys.append(no_key) targets.append(variables) out.append(not_replaced) elif is_dict_like(variables): for k, v in variables.items(): if is_alignable(v) and k not in indexes: # Skip variables in indexes for alignment, because these # should to be overwritten instead: # https://github.com/pydata/xarray/issues/725 positions.append(n) keys.append(k) targets.append(v) out.append(OrderedDict(variables)) elif raise_on_invalid: raise ValueError('object to align is neither an xarray.Dataset, ' 'an xarray.DataArray nor a dictionary: %r' % variables) else: out.append(variables) aligned = align(*targets, join=join, copy=copy, indexes=indexes, exclude=exclude) for position, key, aligned_obj in zip(positions, keys, aligned): if key is no_key: out[position] = aligned_obj else: out[position][key] = aligned_obj # something went wrong: we should have replaced all sentinel values assert all(arg is not not_replaced for arg in out) return out def reindex_like_indexers(target, other): """Extract indexers to align target with other. Not public API. Parameters ---------- target : Dataset or DataArray Object to be aligned. other : Dataset or DataArray Object to be aligned with. Returns ------- Dict[Any, pandas.Index] providing indexes for reindex keyword arguments. Raises ------ ValueError If any dimensions without labels have different sizes. """ indexers = {k: v for k, v in other.indexes.items() if k in target.dims} for dim in other.dims: if dim not in indexers and dim in target.dims: other_size = other.sizes[dim] target_size = target.sizes[dim] if other_size != target_size: raise ValueError('different size for unlabeled ' 'dimension on argument %r: %r vs %r' % (dim, other_size, target_size)) return indexers def reindex_variables(variables, sizes, indexes, indexers, method=None, tolerance=None, copy=True): """Conform a dictionary of aligned variables onto a new set of variables, filling in missing values with NaN. Not public API. Parameters ---------- variables : dict-like Dictionary of xarray.Variable objects. sizes : dict-like Dictionary from dimension names to integer sizes. indexes : dict-like Dictionary of xarray.IndexVariable objects associated with variables. indexers : dict Dictionary with keys given by dimension names and values given by arrays of coordinates tick labels. Any mis-matched coordinate values will be filled in with NaN, and any mis-matched dimension names will simply be ignored. method : {None, 'nearest', 'pad'/'ffill', 'backfill'/'bfill'}, optional Method to use for filling index values in ``indexers`` not found in this dataset: * None (default): don't fill gaps * pad / ffill: propagate last valid index value forward * backfill / bfill: propagate next valid index value backward * nearest: use nearest valid index value tolerance : optional Maximum distance between original and new labels for inexact matches. The values of the index at the matching locations most satisfy the equation ``abs(index[indexer] - target) <= tolerance``. copy : bool, optional If ``copy=True``, data in the return values is always copied. If ``copy=False`` and reindexing is unnecessary, or can be performed with only slice operations, then the output may share memory with the input. In either case, new xarray objects are always returned. Returns ------- reindexed : OrderedDict Another dict, with the items in variables but replaced indexes. """ # build up indexers for assignment along each dimension to_indexers = {} from_indexers = {} # size of reindexed dimensions new_sizes = {} for name, index in iteritems(indexes): if name in indexers: target = utils.safe_cast_to_index(indexers[name]) if not index.is_unique: raise ValueError( 'cannot reindex or align along dimension %r because the ' 'index has duplicate values' % name) indexer = get_indexer(index, target, method, tolerance) new_sizes[name] = len(target) # Note pandas uses negative values from get_indexer to signify # values that are missing in the index # The non-negative values thus indicate the non-missing values to_indexers[name] = indexer >= 0 if to_indexers[name].all(): # If an indexer includes no negative values, then the # assignment can be to a full-slice (which is much faster, # and means we won't need to fill in any missing values) to_indexers[name] = slice(None) from_indexers[name] = indexer[to_indexers[name]] if np.array_equal(from_indexers[name], np.arange(len(index))): # If the indexer is equal to the original index, use a full # slice object to speed up selection and so we can avoid # unnecessary copies from_indexers[name] = slice(None) for dim in sizes: if dim not in indexes and dim in indexers: existing_size = sizes[dim] new_size = utils.safe_cast_to_index(indexers[dim]).size if existing_size != new_size: raise ValueError( 'cannot reindex or align along dimension %r without an ' 'index because its size %r is different from the size of ' 'the new index %r' % (dim, existing_size, new_size)) def any_not_full_slices(indexers): return any(not is_full_slice(idx) for idx in indexers) def var_indexers(var, indexers): return tuple(indexers.get(d, slice(None)) for d in var.dims) # create variables for the new dataset reindexed = OrderedDict() for dim, indexer in indexers.items(): if dim in variables: var = variables[dim] args = (var.attrs, var.encoding) else: args = () reindexed[dim] = IndexVariable((dim,), indexers[dim], *args) for name, var in iteritems(variables): if name not in indexers: assign_to = var_indexers(var, to_indexers) assign_from = var_indexers(var, from_indexers) if any_not_full_slices(assign_to): # there are missing values to in-fill data = var[assign_from].data dtype, fill_value = _maybe_promote(var.dtype) if isinstance(data, np.ndarray): shape = tuple(new_sizes.get(dim, size) for dim, size in zip(var.dims, var.shape)) new_data = np.empty(shape, dtype=dtype) new_data[...] = fill_value # create a new Variable so we can use orthogonal indexing # use fastpath=True to avoid dtype inference new_var = Variable(var.dims, new_data, var.attrs, fastpath=True) new_var[assign_to] = data else: # dask array data = data.astype(dtype, copy=False) for axis, indexer in enumerate(assign_to): if not is_full_slice(indexer): indices = np.cumsum(indexer)[~indexer] data = ops.insert(data, indices, fill_value, axis=axis) new_var = Variable(var.dims, data, var.attrs, fastpath=True) elif any_not_full_slices(assign_from): # type coercion is not necessary as there are no missing # values new_var = var[assign_from] else: # no reindexing is necessary # here we need to manually deal with copying data, since # we neither created a new ndarray nor used fancy indexing new_var = var.copy(deep=copy) reindexed[name] = new_var return reindexed def broadcast(*args, **kwargs): """Explicitly broadcast any number of DataArray or Dataset objects against one another. xarray objects automatically broadcast against each other in arithmetic operations, so this function should not be necessary for normal use. If no change is needed, the input data is returned to the output without being copied. Parameters ---------- *args : DataArray or Dataset objects Arrays to broadcast against each other. exclude : sequence of str, optional Dimensions that must not be broadcasted Returns ------- broadcast : tuple of xarray objects The same data as the input arrays, but with additional dimensions inserted so that all data arrays have the same dimensions and shape. Examples -------- Broadcast two data arrays against one another to fill out their dimensions: >>> a = xr.DataArray([1, 2, 3], dims='x') >>> b = xr.DataArray([5, 6], dims='y') >>> a array([1, 2, 3]) Coordinates: * x (x) int64 0 1 2 >>> b array([5, 6]) Coordinates: * y (y) int64 0 1 >>> a2, b2 = xr.broadcast(a, b) >>> a2 array([[1, 1], [2, 2], [3, 3]]) Coordinates: * x (x) int64 0 1 2 * y (y) int64 0 1 >>> b2 array([[5, 6], [5, 6], [5, 6]]) Coordinates: * y (y) int64 0 1 * x (x) int64 0 1 2 Fill out the dimensions of all data variables in a dataset: >>> ds = xr.Dataset({'a': a, 'b': b}) >>> ds2, = xr.broadcast(ds) # use tuple unpacking to extract one dataset >>> ds2 Dimensions: (x: 3, y: 2) Coordinates: * x (x) int64 0 1 2 * y (y) int64 0 1 Data variables: a (x, y) int64 1 1 2 2 3 3 b (x, y) int64 5 6 5 6 5 6 """ from .dataarray import DataArray from .dataset import Dataset exclude = kwargs.pop('exclude', None) if exclude is None: exclude = set() if kwargs: raise TypeError('broadcast() got unexpected keyword arguments: %s' % list(kwargs)) args = align(*args, join='outer', copy=False, exclude=exclude) common_coords = OrderedDict() dims_map = OrderedDict() for arg in args: for dim in arg.dims: if dim not in common_coords and dim not in exclude: dims_map[dim] = arg.sizes[dim] if dim in arg.coords: common_coords[dim] = arg.coords[dim].variable def _expand_dims(var): # Add excluded dims to a copy of dims_map var_dims_map = dims_map.copy() for dim in exclude: with suppress(ValueError): # ignore dim not in var.dims var_dims_map[dim] = var.shape[var.dims.index(dim)] return var.expand_dims(var_dims_map) def _broadcast_array(array): data = _expand_dims(array.variable) coords = OrderedDict(array.coords) coords.update(common_coords) return DataArray(data, coords, data.dims, name=array.name, attrs=array.attrs, encoding=array.encoding) def _broadcast_dataset(ds): data_vars = OrderedDict( (k, _expand_dims(ds.variables[k])) for k in ds.data_vars) coords = OrderedDict(ds.coords) coords.update(common_coords) return Dataset(data_vars, coords, ds.attrs) result = [] for arg in args: if isinstance(arg, DataArray): result.append(_broadcast_array(arg)) elif isinstance(arg, Dataset): result.append(_broadcast_dataset(arg)) else: raise ValueError('all input must be Dataset or DataArray objects') return tuple(result) def broadcast_arrays(*args): import warnings warnings.warn('xarray.broadcast_arrays is deprecated: use ' 'xarray.broadcast instead', DeprecationWarning, stacklevel=2) return broadcast(*args) �������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������xarray-0.9.1/xarray/core/apply_dask_ufunc.py��������������������������������������������������������0000644�1311115�0011610�00000004444�12765420630�021301� 0����������������������������������������������������������������������������������������������������ustar �shoyer��������������������������eng�����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������ def _deep_unpack_list(arg): if isinstance(arg, list): arg, = arg return _deep_unpack_list(arg) return arg def apply_dask_ufunc(func, *args, **kwargs): import dask.array as da import toolz # required dependency of dask.array signature = kwargs.pop('signature') dtype = kwargs.pop('dtype', None) if kwargs: raise TypeError('apply_dask_ufunc() got unexpected keyword ' 'arguments: %s' % list(kwargs)) if not all(isinstance(arg, da.Array) for arg in args): raise NotImplementedError( "dask_array='auto' currently requires only supports dask arrays " 'if all arguments are dask arrays') if signature.n_outputs != 1: raise NotImplementedError( "cannot yet use dask_array='auto' with functions that " 'return multiple values') if signature.all_output_core_dims - signature.all_input_core_dims: raise NotImplementedError( "cannot yet use dask_array='auto' with functions that create " 'new dimensions') broadcast_dims = [tuple(range(arg.ndim - len(core_dims))[::-1]) for arg, core_dims in zip(args, signature.input_core_dims)] input_dims = [bc_dims + core_dims for bc_dims, core_dims in zip(broadcast_dims, signature.input_core_dims)] n_broadcast_dims = max(len(dims) for dims in broadcast_dims) output_dims = [tuple(range(n_broadcast_dims))[::-1] + core_dims for core_dims in signature.output_core_dims] dropped = signature.all_input_core_dims - signature.all_output_core_dims for arg, dims in zip(args, input_dims): for dropped_dim in dropped: if (dropped_dim in dims and len(arg.chunks[dims.index(dropped_dim)]) != 1): raise ValueError('dimension %r dropped in the output does not ' 'consist of exactly one chunk on all arrays ' 'in the inputs' % dropped_dim) out_ind, = output_dims # only one output currently supported atop_args = [b for a in (args, input_dims) for b in a] func2 = toolz.functoolz.compose(func, _deep_unpack_list) return da.atop(func2, out_ind, *atop_args, dtype=dtype) ����������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������xarray-0.9.1/xarray/core/combine.py�����������������������������������������������������������������0000644�1311115�0011610�00000040473�13043511243�017357� 0����������������������������������������������������������������������������������������������������ustar �shoyer��������������������������eng�����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������from __future__ import absolute_import from __future__ import division from __future__ import print_function import warnings import pandas as pd from . import utils from .alignment import align from .merge import merge from .pycompat import iteritems, OrderedDict, basestring from .variable import Variable, as_variable, IndexVariable, concat as concat_vars def concat(objs, dim=None, data_vars='all', coords='different', compat='equals', positions=None, indexers=None, mode=None, concat_over=None): """Concatenate xarray objects along a new or existing dimension. Parameters ---------- objs : sequence of Dataset and DataArray objects xarray objects to concatenate together. Each object is expected to consist of variables and coordinates with matching shapes except for along the concatenated dimension. dim : str or DataArray or pandas.Index Name of the dimension to concatenate along. This can either be a new dimension name, in which case it is added along axis=0, or an existing dimension name, in which case the location of the dimension is unchanged. If dimension is provided as a DataArray or Index, its name is used as the dimension to concatenate along and the values are added as a coordinate. data_vars : {'minimal', 'different', 'all' or list of str}, optional These data variables will be concatenated together: * 'minimal': Only data variables in which the dimension already appears are included. * 'different': Data variables which are not equal (ignoring attributes) across all datasets are also concatenated (as well as all for which dimension already appears). Beware: this option may load the data payload of data variables into memory if they are not already loaded. * 'all': All data variables will be concatenated. * list of str: The listed data variables will be concatenated, in addition to the 'minimal' data variables. If objects are DataArrays, data_vars must be 'all'. coords : {'minimal', 'different', 'all' o list of str}, optional These coordinate variables will be concatenated together: * 'minimal': Only coordinates in which the dimension already appears are included. * 'different': Coordinates which are not equal (ignoring attributes) across all datasets are also concatenated (as well as all for which dimension already appears). Beware: this option may load the data payload of coordinate variables into memory if they are not already loaded. * 'all': All coordinate variables will be concatenated, except those corresponding to other dimensions. * list of str: The listed coordinate variables will be concatenated, in addition the 'minimal' coordinates. compat : {'equals', 'identical'}, optional String indicating how to compare non-concatenated variables and dataset global attributes for potential conflicts. 'equals' means that all variable values and dimensions must be the same; 'identical' means that variable attributes and global attributes must also be equal. positions : None or list of integer arrays, optional List of integer arrays which specifies the integer positions to which to assign each dataset along the concatenated dimension. If not supplied, objects are concatenated in the provided order. indexers, mode, concat_over : deprecated Returns ------- concatenated : type of objs See also -------- merge auto_combine """ # TODO: add join and ignore_index arguments copied from pandas.concat # TODO: support concatenating scalar coordinates even if the concatenated # dimension already exists from .dataset import Dataset from .dataarray import DataArray try: first_obj, objs = utils.peek_at(objs) except StopIteration: raise ValueError('must supply at least one object to concatenate') if dim is None: warnings.warn('the `dim` argument to `concat` will be required ' 'in a future version of xarray; for now, setting it to ' "the old default of 'concat_dim'", FutureWarning, stacklevel=2) dim = 'concat_dims' if indexers is not None: # pragma: nocover warnings.warn('indexers has been renamed to positions; the alias ' 'will be removed in a future version of xarray', FutureWarning, stacklevel=2) positions = indexers if mode is not None: raise ValueError('`mode` is no longer a valid argument to ' 'xarray.concat; it has been split into the `data_vars` ' 'and `coords` arguments') if concat_over is not None: raise ValueError('`concat_over` is no longer a valid argument to ' 'xarray.concat; it has been split into the `data_vars` ' 'and `coords` arguments') if isinstance(first_obj, DataArray): f = _dataarray_concat elif isinstance(first_obj, Dataset): f = _dataset_concat else: raise TypeError('can only concatenate xarray Dataset and DataArray ' 'objects, got %s' % type(first_obj)) return f(objs, dim, data_vars, coords, compat, positions) def _calc_concat_dim_coord(dim): """ Infer the dimension name and 1d coordinate variable (if appropriate) for concatenating along the new dimension. """ if isinstance(dim, basestring): coord = None elif not hasattr(dim, 'dims'): # dim is not a DataArray or IndexVariable dim_name = getattr(dim, 'name', None) if dim_name is None: dim_name = 'concat_dim' coord = IndexVariable(dim_name, dim) dim = dim_name elif not hasattr(dim, 'name'): coord = as_variable(dim).to_index_variable() dim, = coord.dims else: coord = dim dim, = coord.dims return dim, coord def _calc_concat_over(datasets, dim, data_vars, coords): """ Determine which dataset variables need to be concatenated in the result, and which can simply be taken from the first dataset. """ def process_subset_opt(opt, subset): if subset == 'coords': subset_long_name = 'coordinates' else: subset_long_name = 'data variables' if isinstance(opt, basestring): if opt == 'different': def differs(vname): # simple helper function which compares a variable # across all datasets and indicates whether that # variable differs or not. v = datasets[0].variables[vname] return any(not ds.variables[vname].equals(v) for ds in datasets[1:]) # all nonindexes that are not the same in each dataset concat_new = set(k for k in getattr(datasets[0], subset) if k not in concat_over and differs(k)) elif opt == 'all': concat_new = (set(getattr(datasets[0], subset)) - set(datasets[0].dims)) elif opt == 'minimal': concat_new = set() else: raise ValueError("unexpected value for concat_%s: %s" % (subset, opt)) else: invalid_vars = [k for k in opt if k not in getattr(datasets[0], subset)] if invalid_vars: raise ValueError('some variables in %s are not ' '%s on the first dataset: %s' % (subset, subset_long_name, invalid_vars)) concat_new = set(opt) return concat_new concat_over = set() for ds in datasets: concat_over.update(k for k, v in ds.variables.items() if dim in v.dims) concat_over.update(process_subset_opt(data_vars, 'data_vars')) concat_over.update(process_subset_opt(coords, 'coords')) if dim in datasets[0]: concat_over.add(dim) return concat_over def _dataset_concat(datasets, dim, data_vars, coords, compat, positions): """ Concatenate a sequence of datasets along a new or existing dimension """ from .dataset import Dataset, as_dataset if compat not in ['equals', 'identical']: raise ValueError("compat=%r invalid: must be 'equals' " "or 'identical'" % compat) dim, coord = _calc_concat_dim_coord(dim) datasets = [as_dataset(ds) for ds in datasets] datasets = align(*datasets, join='outer', copy=False, exclude=[dim]) concat_over = _calc_concat_over(datasets, dim, data_vars, coords) def insert_result_variable(k, v): assert isinstance(v, Variable) if k in datasets[0].coords: result_coord_names.add(k) result_vars[k] = v # create the new dataset and add constant variables result_vars = OrderedDict() result_coord_names = set(datasets[0].coords) result_attrs = datasets[0].attrs for k, v in datasets[0].variables.items(): if k not in concat_over: insert_result_variable(k, v) # check that global attributes and non-concatenated variables are fixed # across all datasets for ds in datasets[1:]: if (compat == 'identical' and not utils.dict_equiv(ds.attrs, result_attrs)): raise ValueError('dataset global attributes not equal') for k, v in iteritems(ds.variables): if k not in result_vars and k not in concat_over: raise ValueError('encountered unexpected variable %r' % k) elif (k in result_coord_names) != (k in ds.coords): raise ValueError('%r is a coordinate in some datasets but not ' 'others' % k) elif (k in result_vars and k != dim and not getattr(v, compat)(result_vars[k])): verb = 'equal' if compat == 'equals' else compat raise ValueError( 'variable %r not %s across datasets' % (k, verb)) # we've already verified everything is consistent; now, calculate # shared dimension sizes so we can expand the necessary variables dim_lengths = [ds.dims.get(dim, 1) for ds in datasets] non_concat_dims = {} for ds in datasets: non_concat_dims.update(ds.dims) non_concat_dims.pop(dim, None) def ensure_common_dims(vars): # ensure each variable with the given name shares the same # dimensions and the same shape for all of them except along the # concat dimension common_dims = tuple(pd.unique([d for v in vars for d in v.dims])) if dim not in common_dims: common_dims = (dim,) + common_dims for var, dim_len in zip(vars, dim_lengths): if var.dims != common_dims: common_shape = tuple(non_concat_dims.get(d, dim_len) for d in common_dims) var = var.expand_dims(common_dims, common_shape) yield var # stack up each variable to fill-out the dataset (in order) for k in datasets[0].variables: if k in concat_over: vars = ensure_common_dims([ds.variables[k] for ds in datasets]) combined = concat_vars(vars, dim, positions) insert_result_variable(k, combined) result = Dataset(result_vars, attrs=result_attrs) result = result.set_coords(result_coord_names) if coord is not None: # add concat dimension last to ensure that its in the final Dataset result[coord.name] = coord return result def _dataarray_concat(arrays, dim, data_vars, coords, compat, positions): arrays = list(arrays) if data_vars != 'all': raise ValueError('data_vars is not a valid argument when ' 'concatenating DataArray objects') datasets = [] for n, arr in enumerate(arrays): if n == 0: name = arr.name elif name != arr.name: if compat == 'identical': raise ValueError('array names not identical') else: arr = arr.rename(name) datasets.append(arr._to_temp_dataset()) ds = _dataset_concat(datasets, dim, data_vars, coords, compat, positions) return arrays[0]._from_temp_dataset(ds, name) def _auto_concat(datasets, dim=None): if len(datasets) == 1: return datasets[0] else: if dim is None: ds0 = datasets[0] ds1 = datasets[1] concat_dims = set(ds0.dims) if ds0.dims != ds1.dims: dim_tuples = set(ds0.dims.items()) - set(ds1.dims.items()) concat_dims = set(i for i, _ in dim_tuples) if len(concat_dims) > 1: concat_dims = set(d for d in concat_dims if not ds0[d].equals(ds1[d])) if len(concat_dims) > 1: raise ValueError('too many different dimensions to ' 'concatenate: %s' % concat_dims) elif len(concat_dims) == 0: raise ValueError('cannot infer dimension to concatenate: ' 'supply the ``concat_dim`` argument ' 'explicitly') dim, = concat_dims return concat(datasets, dim=dim) _CONCAT_DIM_DEFAULT = '__infer_concat_dim__' def auto_combine(datasets, concat_dim=_CONCAT_DIM_DEFAULT, compat='no_conflicts'): """Attempt to auto-magically combine the given datasets into one. This method attempts to combine a list of datasets into a single entity by inspecting metadata and using a combination of concat and merge. It does not concatenate along more than one dimension or sort data under any circumstances. It does align coordinates, but different variables on datasets can cause it to fail under some scenarios. In complex cases, you may need to clean up your data and use ``concat``/``merge`` explicitly. ``auto_combine`` works well if you have N years of data and M data variables, and each combination of a distinct time period and set of data variables is saved its own dataset. Parameters ---------- datasets : sequence of xarray.Dataset Dataset objects to merge. concat_dim : str or DataArray or Index, optional Dimension along which to concatenate variables, as used by :py:func:`xarray.concat`. You only need to provide this argument if the dimension along which you want to concatenate is not a dimension in the original datasets, e.g., if you want to stack a collection of 2D arrays along a third dimension. By default, xarray attempts to infer this argument by examining component files. Set ``concat_dim=None`` explicitly to disable concatenation. compat : {'identical', 'equals', 'broadcast_equals', 'no_conflicts'}, optional String indicating how to compare variables of the same name for potential conflicts: - 'broadcast_equals': all values must be equal when variables are broadcast against each other to ensure common dimensions. - 'equals': all values and dimensions must be the same. - 'identical': all values, dimensions and attributes must be the same. - 'no_conflicts': only values which are not null in both datasets must be equal. The returned dataset then contains the combination of all non-null values. Returns ------- combined : xarray.Dataset See also -------- concat Dataset.merge """ from toolz import itertoolz if concat_dim is not None: dim = None if concat_dim is _CONCAT_DIM_DEFAULT else concat_dim grouped = itertoolz.groupby(lambda ds: tuple(sorted(ds.data_vars)), datasets).values() concatenated = [_auto_concat(ds, dim=dim) for ds in grouped] else: concatenated = datasets merged = merge(concatenated, compat=compat) return merged �����������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������xarray-0.9.1/xarray/core/common.py������������������������������������������������������������������0000644�1311115�0011610�00000070715�13043543222�017237� 0����������������������������������������������������������������������������������������������������ustar �shoyer��������������������������eng�����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������from __future__ import absolute_import from __future__ import division from __future__ import print_function import numpy as np import pandas as pd from .pycompat import (basestring, suppress, dask_array_type, OrderedDict) from . import formatting from .utils import SortedKeysDict, not_implemented, Frozen class ImplementsArrayReduce(object): @classmethod def _reduce_method(cls, func, include_skipna, numeric_only): if include_skipna: def wrapped_func(self, dim=None, axis=None, skipna=None, keep_attrs=False, **kwargs): return self.reduce(func, dim, axis, keep_attrs=keep_attrs, skipna=skipna, allow_lazy=True, **kwargs) else: def wrapped_func(self, dim=None, axis=None, keep_attrs=False, **kwargs): return self.reduce(func, dim, axis, keep_attrs=keep_attrs, allow_lazy=True, **kwargs) return wrapped_func _reduce_extra_args_docstring = \ """dim : str or sequence of str, optional Dimension(s) over which to apply `{name}`. axis : int or sequence of int, optional Axis(es) over which to apply `{name}`. Only one of the 'dim' and 'axis' arguments can be supplied. If neither are supplied, then `{name}` is calculated over axes.""" _cum_extra_args_docstring = \ """dim : str or sequence of str, optional Dimension over which to apply `{name}`. axis : int or sequence of int, optional Axis over which to apply `{name}`. Only one of the 'dim' and 'axis' arguments can be supplied.""" class ImplementsDatasetReduce(object): @classmethod def _reduce_method(cls, func, include_skipna, numeric_only): if include_skipna: def wrapped_func(self, dim=None, keep_attrs=False, skipna=None, **kwargs): return self.reduce(func, dim, keep_attrs, skipna=skipna, numeric_only=numeric_only, allow_lazy=True, **kwargs) else: def wrapped_func(self, dim=None, keep_attrs=False, **kwargs): return self.reduce(func, dim, keep_attrs, numeric_only=numeric_only, allow_lazy=True, **kwargs) return wrapped_func _reduce_extra_args_docstring = \ """dim : str or sequence of str, optional Dimension(s) over which to apply `{name}`. By default `{name}` is applied over all dimensions.""" _cum_extra_args_docstring = \ """dim : str or sequence of str, optional Dimension over which to apply `{name}`. axis : int or sequence of int, optional Axis over which to apply `{name}`. Only one of the 'dim' and 'axis' arguments can be supplied.""" class ImplementsRollingArrayReduce(object): @classmethod def _reduce_method(cls, func): def wrapped_func(self, **kwargs): return self.reduce(func, **kwargs) return wrapped_func @classmethod def _bottleneck_reduce(cls, func): def wrapped_func(self, **kwargs): from .dataarray import DataArray if isinstance(self.obj.data, dask_array_type): raise NotImplementedError( 'Rolling window operation does not work with dask arrays') # bottleneck doesn't allow min_count to be 0, although it should # work the same as if min_count = 1 if self.min_periods is not None and self.min_periods == 0: min_count = self.min_periods + 1 else: min_count = self.min_periods values = func(self.obj.data, window=self.window, min_count=min_count, axis=self._axis_num) result = DataArray(values, self.obj.coords) if self.center: result = self._center_result(result) return result return wrapped_func @classmethod def _bottleneck_reduce_without_min_count(cls, func): def wrapped_func(self, **kwargs): from .dataarray import DataArray if self.min_periods is not None: raise ValueError('Rolling.median does not accept min_periods') if isinstance(self.obj.data, dask_array_type): raise NotImplementedError( 'Rolling window operation does not work with dask arrays') values = func(self.obj.data, window=self.window, axis=self._axis_num) result = DataArray(values, self.obj.coords) if self.center: result = self._center_result(result) return result return wrapped_func class AbstractArray(ImplementsArrayReduce, formatting.ReprMixin): """Shared base class for DataArray and Variable.""" def __bool__(self): return bool(self.values) # Python 3 uses __bool__, Python 2 uses __nonzero__ __nonzero__ = __bool__ def __float__(self): return float(self.values) def __int__(self): return int(self.values) def __complex__(self): return complex(self.values) def __long__(self): return long(self.values) def __array__(self, dtype=None): return np.asarray(self.values, dtype=dtype) def __repr__(self): return formatting.array_repr(self) def _iter(self): for n in range(len(self)): yield self[n] def __iter__(self): if self.ndim == 0: raise TypeError('iteration over a 0-d array') return self._iter() @property def T(self): return self.transpose() def get_axis_num(self, dim): """Return axis number(s) corresponding to dimension(s) in this array. Parameters ---------- dim : str or iterable of str Dimension name(s) for which to lookup axes. Returns ------- int or tuple of int Axis number or numbers corresponding to the given dimensions. """ if isinstance(dim, basestring): return self._get_axis_num(dim) else: return tuple(self._get_axis_num(d) for d in dim) def _get_axis_num(self, dim): try: return self.dims.index(dim) except ValueError: raise ValueError("%r not found in array dimensions %r" % (dim, self.dims)) @property def sizes(self): """Ordered mapping from dimension names to lengths. Immutable. See also -------- Dataset.sizes """ return Frozen(OrderedDict(zip(self.dims, self.shape))) class AttrAccessMixin(object): """Mixin class that allows getting keys with attribute access """ _initialized = False @property def _attr_sources(self): """List of places to look-up items for attribute-style access""" return [] def __getattr__(self, name): if name != '__setstate__': # this avoids an infinite loop when pickle looks for the # __setstate__ attribute before the xarray object is initialized for source in self._attr_sources: with suppress(KeyError): return source[name] raise AttributeError("%r object has no attribute %r" % (type(self).__name__, name)) def __setattr__(self, name, value): if self._initialized: try: # Allow setting instance variables if they already exist # (e.g., _attrs). We use __getattribute__ instead of hasattr # to avoid key lookups with attribute-style access. self.__getattribute__(name) except AttributeError: raise AttributeError( "cannot set attribute %r on a %r object. Use __setitem__ " "style assignment (e.g., `ds['name'] = ...`) instead to " "assign variables." % (name, type(self).__name__)) object.__setattr__(self, name, value) def __dir__(self): """Provide method name lookup and completion. Only provide 'public' methods. """ extra_attrs = [ item for sublist in self._attr_sources for item in sublist if isinstance(item, basestring)] return sorted(set(dir(type(self)) + extra_attrs)) def get_squeeze_dims(xarray_obj, dim): """Get a list of dimensions to squeeze out. """ if dim is None: dim = [d for d, s in xarray_obj.sizes.items() if s == 1] else: if isinstance(dim, basestring): dim = [dim] if any(xarray_obj.sizes[k] > 1 for k in dim): raise ValueError('cannot select a dimension to squeeze out ' 'which has length greater than one') return dim class BaseDataObject(AttrAccessMixin): """Shared base class for Dataset and DataArray.""" def squeeze(self, dim=None, drop=False): """Return a new object with squeezed data. Parameters ---------- dim : None or str or tuple of str, optional Selects a subset of the length one dimensions. If a dimension is selected with length greater than one, an error is raised. If None, all length one dimensions are squeezed. drop : bool, optional If ``drop=True``, drop squeezed coordinates instead of making them scalar. Returns ------- squeezed : same type as caller This object, but with with all or a subset of the dimensions of length 1 removed. See Also -------- numpy.squeeze """ dims = get_squeeze_dims(self, dim) return self.isel(drop=drop, **{d: 0 for d in dims}) def get_index(self, key): """Get an index for a dimension, with fall-back to a default RangeIndex """ if key not in self.dims: raise KeyError(key) try: return self.indexes[key] except KeyError: return pd.Index(range(self.sizes[key]), name=key) def _calc_assign_results(self, kwargs): results = SortedKeysDict() for k, v in kwargs.items(): if callable(v): results[k] = v(self) else: results[k] = v return results def assign_coords(self, **kwargs): """Assign new coordinates to this object, returning a new object with all the original data in addition to the new coordinates. Parameters ---------- kwargs : keyword, value pairs keywords are the variables names. If the values are callable, they are computed on this object and assigned to new coordinate variables. If the values are not callable, (e.g. a DataArray, scalar, or array), they are simply assigned. Returns ------- assigned : same type as caller A new object with the new coordinates in addition to the existing data. Notes ----- Since ``kwargs`` is a dictionary, the order of your arguments may not be preserved, and so the order of the new variables is not well defined. Assigning multiple variables within the same ``assign_coords`` is possible, but you cannot reference other variables created within the same ``assign_coords`` call. See also -------- Dataset.assign """ data = self.copy(deep=False) results = self._calc_assign_results(kwargs) data.coords.update(results) return data def pipe(self, func, *args, **kwargs): """ Apply func(self, *args, **kwargs) This method replicates the pandas method of the same name. Parameters ---------- func : function function to apply to this xarray object (Dataset/DataArray). ``args``, and ``kwargs`` are passed into ``func``. Alternatively a ``(callable, data_keyword)`` tuple where ``data_keyword`` is a string indicating the keyword of ``callable`` that expects the xarray object. args : positional arguments passed into ``func``. kwargs : a dictionary of keyword arguments passed into ``func``. Returns ------- object : the return type of ``func``. Notes ----- Use ``.pipe`` when chaining together functions that expect xarray or pandas objects, e.g., instead of writing >>> f(g(h(ds), arg1=a), arg2=b, arg3=c) You can write >>> (ds.pipe(h) ... .pipe(g, arg1=a) ... .pipe(f, arg2=b, arg3=c) ... ) If you have a function that takes the data as (say) the second argument, pass a tuple indicating which keyword expects the data. For example, suppose ``f`` takes its data as ``arg2``: >>> (ds.pipe(h) ... .pipe(g, arg1=a) ... .pipe((f, 'arg2'), arg1=a, arg3=c) ... ) See Also -------- pandas.DataFrame.pipe """ if isinstance(func, tuple): func, target = func if target in kwargs: msg = '%s is both the pipe target and a keyword argument' % target raise ValueError(msg) kwargs[target] = self return func(*args, **kwargs) else: return func(self, *args, **kwargs) def groupby(self, group, squeeze=True): """Returns a GroupBy object for performing grouped operations. Parameters ---------- group : str, DataArray or IndexVariable Array whose unique values should be used to group this array. If a string, must be the name of a variable contained in this dataset. squeeze : boolean, optional If "group" is a dimension of any arrays in this dataset, `squeeze` controls whether the subarrays have a dimension of length 1 along that dimension or if the dimension is squeezed out. Returns ------- grouped : GroupBy A `GroupBy` object patterned after `pandas.GroupBy` that can be iterated over in the form of `(unique_value, grouped_array)` pairs. """ return self.groupby_cls(self, group, squeeze=squeeze) def groupby_bins(self, group, bins, right=True, labels=None, precision=3, include_lowest=False, squeeze=True): """Returns a GroupBy object for performing grouped operations. Rather than using all unique values of `group`, the values are discretized first by applying `pandas.cut` [1]_ to `group`. Parameters ---------- group : str, DataArray or IndexVariable Array whose binned values should be used to group this array. If a string, must be the name of a variable contained in this dataset. bins : int or array of scalars If bins is an int, it defines the number of equal-width bins in the range of x. However, in this case, the range of x is extended by .1% on each side to include the min or max values of x. If bins is a sequence it defines the bin edges allowing for non-uniform bin width. No extension of the range of x is done in this case. right : boolean, optional Indicates whether the bins include the rightmost edge or not. If right == True (the default), then the bins [1,2,3,4] indicate (1,2], (2,3], (3,4]. labels : array or boolean, default None Used as labels for the resulting bins. Must be of the same length as the resulting bins. If False, string bin labels are assigned by `pandas.cut`. precision : int The precision at which to store and display the bins labels. include_lowest : bool Whether the first interval should be left-inclusive or not. squeeze : boolean, optional If "group" is a dimension of any arrays in this dataset, `squeeze` controls whether the subarrays have a dimension of length 1 along that dimension or if the dimension is squeezed out. Returns ------- grouped : GroupBy A `GroupBy` object patterned after `pandas.GroupBy` that can be iterated over in the form of `(unique_value, grouped_array)` pairs. The name of the group has the added suffix `_bins` in order to distinguish it from the original variable. References ---------- .. [1] http://pandas.pydata.org/pandas-docs/stable/generated/pandas.cut.html """ return self.groupby_cls(self, group, squeeze=squeeze, bins=bins, cut_kwargs={'right': right, 'labels': labels, 'precision': precision, 'include_lowest': include_lowest}) def rolling(self, min_periods=None, center=False, **windows): """ Rolling window object. Rolling window aggregations are much faster when bottleneck is installed. Parameters ---------- min_periods : int, default None Minimum number of observations in window required to have a value (otherwise result is NA). The default, None, is equivalent to setting min_periods equal to the size of the window. center : boolean, default False Set the labels at the center of the window. **windows : dim=window dim : str Name of the dimension to create the rolling iterator along (e.g., `time`). window : int Size of the moving window. Returns ------- rolling : type of input argument """ return self.rolling_cls(self, min_periods=min_periods, center=center, **windows) def resample(self, freq, dim, how='mean', skipna=None, closed=None, label=None, base=0, keep_attrs=False): """Resample this object to a new temporal resolution. Handles both downsampling and upsampling. Upsampling with filling is not yet supported; if any intervals contain no values in the original object, they will be given the value ``NaN``. Parameters ---------- freq : str String in the '#offset' to specify the step-size along the resampled dimension, where '#' is an (optional) integer multipler (default 1) and 'offset' is any pandas date offset alias. Examples of valid offsets include: * 'AS': year start * 'QS-DEC': quarterly, starting on December 1 * 'MS': month start * 'D': day * 'H': hour * 'Min': minute The full list of these offset aliases is documented in pandas [1]_. dim : str Name of the dimension to resample along (e.g., 'time'). how : str or func, optional Used for downsampling. If a string, ``how`` must be a valid aggregation operation supported by xarray. Otherwise, ``how`` must be a function that can be called like ``how(values, axis)`` to reduce ndarray values along the given axis. Valid choices that can be provided as a string include all the usual Dataset/DataArray aggregations (``all``, ``any``, ``argmax``, ``argmin``, ``max``, ``mean``, ``median``, ``min``, ``prod``, ``sum``, ``std`` and ``var``), as well as ``first`` and ``last``. skipna : bool, optional Whether to skip missing values when aggregating in downsampling. closed : 'left' or 'right', optional Side of each interval to treat as closed. label : 'left or 'right', optional Side of each interval to use for labeling. base : int, optionalt For frequencies that evenly subdivide 1 day, the "origin" of the aggregated intervals. For example, for '24H' frequency, base could range from 0 through 23. keep_attrs : bool, optional If True, the object's attributes (`attrs`) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes. Returns ------- resampled : same type as caller This object resampled. References ---------- .. [1] http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases """ from .dataarray import DataArray RESAMPLE_DIM = '__resample_dim__' if isinstance(dim, basestring): dim = self[dim] group = DataArray(dim, [(RESAMPLE_DIM, dim)], name=RESAMPLE_DIM) time_grouper = pd.TimeGrouper(freq=freq, how=how, closed=closed, label=label, base=base) gb = self.groupby_cls(self, group, grouper=time_grouper) if isinstance(how, basestring): f = getattr(gb, how) if how in ['first', 'last']: result = f(skipna=skipna, keep_attrs=keep_attrs) else: result = f(dim=dim.name, skipna=skipna, keep_attrs=keep_attrs) else: result = gb.reduce(how, dim=dim.name, keep_attrs=keep_attrs) result = result.rename({RESAMPLE_DIM: dim.name}) return result def where(self, cond, other=None, drop=False): """Return an object of the same shape with all entries where cond is True and all other entries masked. This operation follows the normal broadcasting and alignment rules that xarray uses for binary arithmetic. Parameters ---------- cond : boolean DataArray or Dataset other : unimplemented, optional Unimplemented placeholder for compatibility with future numpy / pandas versions drop : boolean, optional Coordinate labels that only correspond to NA values should be dropped Returns ------- same type as caller or if drop=True same type as caller with dimensions reduced for dim element where mask is True Examples -------- >>> import numpy as np >>> a = xr.DataArray(np.arange(25).reshape(5, 5), dims=('x', 'y')) >>> a.where((a > 6) & (a < 18)) array([[ nan, nan, nan, nan, nan], [ nan, nan, 7., 8., 9.], [ 10., 11., 12., 13., 14.], [ 15., 16., 17., nan, nan], [ nan, nan, nan, nan, nan]]) Coordinates: * y (y) int64 0 1 2 3 4 * x (x) int64 0 1 2 3 4 >>> a.where((a > 6) & (a < 18), drop=True) array([[ nan, nan, 7., 8., 9.], [ 10., 11., 12., 13., 14.], [ 15., 16., 17., nan, nan], Coordinates: * x (x) int64 1 2 3 * y (y) int64 0 1 2 3 4 """ if other is not None: raise NotImplementedError("The optional argument 'other' has not yet been implemented") if drop: from .dataarray import DataArray from .dataset import Dataset # get cond with the minimal size needed for the Dataset if isinstance(cond, Dataset): clipcond = cond.to_array().any('variable') elif isinstance(cond, DataArray): clipcond = cond else: raise TypeError("Cond argument is %r but must be a %r or %r" % (cond, Dataset, DataArray)) # clip the data corresponding to coordinate dims that are not used clip = dict(zip(clipcond.dims, [np.unique(adim) for adim in np.nonzero(clipcond.values)])) outcond = cond.isel(**clip) indexers = {dim: outcond.get_index(dim) for dim in outcond.dims} outobj = self.sel(**indexers) else: outobj = self outcond = cond # preserve attributes out = outobj._where(outcond) out._copy_attrs_from(self) return out def close(self): """Close any files linked to this object """ if self._file_obj is not None: self._file_obj.close() self._file_obj = None def __enter__(self): return self def __exit__(self, exc_type, exc_value, traceback): self.close() # this has no runtime function - these are listed so IDEs know these methods # are defined and don't warn on these operations __lt__ = __le__ =__ge__ = __gt__ = __add__ = __sub__ = __mul__ = \ __truediv__ = __floordiv__ = __mod__ = __pow__ = __and__ = __xor__ = \ __or__ = __div__ = __eq__ = __ne__ = not_implemented def _maybe_promote(dtype): """Simpler equivalent of pandas.core.common._maybe_promote""" # N.B. these casting rules should match pandas if np.issubdtype(dtype, float): fill_value = np.nan elif np.issubdtype(dtype, int): # convert to floating point so NaN is valid dtype = float fill_value = np.nan elif np.issubdtype(dtype, complex): fill_value = np.nan + np.nan * 1j elif np.issubdtype(dtype, np.datetime64): fill_value = np.datetime64('NaT') elif np.issubdtype(dtype, np.timedelta64): fill_value = np.timedelta64('NaT') else: dtype = object fill_value = np.nan return np.dtype(dtype), fill_value def _possibly_convert_objects(values): """Convert arrays of datetime.datetime and datetime.timedelta objects into datetime64 and timedelta64, according to the pandas convention. """ return np.asarray(pd.Series(values.ravel())).reshape(values.shape) def _get_fill_value(dtype): """Return a fill value that appropriately promotes types when used with np.concatenate """ _, fill_value = _maybe_promote(dtype) return fill_value def full_like(other, fill_value, dtype=None): """Return a new object with the same shape and type as a given object. Parameters ---------- other : DataArray, Dataset, or Variable The reference object in input fill_value : scalar Value to fill the new object with before returning it. dtype : dtype, optional dtype of the new array. If omitted, it defaults to other.dtype. Returns ------- out : same as object New object with the same shape and type as other, with the data filled with fill_value. Coords will be copied from other. If other is based on dask, the new one will be as well, and will be split in the same chunks. """ from .dataarray import DataArray from .dataset import Dataset from .variable import Variable if isinstance(other, Dataset): data_vars = OrderedDict( (k, _full_like_variable(v, fill_value, dtype)) for k, v in other.data_vars.items()) return Dataset(data_vars, coords=other.coords, attrs=other.attrs) elif isinstance(other, DataArray): return DataArray( _full_like_variable(other.variable, fill_value, dtype), dims=other.dims, coords=other.coords, attrs=other.attrs, name=other.name) elif isinstance(other, Variable): return _full_like_variable(other, fill_value, dtype) else: raise TypeError("Expected DataArray, Dataset, or Variable") def _full_like_variable(other, fill_value, dtype=None): """Inner function of full_like, where other must be a variable """ from .variable import Variable if isinstance(other.data, dask_array_type): import dask.array if dtype is None: dtype = other.dtype data = dask.array.full(other.shape, fill_value, dtype=dtype, chunks=other.data.chunks) else: data = np.full_like(other, fill_value, dtype=dtype) return Variable(dims=other.dims, data=data, attrs=other.attrs) def zeros_like(other, dtype=None): """Shorthand for full_like(other, 0, dtype) """ return full_like(other, 0, dtype) def ones_like(other, dtype=None): """Shorthand for full_like(other, 1, dtype) """ return full_like(other, 1, dtype) ���������������������������������������������������xarray-0.9.1/xarray/core/computation.py�������������������������������������������������������������0000644�1311115�0011610�00000070201�13043573225�020305� 0����������������������������������������������������������������������������������������������������ustar �shoyer��������������������������eng�����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������"""Functions for applying functions that act on arrays to xarray's labeled data. NOT PUBLIC API. """ import collections import functools import itertools import operator import re import numpy as np from . import ops from .alignment import deep_align from .merge import expand_and_merge_variables from .pycompat import OrderedDict, basestring, dask_array_type from .utils import is_dict_like _DEFAULT_FROZEN_SET = frozenset() _DEFAULT_FILL_VALUE = object() # see http://docs.scipy.org/doc/numpy/reference/c-api.generalized-ufuncs.html DIMENSION_NAME = r'\w+' CORE_DIMENSION_LIST = '(?:' + DIMENSION_NAME + '(?:,' + DIMENSION_NAME + ')*)?' ARGUMENT = r'\(' + CORE_DIMENSION_LIST + r'\)' ARGUMENT_LIST = ARGUMENT + '(?:,' + ARGUMENT + ')*' SIGNATURE = '^' + ARGUMENT_LIST + '->' + ARGUMENT_LIST + '$' def safe_tuple(x): # type: Iterable -> tuple if isinstance(x, basestring): raise ValueError('cannot safely convert %r to a tuple') return tuple(x) class UFuncSignature(object): """Core dimensions signature for a given function. Based on the signature provided by generalized ufuncs in NumPy. Attributes ---------- input_core_dims : list of tuples A list of tuples of core dimension names on each input variable. output_core_dims : list of tuples A list of tuples of core dimension names on each output variable. """ def __init__(self, input_core_dims, output_core_dims=((),)): self.input_core_dims = tuple(safe_tuple(a) for a in input_core_dims) self.output_core_dims = tuple(safe_tuple(a) for a in output_core_dims) self._all_input_core_dims = None self._all_output_core_dims = None self._all_core_dims = None @property def all_input_core_dims(self): if self._all_input_core_dims is None: self._all_input_core_dims = frozenset( dim for dims in self.input_core_dims for dim in dims) return self._all_input_core_dims @property def all_output_core_dims(self): if self._all_output_core_dims is None: self._all_output_core_dims = frozenset( dim for dims in self.output_core_dims for dim in dims) return self._all_output_core_dims @property def all_core_dims(self): if self._all_core_dims is None: self._all_core_dims = (self.all_input_core_dims | self.all_output_core_dims) return self._all_core_dims @property def n_inputs(self): return len(self.input_core_dims) @property def n_outputs(self): return len(self.output_core_dims) @classmethod def default(cls, n_inputs): return cls([()] * n_inputs, [()]) @classmethod def from_sequence(cls, nested): if (not isinstance(nested, collections.Sequence) or not len(nested) == 2 or any(not isinstance(arg_list, collections.Sequence) for arg_list in nested) or any(isinstance(arg, basestring) or not isinstance(arg, collections.Sequence) for arg_list in nested for arg in arg_list)): raise TypeError('functions signatures not provided as a string ' 'must be a triply nested sequence providing the ' 'list of core dimensions for each variable, for ' 'both input and output.') return cls(*nested) @classmethod def from_string(cls, string): """Create a UFuncSignature object from a NumPy gufunc signature. Parameters ---------- string : str Signature string, e.g., (m,n),(n,p)->(m,p). """ if not re.match(SIGNATURE, string): raise ValueError('not a valid gufunc signature: {}'.format(string)) return cls(*[[re.findall(DIMENSION_NAME, arg) for arg in re.findall(ARGUMENT, arg_list)] for arg_list in string.split('->')]) def __eq__(self, other): try: return (self.input_core_dims == other.input_core_dims and self.output_core_dims == other.output_core_dims) except AttributeError: return False def __ne__(self, other): return not self == other def __repr__(self): return ('%s(%r, %r)' % (type(self).__name__, list(self.input_core_dims), list(self.output_core_dims))) def result_name(objects): # type: List[object] -> Any # use the same naming heuristics as pandas: # https://github.com/blaze/blaze/issues/458#issuecomment-51936356 names = {getattr(obj, 'name', None) for obj in objects} names.discard(None) if len(names) == 1: name, = names else: name = None return name _REPEAT_NONE = itertools.repeat(None) def _get_coord_variables(args): input_coords = [] for arg in args: try: coords = arg.coords except AttributeError: pass # skip this argument else: coord_vars = getattr(coords, 'variables', coords) input_coords.append(coord_vars) return input_coords def build_output_coords( args, # type: list signature, # type: UFuncSignature exclude_dims=frozenset(), # type: set ): # type: (...) -> List[OrderedDict[Any, Variable]] input_coords = _get_coord_variables(args) if exclude_dims: input_coords = [OrderedDict((k, v) for k, v in coord_vars.items() if exclude_dims.isdisjoint(v.dims)) for coord_vars in input_coords] if len(input_coords) == 1: # we can skip the expensive merge unpacked_input_coords, = input_coords merged = OrderedDict(unpacked_input_coords) else: merged = expand_and_merge_variables(input_coords) output_coords = [] for output_dims in signature.output_core_dims: dropped_dims = signature.all_input_core_dims - set(output_dims) if dropped_dims: filtered = OrderedDict((k, v) for k, v in merged.items() if dropped_dims.isdisjoint(v.dims)) else: filtered = merged output_coords.append(filtered) return output_coords def apply_dataarray_ufunc(func, *args, **kwargs): """apply_dataarray_ufunc(func, *args, signature, join='inner', exclude_dims=frozenset()) """ from .dataarray import DataArray signature = kwargs.pop('signature') join = kwargs.pop('join', 'inner') exclude_dims = kwargs.pop('exclude_dims', _DEFAULT_FROZEN_SET) keep_attrs = kwargs.pop('keep_attrs', False) if kwargs: raise TypeError('apply_dataarray_ufunc() got unexpected keyword ' 'arguments: %s' % list(kwargs)) if len(args) > 1: args = deep_align(args, join=join, copy=False, exclude=exclude_dims, raise_on_invalid=False) name = result_name(args) result_coords = build_output_coords(args, signature, exclude_dims) data_vars = [getattr(a, 'variable', a) for a in args] result_var = func(*data_vars) if signature.n_outputs > 1: out = tuple(DataArray(variable, coords, name=name, fastpath=True) for variable, coords in zip(result_var, result_coords)) else: coords, = result_coords out = DataArray(result_var, coords, name=name, fastpath=True) if keep_attrs and isinstance(args[0], DataArray): if isinstance(out, tuple): out = tuple(ds._copy_attrs_from(args[0]) for ds in out) else: out._copy_attrs_from(args[0]) return out def ordered_set_union(all_keys): # type: List[Iterable] -> Iterable result_dict = OrderedDict() for keys in all_keys: for key in keys: result_dict[key] = None return result_dict.keys() def ordered_set_intersection(all_keys): # type: List[Iterable] -> Iterable intersection = set(all_keys[0]) for keys in all_keys[1:]: intersection.intersection_update(keys) return [key for key in all_keys[0] if key in intersection] _JOINERS = { 'inner': ordered_set_intersection, 'outer': ordered_set_union, 'left': operator.itemgetter(0), 'right': operator.itemgetter(-1), } def join_dict_keys(objects, how='inner'): # type: (Iterable[Union[Mapping, Any]], str) -> Iterable joiner = _JOINERS[how] all_keys = [obj.keys() for obj in objects if hasattr(obj, 'keys')] return joiner(all_keys) def collect_dict_values(objects, keys, fill_value=None): # type: (Iterable[Union[Mapping, Any]], Iterable, Any) -> List[list] return [[obj.get(key, fill_value) if is_dict_like(obj) else obj for obj in objects] for key in keys] def _as_variables_or_variable(arg): try: return arg.variables except AttributeError: try: return arg.variable except AttributeError: return arg def _unpack_dict_tuples( result_vars, # type: Mapping[Any, Tuple[Variable]] n_outputs, # type: int ): # type: (...) -> Tuple[Dict[Any, Variable]] out = tuple(OrderedDict() for _ in range(n_outputs)) for name, values in result_vars.items(): for value, results_dict in zip(values, out): results_dict[name] = value return out def apply_dict_of_variables_ufunc(func, *args, **kwargs): """apply_dict_of_variables_ufunc(func, *args, signature, join='inner', fill_value=None): """ signature = kwargs.pop('signature') join = kwargs.pop('join', 'inner') fill_value = kwargs.pop('fill_value', None) if kwargs: raise TypeError('apply_dict_of_variables_ufunc() got unexpected ' 'keyword arguments: %s' % list(kwargs)) args = [_as_variables_or_variable(arg) for arg in args] names = join_dict_keys(args, how=join) grouped_by_name = collect_dict_values(args, names, fill_value) result_vars = OrderedDict() for name, variable_args in zip(names, grouped_by_name): result_vars[name] = func(*variable_args) if signature.n_outputs > 1: return _unpack_dict_tuples(result_vars, signature.n_outputs) else: return result_vars def _fast_dataset(variables, coord_variables): # type: (OrderedDict[Any, Variable], Mapping[Any, Variable]) -> Dataset """Create a dataset as quickly as possible. Beware: the `variables` OrderedDict is modified INPLACE. """ from .dataset import Dataset variables.update(coord_variables) coord_names = set(coord_variables) return Dataset._from_vars_and_coord_names(variables, coord_names) def apply_dataset_ufunc(func, *args, **kwargs): """apply_dataset_ufunc(func, *args, signature, join='inner', dataset_join='inner', fill_value=None, exclude_dims=frozenset(), keep_attrs=False): If dataset_join != 'inner', a non-default fill_value must be supplied by the user. Otherwise a TypeError is raised. """ from .dataset import Dataset signature = kwargs.pop('signature') join = kwargs.pop('join', 'inner') dataset_join = kwargs.pop('dataset_join', 'inner') fill_value = kwargs.pop('fill_value', None) exclude_dims = kwargs.pop('exclude_dims', _DEFAULT_FROZEN_SET) keep_attrs = kwargs.pop('keep_attrs', False) first_obj = args[0] # we'll copy attrs from this in case keep_attrs=True if dataset_join != 'inner' and fill_value is _DEFAULT_FILL_VALUE: raise TypeError('To apply an operation to datasets with different ', 'data variables, you must supply the ', 'dataset_fill_value argument.') if kwargs: raise TypeError('apply_dataset_ufunc() got unexpected keyword ' 'arguments: %s' % list(kwargs)) if len(args) > 1: args = deep_align(args, join=join, copy=False, exclude=exclude_dims, raise_on_invalid=False) list_of_coords = build_output_coords(args, signature, exclude_dims) args = [getattr(arg, 'data_vars', arg) for arg in args] result_vars = apply_dict_of_variables_ufunc( func, *args, signature=signature, join=dataset_join, fill_value=fill_value) if signature.n_outputs > 1: out = tuple(_fast_dataset(*args) for args in zip(result_vars, list_of_coords)) else: coord_vars, = list_of_coords out = _fast_dataset(result_vars, coord_vars) if keep_attrs and isinstance(first_obj, Dataset): if isinstance(out, tuple): out = tuple(ds._copy_attrs_from(first_obj) for ds in out) else: out._copy_attrs_from(first_obj) return out def _iter_over_selections(obj, dim, values): """Iterate over selections of an xarray object in the provided order.""" from .groupby import _dummy_copy dummy = None for value in values: try: obj_sel = obj.sel(**{dim: value}) except (KeyError, IndexError): if dummy is None: dummy = _dummy_copy(obj) obj_sel = dummy yield obj_sel def apply_groupby_ufunc(func, *args): from .groupby import GroupBy, peek_at from .variable import Variable groupbys = [arg for arg in args if isinstance(arg, GroupBy)] if not groupbys: raise ValueError('must have at least one groupby to iterate over') first_groupby = groupbys[0] if any(not first_groupby._group.equals(gb._group) for gb in groupbys[1:]): raise ValueError('can only perform operations over multiple groupbys ' 'at once if they are all grouped the same way') grouped_dim = first_groupby._group.name unique_values = first_groupby._unique_coord.values iterators = [] for arg in args: if isinstance(arg, GroupBy): iterator = (value for _, value in arg) elif hasattr(arg, 'dims') and grouped_dim in arg.dims: if isinstance(arg, Variable): raise ValueError( 'groupby operations cannot be performed with ' 'xarray.Variable objects that share a dimension with ' 'the grouped dimension') iterator = _iter_over_selections(arg, grouped_dim, unique_values) else: iterator = itertools.repeat(arg) iterators.append(iterator) applied = (func(*zipped_args) for zipped_args in zip(*iterators)) applied_example, applied = peek_at(applied) combine = first_groupby._combine if isinstance(applied_example, tuple): combined = tuple(combine(output) for output in zip(*applied)) else: combined = combine(applied) return combined def unified_dim_sizes(variables, exclude_dims=frozenset()): # type: Iterable[Variable] -> OrderedDict[Any, int] dim_sizes = OrderedDict() for var in variables: if len(set(var.dims)) < len(var.dims): raise ValueError('broadcasting cannot handle duplicate ' 'dimensions: %r' % list(var.dims)) for dim, size in zip(var.dims, var.shape): if dim not in exclude_dims: if dim not in dim_sizes: dim_sizes[dim] = size elif dim_sizes[dim] != size: raise ValueError('operands cannot be broadcast together ' 'with mismatched lengths for dimension ' '%r: %s vs %s' % (dim, dim_sizes[dim], size)) return dim_sizes SLICE_NONE = slice(None) # A = TypeVar('A', numpy.ndarray, dask.array.Array) def broadcast_compat_data(variable, broadcast_dims, core_dims): # type: (Variable[A], tuple, tuple) -> A data = variable.data old_dims = variable.dims new_dims = broadcast_dims + core_dims if new_dims == old_dims: # optimize for the typical case return data set_old_dims = set(old_dims) missing_core_dims = [d for d in core_dims if d not in set_old_dims] if missing_core_dims: raise ValueError('operation requires dimensions missing on input ' 'variable: %r' % missing_core_dims) set_new_dims = set(new_dims) unexpected_dims = [d for d in old_dims if d not in set_new_dims] if unexpected_dims: raise ValueError('operation encountered unexpected dimensions %r ' 'on input variable: these are core dimensions on ' 'other input or output variables' % unexpected_dims) # for consistency with numpy, keep broadcast dimensions to the left old_broadcast_dims = tuple(d for d in broadcast_dims if d in set_old_dims) reordered_dims = old_broadcast_dims + core_dims if reordered_dims != old_dims: order = tuple(old_dims.index(d) for d in reordered_dims) data = ops.transpose(data, order) if new_dims != reordered_dims: key = tuple(SLICE_NONE if dim in set_old_dims else None for dim in new_dims) data = data[key] return data def apply_variable_ufunc(func, *args, **kwargs): """apply_variable_ufunc(func, *args, signature, exclude_dims=frozenset()) """ from .variable import Variable signature = kwargs.pop('signature') exclude_dims = kwargs.pop('exclude_dims', _DEFAULT_FROZEN_SET) if kwargs: raise TypeError('apply_variable_ufunc() got unexpected keyword ' 'arguments: %s' % list(kwargs)) dim_sizes = unified_dim_sizes((a for a in args if hasattr(a, 'dims')), exclude_dims=exclude_dims) broadcast_dims = tuple(dim for dim in dim_sizes if dim not in signature.all_core_dims) output_dims = [broadcast_dims + out for out in signature.output_core_dims] input_data = [broadcast_compat_data(arg, broadcast_dims, core_dims) if isinstance(arg, Variable) else arg for arg, core_dims in zip(args, signature.input_core_dims)] result_data = func(*input_data) if signature.n_outputs > 1: output = [] for dims, data in zip(output_dims, result_data): output.append(Variable(dims, data)) return tuple(output) else: dims, = output_dims return Variable(dims, result_data) def apply_array_ufunc(func, *args, **kwargs): """apply_variable_ufunc(func, *args, dask_array='forbidden') """ dask_array = kwargs.pop('dask_array', 'forbidden') if kwargs: raise TypeError('apply_array_ufunc() got unexpected keyword ' 'arguments: %s' % list(kwargs)) if any(isinstance(arg, dask_array_type) for arg in args): # TODO: add a mode dask_array='auto' when dask.array gets a function # for applying arbitrary gufuncs if dask_array == 'forbidden': raise ValueError('encountered dask array, but did not set ' "dask_array='allowed'") elif dask_array != 'allowed': raise ValueError('unknown setting for dask array handling: %r' % dask_array) # fall through return func(*args) def apply_ufunc(func, *args, **kwargs): """apply_ufunc(func, *args, signature=None, join='inner', exclude_dims=frozenset(), dataset_join='inner', dataset_fill_value=_DEFAULT_FILL_VALUE, keep_attrs=False, kwargs=None, dask_array='forbidden') Apply a vectorized function for unlabeled arrays to xarray objects. The input arguments will be handled using xarray's standard rules for labeled computation, including alignment, broadcasting, looping over GroupBy/Dataset variables, and merging of coordinates. Parameters ---------- func : callable Function to call like ``func(*args, **kwargs)`` on unlabeled arrays (``.data``). If multiple arguments with non-matching dimensions are supplied, this function is expected to vectorize (broadcast) over axes of positional arguments in the style of NumPy universal functions [1]_. *args : Dataset, DataArray, GroupBy, Variable, numpy/dask arrays or scalars Mix of labeled and/or unlabeled arrays to which to apply the function. signature : string or triply nested sequence, optional Object indicating core dimensions that should not be broadcast on the input and outputs arguments. If omitted, inputs will be broadcast to share all dimensions in common before calling ``func`` on their values, and the output of ``func`` will be assumed to be a single array with the same shape as the inputs. Two forms of signatures are accepted: (a) A signature string of the form used by NumPy's generalized universal functions [2]_, e.g., '(),(time)->()' indicating a function that accepts two arguments and returns a single argument, on which all dimensions should be broadcast except 'time' on the second argument. (a) A triply nested sequence providing lists of core dimensions for each variable, for both input and output, e.g., ``([(), ('time',)], [()])``. Core dimensions are automatically moved to the last axes of any input variables, which facilitates using NumPy style generalized ufuncs (see the examples below). Unlike the NumPy gufunc signature spec, the names of all dimensions provided in signatures must be the names of actual dimensions on the xarray objects. join : {'outer', 'inner', 'left', 'right'}, optional Method for joining the indexes of the passed objects along each dimension, and the variables of Dataset objects with mismatched data variables: - 'outer': use the union of object indexes - 'inner': use the intersection of object indexes - 'left': use indexes from the first object with each dimension - 'right': use indexes from the last object with each dimension dataset_join : {'outer', 'inner', 'left', 'right'}, optional Method for joining variables of Dataset objects with mismatched data variables. - 'outer': take variables from both Dataset objects - 'inner': take only overlapped variables - 'left': take only variables from the first object - 'right': take only variables from the last object dataset_fill_value : optional Value used in place of missing variables on Dataset inputs when the datasets do not share the exact same ``data_vars``. Required if ``dataset_join != 'inner'``, otherwise ignored. keep_attrs: boolean, Optional Whether to copy attributes from the first argument to the output. exclude_dims : set, optional Dimensions to exclude from alignment and broadcasting. Any inputs coordinates along these dimensions will be dropped. Each excluded dimension must be a core dimension in the function signature. kwargs: dict, optional Optional keyword arguments passed directly on to call ``func``. dask_array: 'forbidden' or 'allowed', optional Whether or not to allow applying the ufunc to objects containing lazy data in the form of dask arrays. By default, this is forbidden, to avoid implicitly converting lazy data. Returns ------- Single value or tuple of Dataset, DataArray, Variable, dask.array.Array or numpy.ndarray, the first type on that list to appear on an input. Examples -------- For illustrative purposes only, here are examples of how you could use ``apply_ufunc`` to write functions to (very nearly) replicate existing xarray functionality: Calculate the vector magnitude of two arguments: def magnitude(a, b): func = lambda x, y: np.sqrt(x ** 2 + y ** 2) return xr.apply_func(func, a, b) Compute the mean (``.mean``):: def mean(obj, dim): # note: apply always moves core dimensions to the end sig = ([(dim,)], [()]) kwargs = {'axis': -1} return apply_ufunc(np.mean, obj, signature=sig, kwargs=kwargs) Inner product over a specific dimension:: def _inner(x, y): result = np.matmul(x[..., np.newaxis, :], y[..., :, np.newaxis]) return result[..., 0, 0] def inner_product(a, b, dim): sig = ([(dim,), (dim,)], [()]) return apply_ufunc(_inner, a, b, signature=sig) Stack objects along a new dimension (like ``xr.concat``):: def stack(objects, dim, new_coord): sig = ([()] * len(objects), [(dim,)]) func = lambda *x: np.stack(x, axis=-1) result = apply_ufunc(func, *objects, signature=sig, join='outer', dataset_fill_value=np.nan) result[dim] = new_coord return result Most of NumPy's builtin functions already broadcast their inputs appropriately for use in `apply`. You may find helper functions such as numpy.broadcast_arrays or numpy.vectorize helpful in writing your function. `apply_ufunc` also works well with numba's vectorize and guvectorize. See also -------- numpy.broadcast_arrays numpy.vectorize numba.vectorize numba.guvectorize References ---------- .. [1] http://docs.scipy.org/doc/numpy/reference/ufuncs.html .. [2] http://docs.scipy.org/doc/numpy/reference/c-api.generalized-ufuncs.html """ from .groupby import GroupBy from .dataarray import DataArray from .variable import Variable signature = kwargs.pop('signature', None) join = kwargs.pop('join', 'inner') dataset_join = kwargs.pop('dataset_join', 'inner') keep_attrs = kwargs.pop('keep_attrs', False) exclude_dims = kwargs.pop('exclude_dims', frozenset()) dataset_fill_value = kwargs.pop('dataset_fill_value', _DEFAULT_FILL_VALUE) kwargs_ = kwargs.pop('kwargs', None) dask_array = kwargs.pop('dask_array', 'forbidden') if kwargs: raise TypeError('apply_ufunc() got unexpected keyword arguments: %s' % list(kwargs)) if signature is None: signature = UFuncSignature.default(len(args)) elif isinstance(signature, basestring): signature = UFuncSignature.from_string(signature) elif not isinstance(signature, UFuncSignature): signature = UFuncSignature.from_sequence(signature) if exclude_dims and not exclude_dims <= signature.all_core_dims: raise ValueError('each dimension in `exclude_dims` must also be a ' 'core dimension in the function signature') if kwargs_: func = functools.partial(func, **kwargs_) array_ufunc = functools.partial( apply_array_ufunc, func, dask_array=dask_array) variables_ufunc = functools.partial( apply_variable_ufunc, array_ufunc, signature=signature, exclude_dims=exclude_dims) if any(isinstance(a, GroupBy) for a in args): this_apply = functools.partial( apply_ufunc, func, signature=signature, join=join, dask_array=dask_array, exclude_dims=exclude_dims, dataset_fill_value=dataset_fill_value, dataset_join=dataset_join, keep_attrs=keep_attrs) return apply_groupby_ufunc(this_apply, *args) elif any(is_dict_like(a) for a in args): return apply_dataset_ufunc(variables_ufunc, *args, signature=signature, join=join, exclude_dims=exclude_dims, fill_value=dataset_fill_value, dataset_join=dataset_join, keep_attrs=keep_attrs) elif any(isinstance(a, DataArray) for a in args): return apply_dataarray_ufunc(variables_ufunc, *args, signature=signature, join=join, exclude_dims=exclude_dims, keep_attrs=keep_attrs) elif any(isinstance(a, Variable) for a in args): return variables_ufunc(*args) else: return array_ufunc(*args) �����������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������xarray-0.9.1/xarray/core/coordinates.py�������������������������������������������������������������0000644�1311115�0011610�00000020501�13043511243�020243� 0����������������������������������������������������������������������������������������������������ustar �shoyer��������������������������eng�����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������from __future__ import absolute_import from __future__ import division from __future__ import print_function from collections import Mapping from contextlib import contextmanager import pandas as pd from . import formatting from .utils import Frozen from .merge import ( merge_coords, expand_and_merge_variables, merge_coords_for_inplace_math) from .pycompat import OrderedDict class AbstractCoordinates(Mapping, formatting.ReprMixin): def __getitem__(self, key): raise NotImplementedError def __setitem__(self, key, value): self.update({key: value}) @property def indexes(self): return self._data.indexes @property def variables(self): raise NotImplementedError def _update_coords(self, coords): raise NotImplementedError def __iter__(self): # needs to be in the same order as the dataset variables for k in self.variables: if k in self._names: yield k def __len__(self): return len(self._names) def __contains__(self, key): return key in self._names def __unicode__(self): return formatting.coords_repr(self) @property def dims(self): return self._data.dims def to_index(self, ordered_dims=None): """Convert all index coordinates into a :py:class:`pandas.MultiIndex` """ if ordered_dims is None: ordered_dims = self.dims indexes = [self._data.get_index(k) for k in ordered_dims] return pd.MultiIndex.from_product(indexes, names=list(ordered_dims)) def update(self, other): other_vars = getattr(other, 'variables', other) coords = merge_coords([self.variables, other_vars], priority_arg=1, indexes=self.indexes) self._update_coords(coords) def _merge_raw(self, other): """For use with binary arithmetic.""" if other is None: variables = OrderedDict(self.variables) else: # don't align because we already called xarray.align variables = expand_and_merge_variables( [self.variables, other.variables]) return variables @contextmanager def _merge_inplace(self, other): """For use with in-place binary arithmetic.""" if other is None: yield else: # don't include indexes in priority_vars, because we didn't align # first priority_vars = OrderedDict( (k, v) for k, v in self.variables.items() if k not in self.dims) variables = merge_coords_for_inplace_math( [self.variables, other.variables], priority_vars=priority_vars) yield self._update_coords(variables) def merge(self, other): """Merge two sets of coordinates to create a new Dataset The method implements the logic used for joining coordinates in the result of a binary operation performed on xarray objects: - If two index coordinates conflict (are not equal), an exception is raised. You must align your data before passing it to this method. - If an index coordinate and a non-index coordinate conflict, the non- index coordinate is dropped. - If two non-index coordinates conflict, both are dropped. Parameters ---------- other : DatasetCoordinates or DataArrayCoordinates The coordinates from another dataset or data array. Returns ------- merged : Dataset A new Dataset with merged coordinates. """ from .dataset import Dataset if other is None: return self.to_dataset() else: other_vars = getattr(other, 'variables', other) coords = expand_and_merge_variables([self.variables, other_vars]) return Dataset._from_vars_and_coord_names(coords, set(coords)) class DatasetCoordinates(AbstractCoordinates): """Dictionary like container for Dataset coordinates. Essentially an immutable OrderedDict with keys given by the array's dimensions and the values given by the corresponding xarray.Coordinate objects. """ def __init__(self, dataset): self._data = dataset @property def _names(self): return self._data._coord_names @property def variables(self): return Frozen(OrderedDict((k, v) for k, v in self._data.variables.items() if k in self._names)) def __getitem__(self, key): if key in self._data.data_vars: raise KeyError(key) return self._data[key] def to_dataset(self): """Convert these coordinates into a new Dataset """ return self._data._copy_listed(self._names) def _update_coords(self, coords): from .dataset import calculate_dimensions variables = self._data._variables.copy() variables.update(coords) # check for inconsistent state *before* modifying anything in-place dims = calculate_dimensions(variables) new_coord_names = set(coords) for dim, size in dims.items(): if dim in variables: new_coord_names.add(dim) self._data._variables = variables self._data._coord_names.update(new_coord_names) self._data._dims = dict(dims) def __delitem__(self, key): if key in self: del self._data[key] else: raise KeyError(key) class DataArrayCoordinates(AbstractCoordinates): """Dictionary like container for DataArray coordinates. Essentially an OrderedDict with keys given by the array's dimensions and the values given by corresponding DataArray objects. """ def __init__(self, dataarray): self._data = dataarray @property def _names(self): return set(self._data._coords) def __getitem__(self, key): return self._data._getitem_coord(key) def _update_coords(self, coords): from .dataset import calculate_dimensions dims = calculate_dimensions(coords) if not set(dims) <= set(self.dims): raise ValueError('cannot add coordinates with new dimensions to ' 'a DataArray') self._data._coords = coords @property def variables(self): return Frozen(self._data._coords) def _to_dataset(self, shallow_copy=True): from .dataset import Dataset coords = OrderedDict((k, v.copy(deep=False) if shallow_copy else v) for k, v in self._data._coords.items()) return Dataset._from_vars_and_coord_names(coords, set(coords)) def to_dataset(self): return self._to_dataset() def __delitem__(self, key): del self._data._coords[key] class LevelCoordinatesSource(object): """Iterator for MultiIndex level coordinates. Used for attribute style lookup with AttrAccessMixin. Not returned directly by any public methods. """ def __init__(self, data_object): self._data = data_object def __getitem__(self, key): # not necessary -- everything here can already be found in coords. raise KeyError def __iter__(self): return iter(self._data._level_coords) class Indexes(Mapping, formatting.ReprMixin): """Ordered Mapping[str, pandas.Index] for xarray objects. """ def __init__(self, variables, sizes): """Not for public consumption. Arguments --------- variables : OrderedDict[Any, Variable] Reference to OrderedDict holding variable objects. Should be the same dictionary used by the source object. sizes : OrderedDict[Any, int] Map from dimension names to sizes. """ self._variables = variables self._sizes = sizes def __iter__(self): for key in self._sizes: if key in self._variables: yield key def __len__(self): return sum(key in self._variables for key in self._sizes) def __contains__(self, key): return key in self._sizes and key in self._variables def __getitem__(self, key): if key not in self._sizes: raise KeyError(key) return self._variables[key].to_index() def __unicode__(self): return formatting.indexes_repr(self) �����������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������xarray-0.9.1/xarray/core/dataarray.py���������������������������������������������������������������0000644�1311115�0011610�00000177216�13043543222�017723� 0����������������������������������������������������������������������������������������������������ustar �shoyer��������������������������eng�����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������from __future__ import absolute_import from __future__ import division from __future__ import print_function import functools import warnings import numpy as np import pandas as pd from ..plot.plot import _PlotMethods from . import indexing from . import groupby from . import rolling from . import ops from . import utils from .alignment import align, reindex_like_indexers from .common import AbstractArray, BaseDataObject from .coordinates import (DataArrayCoordinates, LevelCoordinatesSource, Indexes) from .dataset import Dataset, merge_indexes, split_indexes from .pycompat import iteritems, basestring, OrderedDict, zip, range from .variable import (as_variable, Variable, as_compatible_data, IndexVariable, assert_unique_multiindex_level_names) from .formatting import format_item from .utils import decode_numpy_dict_values, ensure_us_time_resolution from .options import OPTIONS def _infer_coords_and_dims(shape, coords, dims): """All the logic for creating a new DataArray""" if (coords is not None and not utils.is_dict_like(coords) and len(coords) != len(shape)): raise ValueError('coords is not dict-like, but it has %s items, ' 'which does not match the %s dimensions of the ' 'data' % (len(coords), len(shape))) if isinstance(dims, basestring): dims = (dims,) if dims is None: dims = ['dim_%s' % n for n in range(len(shape))] if coords is not None and len(coords) == len(shape): # try to infer dimensions from coords if utils.is_dict_like(coords): warnings.warn('inferring DataArray dimensions from dictionary ' 'like ``coords`` has been deprecated. Use an ' 'explicit list of ``dims`` instead.', FutureWarning, stacklevel=3) dims = list(coords.keys()) else: for n, (dim, coord) in enumerate(zip(dims, coords)): coord = as_variable(coord, name=dims[n]).to_index_variable() dims[n] = coord.name dims = tuple(dims) else: for d in dims: if not isinstance(d, basestring): raise TypeError('dimension %s is not a string' % d) new_coords = OrderedDict() if utils.is_dict_like(coords): for k, v in coords.items(): new_coords[k] = as_variable(v, name=k) elif coords is not None: for dim, coord in zip(dims, coords): var = as_variable(coord, name=dim) var.dims = (dim,) new_coords[dim] = var sizes = dict(zip(dims, shape)) for k, v in new_coords.items(): if any(d not in dims for d in v.dims): raise ValueError('coordinate %s has dimensions %s, but these ' 'are not a subset of the DataArray ' 'dimensions %s' % (k, v.dims, dims)) for d, s in zip(v.dims, v.shape): if s != sizes[d]: raise ValueError('conflicting sizes for dimension %r: ' 'length %s on the data but length %s on ' 'coordinate %r' % (d, sizes[d], s, k)) assert_unique_multiindex_level_names(new_coords) return new_coords, dims class _LocIndexer(object): def __init__(self, data_array): self.data_array = data_array def _remap_key(self, key): if not utils.is_dict_like(key): # expand the indexer so we can handle Ellipsis labels = indexing.expanded_indexer(key, self.data_array.ndim) key = dict(zip(self.data_array.dims, labels)) return indexing.remap_label_indexers(self.data_array, key) def __getitem__(self, key): pos_indexers, new_indexes = self._remap_key(key) return self.data_array[pos_indexers]._replace_indexes(new_indexes) def __setitem__(self, key, value): pos_indexers, _ = self._remap_key(key) self.data_array[pos_indexers] = value class _ThisArray(object): """An instance of this object is used as the key corresponding to the variable when converting arbitrary DataArray objects to datasets """ def __repr__(self): return '' class DataArray(AbstractArray, BaseDataObject): """N-dimensional array with labeled coordinates and dimensions. DataArray provides a wrapper around numpy ndarrays that uses labeled dimensions and coordinates to support metadata aware operations. The API is similar to that for the pandas Series or DataFrame, but DataArray objects can have any number of dimensions, and their contents have fixed data types. Additional features over raw numpy arrays: - Apply operations over dimensions by name: ``x.sum('time')``. - Select or assign values by integer location (like numpy): ``x[:10]`` or by label (like pandas): ``x.loc['2014-01-01']`` or ``x.sel(time='2014-01-01')``. - Mathematical operations (e.g., ``x - y``) vectorize across multiple dimensions (known in numpy as "broadcasting") based on dimension names, regardless of their original order. - Keep track of arbitrary metadata in the form of a Python dictionary: ``x.attrs`` - Convert to a pandas Series: ``x.to_series()``. Getting items from or doing mathematical operations with a DataArray always returns another DataArray. Attributes ---------- dims : tuple Dimension names associated with this array. values : np.ndarray Access or modify DataArray values as a numpy array. coords : dict-like Dictionary of DataArray objects that label values along each dimension. name : str or None Name of this array. attrs : OrderedDict Dictionary for holding arbitrary metadata. """ groupby_cls = groupby.DataArrayGroupBy rolling_cls = rolling.DataArrayRolling def __init__(self, data, coords=None, dims=None, name=None, attrs=None, encoding=None, fastpath=False): """ Parameters ---------- data : array_like Values for this array. Must be an ``numpy.ndarray``, ndarray like, or castable to an ``ndarray``. If a self-described xarray or pandas object, attempts are made to use this array's metadata to fill in other unspecified arguments. A view of the array's data is used instead of a copy if possible. coords : sequence or dict of array_like objects, optional Coordinates (tick labels) to use for indexing along each dimension. If dict-like, should be a mapping from dimension names to the corresponding coordinates. dims : str or sequence of str, optional Name(s) of the the data dimension(s). Must be either a string (only for 1D data) or a sequence of strings with length equal to the number of dimensions. If this argument is omitted, dimension names are taken from ``coords`` (if possible) and otherwise default to ``['dim_0', ... 'dim_n']``. name : str or None, optional Name of this array. attrs : dict_like or None, optional Attributes to assign to the new instance. By default, an empty attribute dictionary is initialized. encoding : dict_like or None, optional Dictionary specifying how to encode this array's data into a serialized format like netCDF4. Currently used keys (for netCDF) include '_FillValue', 'scale_factor', 'add_offset', 'dtype', 'units' and 'calendar' (the later two only for datetime arrays). Unrecognized keys are ignored. """ if fastpath: variable = data assert dims is None assert attrs is None assert encoding is None else: # try to fill in arguments from data if they weren't supplied if coords is None: coords = getattr(data, 'coords', None) if isinstance(data, pd.Series): coords = [data.index] elif isinstance(data, pd.DataFrame): coords = [data.index, data.columns] elif isinstance(data, (pd.Index, IndexVariable)): coords = [data] elif isinstance(data, pd.Panel): coords = [data.items, data.major_axis, data.minor_axis] if dims is None: dims = getattr(data, 'dims', getattr(coords, 'dims', None)) if name is None: name = getattr(data, 'name', None) if attrs is None: attrs = getattr(data, 'attrs', None) if encoding is None: encoding = getattr(data, 'encoding', None) data = as_compatible_data(data) coords, dims = _infer_coords_and_dims(data.shape, coords, dims) variable = Variable(dims, data, attrs, encoding, fastpath=True) # uncomment for a useful consistency check: # assert all(isinstance(v, Variable) for v in coords.values()) # These fully describe a DataArray self._variable = variable self._coords = coords self._name = name self._file_obj = None self._initialized = True __default = object() def _replace(self, variable=None, coords=None, name=__default): if variable is None: variable = self.variable if coords is None: coords = self._coords if name is self.__default: name = self.name return type(self)(variable, coords, name=name, fastpath=True) def _replace_maybe_drop_dims(self, variable, name=__default): if variable.dims == self.dims: coords = None else: allowed_dims = set(variable.dims) coords = OrderedDict((k, v) for k, v in self._coords.items() if set(v.dims) <= allowed_dims) return self._replace(variable, coords, name) def _replace_indexes(self, indexes): if not len(indexes): return self coords = self._coords.copy() for name, idx in indexes.items(): coords[name] = IndexVariable(name, idx) obj = self._replace(coords=coords) # switch from dimension to level names, if necessary dim_names = {} for dim, idx in indexes.items(): if not isinstance(idx, pd.MultiIndex) and idx.name != dim: dim_names[dim] = idx.name if dim_names: obj = obj.rename(dim_names) return obj __this_array = _ThisArray() def _to_temp_dataset(self): return self._to_dataset_whole(name=self.__this_array, shallow_copy=False) def _from_temp_dataset(self, dataset, name=__default): variable = dataset._variables.pop(self.__this_array) coords = dataset._variables return self._replace(variable, coords, name) def _to_dataset_split(self, dim): def subset(dim, label): array = self.loc[{dim: label}] if dim in array.coords: del array.coords[dim] array.attrs = {} return array variables = OrderedDict([(label, subset(dim, label)) for label in self.get_index(dim)]) coords = self.coords.to_dataset() if dim in coords: del coords[dim] return Dataset(variables, coords, self.attrs) def _to_dataset_whole(self, name=None, shallow_copy=True): if name is None: name = self.name if name is None: raise ValueError('unable to convert unnamed DataArray to a ' 'Dataset without providing an explicit name') if name in self.coords: raise ValueError('cannot create a Dataset from a DataArray with ' 'the same name as one of its coordinates') # use private APIs here for speed: this is called by _to_temp_dataset(), # which is used in the guts of a lot of operations (e.g., reindex) variables = self._coords.copy() variables[name] = self.variable if shallow_copy: for k in variables: variables[k] = variables[k].copy(deep=False) coord_names = set(self._coords) dataset = Dataset._from_vars_and_coord_names(variables, coord_names) return dataset def to_dataset(self, dim=None, name=None): """Convert a DataArray to a Dataset. Parameters ---------- dim : str, optional Name of the dimension on this array along which to split this array into separate variables. If not provided, this array is converted into a Dataset of one variable. name : str, optional Name to substitute for this array's name. Only valid if ``dim`` is not provided. Returns ------- dataset : Dataset """ if dim is not None and dim not in self.dims: warnings.warn('the order of the arguments on DataArray.to_dataset ' 'has changed; you now need to supply ``name`` as ' 'a keyword argument', FutureWarning, stacklevel=2) name = dim dim = None if dim is not None: if name is not None: raise TypeError('cannot supply both dim and name arguments') return self._to_dataset_split(dim) else: return self._to_dataset_whole(name) @property def name(self): """The name of this array. """ return self._name @name.setter def name(self, value): self._name = value @property def variable(self): return self._variable @property def dtype(self): return self.variable.dtype @property def shape(self): return self.variable.shape @property def size(self): return self.variable.size @property def nbytes(self): return self.variable.nbytes @property def ndim(self): return self.variable.ndim def __len__(self): return len(self.variable) @property def data(self): """The array's data as a dask or numpy array""" return self.variable.data @data.setter def data(self, value): self.variable.data = value @property def values(self): """The array's data as a numpy.ndarray""" return self.variable.values @values.setter def values(self, value): self.variable.values = value @property def _in_memory(self): return self.variable._in_memory def to_index(self): """Convert this variable to a pandas.Index. Only possible for 1D arrays. """ return self.variable.to_index() @property def dims(self): """Tuple of dimension names associated with this array. Note that the type of this property is inconsistent with `Dataset.dims`. See `Dataset.sizes` and `DataArray.sizes` for consistently named properties. """ return self.variable.dims @dims.setter def dims(self, value): raise AttributeError('you cannot assign dims on a DataArray. Use ' '.rename() or .swap_dims() instead.') def _item_key_to_dict(self, key): if utils.is_dict_like(key): return key else: key = indexing.expanded_indexer(key, self.ndim) return dict(zip(self.dims, key)) @property def _level_coords(self): """Return a mapping of all MultiIndex levels and their corresponding coordinate name. """ level_coords = OrderedDict() for cname, var in self._coords.items(): if var.ndim == 1: level_names = var.to_index_variable().level_names if level_names is not None: dim, = var.dims level_coords.update({lname: dim for lname in level_names}) return level_coords def _getitem_coord(self, key): from .dataset import _get_virtual_variable try: var = self._coords[key] except KeyError: dim_sizes = dict(zip(self.dims, self.shape)) _, key, var = _get_virtual_variable( self._coords, key, self._level_coords, dim_sizes) return self._replace_maybe_drop_dims(var, name=key) def __getitem__(self, key): if isinstance(key, basestring): return self._getitem_coord(key) else: # orthogonal array indexing return self.isel(**self._item_key_to_dict(key)) def __setitem__(self, key, value): if isinstance(key, basestring): self.coords[key] = value else: # orthogonal array indexing self.variable[key] = value def __delitem__(self, key): del self.coords[key] @property def _attr_sources(self): """List of places to look-up items for attribute-style access""" return [self.coords, LevelCoordinatesSource(self), self.attrs] def __contains__(self, key): return key in self._coords @property def loc(self): """Attribute for location based indexing like pandas. """ return _LocIndexer(self) @property def attrs(self): """Dictionary storing arbitrary metadata with this array.""" return self.variable.attrs @attrs.setter def attrs(self, value): self.variable.attrs = value @property def encoding(self): """Dictionary of format-specific settings for how this array should be serialized.""" return self.variable.encoding @encoding.setter def encoding(self, value): self.variable.encoding = value @property def indexes(self): """OrderedDict of pandas.Index objects used for label based indexing """ return Indexes(self._coords, self.sizes) @property def coords(self): """Dictionary-like container of coordinate arrays. """ return DataArrayCoordinates(self) def reset_coords(self, names=None, drop=False, inplace=False): """Given names of coordinates, reset them to become variables. Parameters ---------- names : str or list of str, optional Name(s) of non-index coordinates in this dataset to reset into variables. By default, all non-index coordinates are reset. drop : bool, optional If True, remove coordinates instead of converting them into variables. inplace : bool, optional If True, modify this dataset inplace. Otherwise, create a new object. Returns ------- Dataset, or DataArray if ``drop == True`` """ if inplace and not drop: raise ValueError('cannot reset coordinates in-place on a ' 'DataArray without ``drop == True``') if names is None: names = set(self.coords) - set(self.dims) dataset = self.coords.to_dataset().reset_coords(names, drop) if drop: if inplace: self._coords = dataset._variables else: return self._replace(coords=dataset._variables) else: if self.name is None: raise ValueError('cannot reset_coords with drop=False ' 'on an unnamed DataArrray') dataset[self.name] = self.variable return dataset def load(self): """Manually trigger loading of this array's data from disk or a remote source into memory and return this array. Normally, it should not be necessary to call this method in user code, because all xarray functions should either work on deferred data or load data automatically. However, this method can be necessary when working with many file objects on disk. """ ds = self._to_temp_dataset().load() new = self._from_temp_dataset(ds) self._variable = new._variable self._coords = new._coords return self def compute(self): """Manually trigger loading of this array's data from disk or a remote source into memory and return a new array. The original is left unaltered. Normally, it should not be necessary to call this method in user code, because all xarray functions should either work on deferred data or load data automatically. However, this method can be necessary when working with many file objects on disk. """ new = self.copy(deep=False) return new.load() def copy(self, deep=True): """Returns a copy of this array. If `deep=True`, a deep copy is made of all variables in the underlying dataset. Otherwise, a shallow copy is made, so each variable in the new array's dataset is also a variable in this array's dataset. """ variable = self.variable.copy(deep=deep) coords = OrderedDict((k, v.copy(deep=deep)) for k, v in self._coords.items()) return self._replace(variable, coords) def __copy__(self): return self.copy(deep=False) def __deepcopy__(self, memo=None): # memo does nothing but is required for compatibility with # copy.deepcopy return self.copy(deep=True) # mutable objects should not be hashable __hash__ = None @property def chunks(self): """Block dimensions for this array's data or None if it's not a dask array. """ return self.variable.chunks def chunk(self, chunks=None): """Coerce this array's data into a dask arrays with the given chunks. If this variable is a non-dask array, it will be converted to dask array. If it's a dask array, it will be rechunked to the given chunk sizes. If neither chunks is not provided for one or more dimensions, chunk sizes along that dimension will not be updated; non-dask arrays will be converted into dask arrays with a single block. Parameters ---------- chunks : int, tuple or dict, optional Chunk sizes along each dimension, e.g., ``5``, ``(5, 5)`` or ``{'x': 5, 'y': 5}``. Returns ------- chunked : xarray.DataArray """ if isinstance(chunks, (list, tuple)): chunks = dict(zip(self.dims, chunks)) ds = self._to_temp_dataset().chunk(chunks) return self._from_temp_dataset(ds) def isel(self, drop=False, **indexers): """Return a new DataArray whose dataset is given by integer indexing along the specified dimension(s). See Also -------- Dataset.isel DataArray.sel """ ds = self._to_temp_dataset().isel(drop=drop, **indexers) return self._from_temp_dataset(ds) def sel(self, method=None, tolerance=None, drop=False, **indexers): """Return a new DataArray whose dataset is given by selecting index labels along the specified dimension(s). See Also -------- Dataset.sel DataArray.isel """ pos_indexers, new_indexes = indexing.remap_label_indexers( self, indexers, method=method, tolerance=tolerance ) result = self.isel(drop=drop, **pos_indexers) return result._replace_indexes(new_indexes) def isel_points(self, dim='points', **indexers): """Return a new DataArray whose dataset is given by pointwise integer indexing along the specified dimension(s). See Also -------- Dataset.isel_points """ ds = self._to_temp_dataset().isel_points(dim=dim, **indexers) return self._from_temp_dataset(ds) def sel_points(self, dim='points', method=None, tolerance=None, **indexers): """Return a new DataArray whose dataset is given by pointwise selection of index labels along the specified dimension(s). See Also -------- Dataset.sel_points """ ds = self._to_temp_dataset().sel_points( dim=dim, method=method, tolerance=tolerance, **indexers) return self._from_temp_dataset(ds) def reindex_like(self, other, method=None, tolerance=None, copy=True): """Conform this object onto the indexes of another object, filling in missing values with NaN. Parameters ---------- other : Dataset or DataArray Object with an 'indexes' attribute giving a mapping from dimension names to pandas.Index objects, which provides coordinates upon which to index the variables in this dataset. The indexes on this other object need not be the same as the indexes on this dataset. Any mis-matched index values will be filled in with NaN, and any mis-matched dimension names will simply be ignored. method : {None, 'nearest', 'pad'/'ffill', 'backfill'/'bfill'}, optional Method to use for filling index values from other not found on this data array: * None (default): don't fill gaps * pad / ffill: propagate last valid index value forward * backfill / bfill: propagate next valid index value backward * nearest: use nearest valid index value (requires pandas>=0.16) tolerance : optional Maximum distance between original and new labels for inexact matches. The values of the index at the matching locations most satisfy the equation ``abs(index[indexer] - target) <= tolerance``. Requires pandas>=0.17. copy : bool, optional If ``copy=True``, data in the return value is always copied. If ``copy=False`` and reindexing is unnecessary, or can be performed with only slice operations, then the output may share memory with the input. In either case, a new xarray object is always returned. Returns ------- reindexed : DataArray Another dataset array, with this array's data but coordinates from the other object. See Also -------- DataArray.reindex align """ indexers = reindex_like_indexers(self, other) return self.reindex(method=method, tolerance=tolerance, copy=copy, **indexers) def reindex(self, method=None, tolerance=None, copy=True, **indexers): """Conform this object onto a new set of indexes, filling in missing values with NaN. Parameters ---------- copy : bool, optional If ``copy=True``, data in the return value is always copied. If ``copy=False`` and reindexing is unnecessary, or can be performed with only slice operations, then the output may share memory with the input. In either case, a new xarray object is always returned. method : {None, 'nearest', 'pad'/'ffill', 'backfill'/'bfill'}, optional Method to use for filling index values in ``indexers`` not found on this data array: * None (default): don't fill gaps * pad / ffill: propagate last valid index value forward * backfill / bfill: propagate next valid index value backward * nearest: use nearest valid index value (requires pandas>=0.16) tolerance : optional Maximum distance between original and new labels for inexact matches. The values of the index at the matching locations most satisfy the equation ``abs(index[indexer] - target) <= tolerance``. Requires pandas>=0.17. **indexers : dict Dictionary with keys given by dimension names and values given by arrays of coordinates tick labels. Any mis-matched coordinate values will be filled in with NaN, and any mis-matched dimension names will simply be ignored. Returns ------- reindexed : DataArray Another dataset array, with this array's data but replaced coordinates. See Also -------- DataArray.reindex_like align """ ds = self._to_temp_dataset().reindex( method=method, tolerance=tolerance, copy=copy, **indexers) return self._from_temp_dataset(ds) def rename(self, new_name_or_name_dict): """Returns a new DataArray with renamed coordinates or a new name. Parameters ---------- new_name_or_name_dict : str or dict-like If the argument is dict-like, it it used as a mapping from old names to new names for coordinates. Otherwise, use the argument as the new name for this array. Returns ------- renamed : DataArray Renamed array or array with renamed coordinates. See Also -------- Dataset.rename DataArray.swap_dims """ if utils.is_dict_like(new_name_or_name_dict): dataset = self._to_temp_dataset().rename(new_name_or_name_dict) return self._from_temp_dataset(dataset) else: return self._replace(name=new_name_or_name_dict) def swap_dims(self, dims_dict): """Returns a new DataArray with swapped dimensions. Parameters ---------- dims_dict : dict-like Dictionary whose keys are current dimension names and whose values are new names. Each value must already be a coordinate on this array. Returns ------- renamed : Dataset DataArray with swapped dimensions. See Also -------- DataArray.rename Dataset.swap_dims """ ds = self._to_temp_dataset().swap_dims(dims_dict) return self._from_temp_dataset(ds) def set_index(self, append=False, inplace=False, **indexes): """Set DataArray (multi-)indexes using one or more existing coordinates. Parameters ---------- append : bool, optional If True, append the supplied index(es) to the existing index(es). Otherwise replace the existing index(es) (default). inplace : bool, optional If True, set new index(es) in-place. Otherwise, return a new DataArray object. **indexes : {dim: index, ...} Keyword arguments with names matching dimensions and values given by (lists of) the names of existing coordinates or variables to set as new (multi-)index. Returns ------- obj : DataArray Another dataarray, with this dataarray's data but replaced coordinates. See Also -------- DataArray.reset_index """ coords, _ = merge_indexes(indexes, self._coords, set(), append=append) if inplace: self._coords = coords else: return self._replace(coords=coords) def reset_index(self, dims_or_levels, drop=False, inplace=False): """Reset the specified index(es) or multi-index level(s). Parameters ---------- dims_or_levels : str or list Name(s) of the dimension(s) and/or multi-index level(s) that will be reset. drop : bool, optional If True, remove the specified indexes and/or multi-index levels instead of extracting them as new coordinates (default: False). inplace : bool, optional If True, modify the dataarray in-place. Otherwise, return a new DataArray object. Returns ------- obj : DataArray Another dataarray, with this dataarray's data but replaced coordinates. See Also -------- DataArray.set_index """ coords, _ = split_indexes(dims_or_levels, self._coords, set(), self._level_coords, drop=drop) if inplace: self._coords = coords else: return self._replace(coords=coords) def reorder_levels(self, inplace=False, **dim_order): """Rearrange index levels using input order. Parameters ---------- inplace : bool, optional If True, modify the dataarray in-place. Otherwise, return a new DataArray object. **dim_order : optional Keyword arguments with names matching dimensions and values given by lists representing new level orders. Every given dimension must have a multi-index. Returns ------- obj : DataArray Another dataarray, with this dataarray's data but replaced coordinates. """ replace_coords = {} for dim, order in dim_order.items(): coord = self._coords[dim] index = coord.to_index() if not isinstance(index, pd.MultiIndex): raise ValueError("coordinate %r has no MultiIndex" % dim) replace_coords[dim] = IndexVariable(coord.dims, index.reorder_levels(order)) coords = self._coords.copy() coords.update(replace_coords) if inplace: self._coords = coords else: return self._replace(coords=coords) def stack(self, **dimensions): """ Stack any number of existing dimensions into a single new dimension. New dimensions will be added at the end, and the corresponding coordinate variables will be combined into a MultiIndex. Parameters ---------- **dimensions : keyword arguments of the form new_name=(dim1, dim2, ...) Names of new dimensions, and the existing dimensions that they replace. Returns ------- stacked : DataArray DataArray with stacked data. Examples -------- >>> arr = DataArray(np.arange(6).reshape(2, 3), ... coords=[('x', ['a', 'b']), ('y', [0, 1, 2])]) >>> arr array([[0, 1, 2], [3, 4, 5]]) Coordinates: * x (x) |S1 'a' 'b' * y (y) int64 0 1 2 >>> stacked = arr.stack(z=('x', 'y')) >>> stacked.indexes['z'] MultiIndex(levels=[[u'a', u'b'], [0, 1, 2]], labels=[[0, 0, 0, 1, 1, 1], [0, 1, 2, 0, 1, 2]], names=[u'x', u'y']) See also -------- DataArray.unstack """ ds = self._to_temp_dataset().stack(**dimensions) return self._from_temp_dataset(ds) def unstack(self, dim): """ Unstack an existing dimension corresponding to a MultiIndex into multiple new dimensions. New dimensions will be added at the end. Parameters ---------- dim : str Name of the existing dimension to unstack. Returns ------- unstacked : DataArray Array with unstacked data. See also -------- DataArray.stack """ ds = self._to_temp_dataset().unstack(dim) return self._from_temp_dataset(ds) def transpose(self, *dims): """Return a new DataArray object with transposed dimensions. Parameters ---------- *dims : str, optional By default, reverse the dimensions. Otherwise, reorder the dimensions to this order. Returns ------- transposed : DataArray The returned DataArray's array is transposed. Notes ----- Although this operation returns a view of this array's data, it is not lazy -- the data will be fully loaded. See Also -------- numpy.transpose Dataset.transpose """ variable = self.variable.transpose(*dims) return self._replace(variable) def drop(self, labels, dim=None): """Drop coordinates or index labels from this DataArray. Parameters ---------- labels : scalar or list of scalars Name(s) of coordinate variables or index labels to drop. dim : str, optional Dimension along which to drop index labels. By default (if ``dim is None``), drops coordinates rather than index labels. Returns ------- dropped : DataArray """ if utils.is_scalar(labels): labels = [labels] ds = self._to_temp_dataset().drop(labels, dim) return self._from_temp_dataset(ds) def dropna(self, dim, how='any', thresh=None): """Returns a new array with dropped labels for missing values along the provided dimension. Parameters ---------- dim : str Dimension along which to drop missing values. Dropping along multiple dimensions simultaneously is not yet supported. how : {'any', 'all'}, optional * any : if any NA values are present, drop that label * all : if all values are NA, drop that label thresh : int, default None If supplied, require this many non-NA values. Returns ------- DataArray """ ds = self._to_temp_dataset().dropna(dim, how=how, thresh=thresh) return self._from_temp_dataset(ds) def fillna(self, value): """Fill missing values in this object. This operation follows the normal broadcasting and alignment rules that xarray uses for binary arithmetic, except the result is aligned to this object (``join='left'``) instead of aligned to the intersection of index coordinates (``join='inner'``). Parameters ---------- value : scalar, ndarray or DataArray Used to fill all matching missing values in this array. If the argument is a DataArray, it is first aligned with (reindexed to) this array. Returns ------- DataArray """ if utils.is_dict_like(value): raise TypeError('cannot provide fill value as a dictionary with ' 'fillna on a DataArray') out = ops.fillna(self, value) return out def combine_first(self, other): """Combine two DataArray objects, with union of coordinates. This operation follows the normal broadcasting and alignment rules of ``join='outer'``. Default to non-null values of array calling the method. Use np.nan to fill in vacant cells after alignment. Parameters ---------- other : DataArray Used to fill all matching missing values in this array. Returns ------- DataArray """ return ops.fillna(self, other, join="outer") def reduce(self, func, dim=None, axis=None, keep_attrs=False, **kwargs): """Reduce this array by applying `func` along some dimension(s). Parameters ---------- func : function Function which can be called in the form `f(x, axis=axis, **kwargs)` to return the result of reducing an np.ndarray over an integer valued axis. dim : str or sequence of str, optional Dimension(s) over which to apply `func`. axis : int or sequence of int, optional Axis(es) over which to repeatedly apply `func`. Only one of the 'dim' and 'axis' arguments can be supplied. If neither are supplied, then the reduction is calculated over the flattened array (by calling `f(x)` without an axis argument). keep_attrs : bool, optional If True, the variable's attributes (`attrs`) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes. **kwargs : dict Additional keyword arguments passed on to `func`. Returns ------- reduced : DataArray DataArray with this object's array replaced with an array with summarized data and the indicated dimension(s) removed. """ var = self.variable.reduce(func, dim, axis, keep_attrs, **kwargs) return self._replace_maybe_drop_dims(var) def to_pandas(self): """Convert this array into a pandas object with the same shape. The type of the returned object depends on the number of DataArray dimensions: * 1D -> `pandas.Series` * 2D -> `pandas.DataFrame` * 3D -> `pandas.Panel` Only works for arrays with 3 or fewer dimensions. The DataArray constructor performs the inverse transformation. """ # TODO: consolidate the info about pandas constructors and the # attributes that correspond to their indexes into a separate module? constructors = {0: lambda x: x, 1: pd.Series, 2: pd.DataFrame, 3: pd.Panel} try: constructor = constructors[self.ndim] except KeyError: raise ValueError('cannot convert arrays with %s dimensions into ' 'pandas objects' % self.ndim) indexes = [self.get_index(dim) for dim in self.dims] return constructor(self.values, *indexes) def to_dataframe(self, name=None): """Convert this array and its coordinates into a tidy pandas.DataFrame. The DataFrame is indexed by the Cartesian product of index coordinates (in the form of a :py:class:`pandas.MultiIndex`). Other coordinates are included as columns in the DataFrame. """ if name is None: name = self.name if name is None: raise ValueError('cannot convert an unnamed DataArray to a ' 'DataFrame: use the ``name`` parameter') dims = OrderedDict(zip(self.dims, self.shape)) # By using a unique name, we can convert a DataArray into a DataFrame # even if it shares a name with one of its coordinates. # I would normally use unique_name = object() but that results in a # dataframe with columns in the wrong order, for reasons I have not # been able to debug (possibly a pandas bug?). unique_name = '__unique_name_identifier_z98xfz98xugfg73ho__' ds = self._to_dataset_whole(name=unique_name) df = ds._to_dataframe(dims) df.columns = [name if c == unique_name else c for c in df.columns] return df def to_series(self): """Convert this array into a pandas.Series. The Series is indexed by the Cartesian product of index coordinates (in the form of a :py:class:`pandas.MultiIndex`). """ index = self.coords.to_index() return pd.Series(self.values.reshape(-1), index=index, name=self.name) def to_masked_array(self, copy=True): """Convert this array into a numpy.ma.MaskedArray Parameters ---------- copy : bool If True (default) make a copy of the array in the result. If False, a MaskedArray view of DataArray.values is returned. Returns ------- result : MaskedArray Masked where invalid values (nan or inf) occur. """ isnull = pd.isnull(self.values) return np.ma.MaskedArray(data=self.values, mask=isnull, copy=copy) def to_netcdf(self, *args, **kwargs): """ Write DataArray contents to a netCDF file. Parameters ---------- path : str, optional Path to which to save this dataset. If no path is provided, this function returns the resulting netCDF file as a bytes object; in this case, we need to use scipy.io.netcdf, which does not support netCDF version 4 (the default format becomes NETCDF3_64BIT). mode : {'w', 'a'}, optional Write ('w') or append ('a') mode. If mode='w', any existing file at this location will be overwritten. format : {'NETCDF4', 'NETCDF4_CLASSIC', 'NETCDF3_64BIT', 'NETCDF3_CLASSIC'}, optional File format for the resulting netCDF file: * NETCDF4: Data is stored in an HDF5 file, using netCDF4 API features. * NETCDF4_CLASSIC: Data is stored in an HDF5 file, using only netCDF 3 compatible API features. * NETCDF3_64BIT: 64-bit offset version of the netCDF 3 file format, which fully supports 2+ GB files, but is only compatible with clients linked against netCDF version 3.6.0 or later. * NETCDF3_CLASSIC: The classic netCDF 3 file format. It does not handle 2+ GB files very well. All formats are supported by the netCDF4-python library. scipy.io.netcdf only supports the last two formats. The default format is NETCDF4 if you are saving a file to disk and have the netCDF4-python library available. Otherwise, xarray falls back to using scipy to write netCDF files and defaults to the NETCDF3_64BIT format (scipy does not support netCDF4). group : str, optional Path to the netCDF4 group in the given file to open (only works for format='NETCDF4'). The group(s) will be created if necessary. engine : {'netcdf4', 'scipy', 'h5netcdf'}, optional Engine to use when writing netCDF files. If not provided, the default engine is chosen based on available dependencies, with a preference for 'netcdf4' if writing to a file on disk. encoding : dict, optional Nested dictionary with variable names as keys and dictionaries of variable specific encodings as values, e.g., ``{'my_variable': {'dtype': 'int16', 'scale_factor': 0.1, 'zlib': True}, ...}`` Notes ----- Only xarray.Dataset objects can be written to netCDF files, so the xarray.DataArray is converted to a xarray.Dataset object containing a single variable. If the DataArray has no name, or if the name is the same as a co-ordinate name, then it is given the name '__xarray_dataarray_variable__'. All parameters are passed directly to `xarray.Dataset.to_netcdf`. """ from ..backends.api import DATAARRAY_NAME, DATAARRAY_VARIABLE if self.name is None: # If no name is set then use a generic xarray name dataset = self.to_dataset(name=DATAARRAY_VARIABLE) elif self.name in self.coords or self.name in self.dims: # The name is the same as one of the coords names, which netCDF # doesn't support, so rename it but keep track of the old name dataset = self.to_dataset(name=DATAARRAY_VARIABLE) dataset.attrs[DATAARRAY_NAME] = self.name else: # No problems with the name - so we're fine! dataset = self.to_dataset() dataset.to_netcdf(*args, **kwargs) def to_dict(self): """ Convert this xarray.DataArray into a dictionary following xarray naming conventions. Converts all variables and attributes to native Python objects. Useful for coverting to json. To avoid datetime incompatibility use decode_times=False kwarg in xarrray.open_dataset. See also -------- DataArray.from_dict """ d = {'coords': {}, 'attrs': decode_numpy_dict_values(self.attrs), 'dims': self.dims} for k in self.coords: data = ensure_us_time_resolution(self[k].values).tolist() d['coords'].update({ k: {'data': data, 'dims': self[k].dims, 'attrs': decode_numpy_dict_values(self[k].attrs)}}) d.update({'data': ensure_us_time_resolution(self.values).tolist(), 'name': self.name}) return d @classmethod def from_dict(cls, d): """ Convert a dictionary into an xarray.DataArray Input dict can take several forms:: d = {'dims': ('t'), 'data': x} d = {'coords': {'t': {'dims': 't', 'data': t, 'attrs': {'units':'s'}}}, 'attrs': {'title': 'air temperature'}, 'dims': 't', 'data': x, 'name': 'a'} where 't' is the name of the dimesion, 'a' is the name of the array, and x and t are lists, numpy.arrays, or pandas objects. Parameters ---------- d : dict, with a minimum structure of {'dims': [..], 'data': [..]} Returns ------- obj : xarray.DataArray See also -------- DataArray.to_dict Dataset.from_dict """ coords = None if 'coords' in d: try: coords = OrderedDict([(k, (v['dims'], v['data'], v.get('attrs'))) for k, v in d['coords'].items()]) except KeyError as e: raise ValueError( "cannot convert dict when coords are missing the key " "'{dims_data}'".format(dims_data=str(e.args[0]))) try: data = d['data'] except KeyError: raise ValueError("cannot convert dict without the key 'data''") else: obj = cls(data, coords, d.get('dims'), d.get('name'), d.get('attrs')) return obj @classmethod def from_series(cls, series): """Convert a pandas.Series into an xarray.DataArray. If the series's index is a MultiIndex, it will be expanded into a tensor product of one-dimensional coordinates (filling in missing values with NaN). Thus this operation should be the inverse of the `to_series` method. """ # TODO: add a 'name' parameter name = series.name df = pd.DataFrame({name: series}) ds = Dataset.from_dataframe(df) return ds[name] def to_cdms2(self): """Convert this array into a cdms2.Variable """ from ..convert import to_cdms2 return to_cdms2(self) @classmethod def from_cdms2(cls, variable): """Convert a cdms2.Variable into an xarray.DataArray """ from ..convert import from_cdms2 return from_cdms2(variable) def _all_compat(self, other, compat_str): """Helper function for equals and identical""" def compat(x, y): return getattr(x.variable, compat_str)(y.variable) return (utils.dict_equiv(self.coords, other.coords, compat=compat) and compat(self, other)) def broadcast_equals(self, other): """Two DataArrays are broadcast equal if they are equal after broadcasting them against each other such that they have the same dimensions. See Also -------- DataArray.equals DataArray.identical """ try: return self._all_compat(other, 'broadcast_equals') except (TypeError, AttributeError): return False def equals(self, other): """True if two DataArrays have the same dimensions, coordinates and values; otherwise False. DataArrays can still be equal (like pandas objects) if they have NaN values in the same locations. This method is necessary because `v1 == v2` for ``DataArray`` does element-wise comparisons (like numpy.ndarrays). See Also -------- DataArray.broadcast_equals DataArray.identical """ try: return self._all_compat(other, 'equals') except (TypeError, AttributeError): return False def identical(self, other): """Like equals, but also checks the array name and attributes, and attributes on all coordinates. See Also -------- DataArray.broadcast_equals DataArray.equal """ try: return (self.name == other.name and self._all_compat(other, 'identical')) except (TypeError, AttributeError): return False __default_name = object() def _result_name(self, other=None): # use the same naming heuristics as pandas: # https://github.com/ContinuumIO/blaze/issues/458#issuecomment-51936356 other_name = getattr(other, 'name', self.__default_name) if other_name is self.__default_name or other_name == self.name: return self.name else: return None def __array_wrap__(self, obj, context=None): new_var = self.variable.__array_wrap__(obj, context) return self._replace(new_var) @staticmethod def _unary_op(f): @functools.wraps(f) def func(self, *args, **kwargs): return self.__array_wrap__(f(self.variable.data, *args, **kwargs)) return func @staticmethod def _binary_op(f, reflexive=False, join=None, **ignored_kwargs): @functools.wraps(f) def func(self, other): if isinstance(other, (Dataset, groupby.GroupBy)): return NotImplemented if hasattr(other, 'indexes'): align_type = OPTIONS['arithmetic_join'] if join is None else join self, other = align(self, other, join=align_type, copy=False) other_variable = getattr(other, 'variable', other) other_coords = getattr(other, 'coords', None) variable = (f(self.variable, other_variable) if not reflexive else f(other_variable, self.variable)) coords = self.coords._merge_raw(other_coords) name = self._result_name(other) return self._replace(variable, coords, name) return func @staticmethod def _inplace_binary_op(f): @functools.wraps(f) def func(self, other): if isinstance(other, groupby.GroupBy): raise TypeError('in-place operations between a DataArray and ' 'a grouped object are not permitted') # n.b. we can't align other to self (with other.reindex_like(self)) # because `other` may be converted into floats, which would cause # in-place arithmetic to fail unpredictably. Instead, we simply # don't support automatic alignment with in-place arithmetic. other_coords = getattr(other, 'coords', None) other_variable = getattr(other, 'variable', other) with self.coords._merge_inplace(other_coords): f(self.variable, other_variable) return self return func def _copy_attrs_from(self, other): self.attrs = other.attrs @property def plot(self): """ Access plotting functions >>> d = DataArray([[1, 2], [3, 4]]) For convenience just call this directly >>> d.plot() Or use it as a namespace to use xarray.plot functions as DataArray methods >>> d.plot.imshow() # equivalent to xarray.plot.imshow(d) """ return _PlotMethods(self) def _title_for_slice(self, truncate=50): """ If the dataarray has 1 dimensional coordinates or comes from a slice we can show that info in the title Parameters ---------- truncate : integer maximum number of characters for title Returns ------- title : string Can be used for plot titles """ one_dims = [] for dim, coord in iteritems(self.coords): if coord.size == 1: one_dims.append('{dim} = {v}'.format( dim=dim, v=format_item(coord.values))) title = ', '.join(one_dims) if len(title) > truncate: title = title[:(truncate - 3)] + '...' return title def diff(self, dim, n=1, label='upper'): """Calculate the n-th order discrete difference along given axis. Parameters ---------- dim : str, optional Dimension over which to calculate the finite difference. n : int, optional The number of times values are differenced. label : str, optional The new coordinate in dimension ``dim`` will have the values of either the minuend's or subtrahend's coordinate for values 'upper' and 'lower', respectively. Other values are not supported. Returns ------- difference : same type as caller The n-th order finite difference of this object. Examples -------- >>> arr = xr.DataArray([5, 5, 6, 6], [[1, 2, 3, 4]], ['x']) >>> arr.diff('x') array([0, 1, 0]) Coordinates: * x (x) int64 2 3 4 >>> arr.diff('x', 2) array([ 1, -1]) Coordinates: * x (x) int64 3 4 """ ds = self._to_temp_dataset().diff(n=n, dim=dim, label=label) return self._from_temp_dataset(ds) def shift(self, **shifts): """Shift this array by an offset along one or more dimensions. Only the data is moved; coordinates stay in place. Values shifted from beyond array bounds are replaced by NaN. This is consistent with the behavior of ``shift`` in pandas. Parameters ---------- **shifts : keyword arguments of the form {dim: offset} Integer offset to shift along each of the given dimensions. Positive offsets shift to the right; negative offsets shift to the left. Returns ------- shifted : DataArray DataArray with the same coordinates and attributes but shifted data. See also -------- roll Examples -------- >>> arr = xr.DataArray([5, 6, 7], dims='x') >>> arr.shift(x=1) array([ nan, 5., 6.]) Coordinates: * x (x) int64 0 1 2 """ variable = self.variable.shift(**shifts) return self._replace(variable) def roll(self, **shifts): """Roll this array by an offset along one or more dimensions. Unlike shift, roll rotates all variables, including coordinates. The direction of rotation is consistent with :py:func:`numpy.roll`. Parameters ---------- **shifts : keyword arguments of the form {dim: offset} Integer offset to rotate each of the given dimensions. Positive offsets roll to the right; negative offsets roll to the left. Returns ------- rolled : DataArray DataArray with the same attributes but rolled data and coordinates. See also -------- shift Examples -------- >>> arr = xr.DataArray([5, 6, 7], dims='x') >>> arr.roll(x=1) array([7, 5, 6]) Coordinates: * x (x) int64 2 0 1 """ ds = self._to_temp_dataset().roll(**shifts) return self._from_temp_dataset(ds) @property def real(self): return self._replace(self.variable.real) @property def imag(self): return self._replace(self.variable.imag) def dot(self, other): """Perform dot product of two DataArrays along their shared dims. Equivalent to taking taking tensordot over all shared dims. Parameters ---------- other : DataArray The other array with which the dot product is performed. Returns ------- result : DataArray Array resulting from the dot product over all shared dimensions. See also -------- numpy.tensordot Examples -------- >>> da_vals = np.arange(6 * 5 * 4).reshape((6, 5, 4)) >>> da = DataArray(da_vals, dims=['x', 'y', 'z']) >>> dm_vals = np.arange(4) >>> dm = DataArray(dm_vals, dims=['z']) >>> dm.dims ('z') >>> da.dims ('x', 'y', 'z') >>> dot_result = da.dot(dm) >>> dot_result.dims ('x', 'y') """ if isinstance(other, Dataset): raise NotImplementedError('dot products are not yet supported ' 'with Dataset objects.') if not isinstance(other, DataArray): raise TypeError('dot only operates on DataArrays.') # sum over the common dims dims = set(self.dims) & set(other.dims) if len(dims) == 0: raise ValueError('DataArrays have no shared dimensions over which ' 'to perform dot.') self, other = align(self, other, join='inner', copy=False) axes = (self.get_axis_num(dims), other.get_axis_num(dims)) new_data = ops.tensordot(self.data, other.data, axes=axes) new_coords = self.coords.merge(other.coords) new_coords = new_coords.drop([d for d in dims if d in new_coords]) new_dims = ([d for d in self.dims if d not in dims] + [d for d in other.dims if d not in dims]) return type(self)(new_data, new_coords, new_dims) def quantile(self, q, dim=None, interpolation='linear', keep_attrs=False): """Compute the qth quantile of the data along the specified dimension. Returns the qth quantiles(s) of the array elements. Parameters ---------- q : float in range of [0,1] (or sequence of floats) Quantile to compute, which must be between 0 and 1 inclusive. dim : str or sequence of str, optional Dimension(s) over which to apply quantile. interpolation : {'linear', 'lower', 'higher', 'midpoint', 'nearest'} This optional parameter specifies the interpolation method to use when the desired quantile lies between two data points ``i < j``: * linear: ``i + (j - i) * fraction``, where ``fraction`` is the fractional part of the index surrounded by ``i`` and ``j``. * lower: ``i``. * higher: ``j``. * nearest: ``i`` or ``j``, whichever is nearest. * midpoint: ``(i + j) / 2``. keep_attrs : bool, optional If True, the dataset's attributes (`attrs`) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes. Returns ------- quantiles : DataArray If `q` is a single quantile, then the result is a scalar. If multiple percentiles are given, first axis of the result corresponds to the quantile and a quantile dimension is added to the return array. The other dimensions are the dimensions that remain after the reduction of the array. See Also -------- numpy.nanpercentile, pandas.Series.quantile, Dataset.quantile """ ds = self._to_temp_dataset().quantile(q, dim=dim, keep_attrs=keep_attrs, interpolation=interpolation) return self._from_temp_dataset(ds) # priority most be higher than Variable to properly work with binary ufuncs ops.inject_all_ops_and_reduce_methods(DataArray, priority=60) ����������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������xarray-0.9.1/xarray/core/dataset.py�����������������������������������������������������������������0000644�1311115�0011610�00000322712�13043511243�017367� 0����������������������������������������������������������������������������������������������������ustar �shoyer��������������������������eng�����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������from __future__ import absolute_import from __future__ import division from __future__ import print_function import functools from collections import Mapping, defaultdict from numbers import Number import sys import numpy as np import pandas as pd from . import ops from . import utils from . import groupby from . import indexing from . import alignment from . import formatting from .. import conventions from .alignment import align from .coordinates import DatasetCoordinates, LevelCoordinatesSource, Indexes from .common import ImplementsDatasetReduce, BaseDataObject from .merge import (dataset_update_method, dataset_merge_method, merge_data_and_coords) from .utils import (Frozen, SortedKeysDict, maybe_wrap_array, hashable, decode_numpy_dict_values, ensure_us_time_resolution) from .variable import (Variable, as_variable, IndexVariable, broadcast_variables) from .pycompat import (iteritems, basestring, OrderedDict, dask_array_type, range) from .combine import concat from .formatting import ensure_valid_repr from .options import OPTIONS # list of attributes of pd.DatetimeIndex that are ndarrays of time info _DATETIMEINDEX_COMPONENTS = ['year', 'month', 'day', 'hour', 'minute', 'second', 'microsecond', 'nanosecond', 'date', 'time', 'dayofyear', 'weekofyear', 'dayofweek', 'quarter'] def _get_virtual_variable(variables, key, level_vars=None, dim_sizes=None): """Get a virtual variable (e.g., 'time.year' or a MultiIndex level) from a dict of xarray.Variable objects (if possible) """ if level_vars is None: level_vars = {} if dim_sizes is None: dim_sizes = {} if key in dim_sizes: data = pd.Index(range(dim_sizes[key]), name=key) variable = IndexVariable((key,), data) return key, key, variable if not isinstance(key, basestring): raise KeyError(key) split_key = key.split('.', 1) if len(split_key) == 2: ref_name, var_name = split_key elif len(split_key) == 1: ref_name, var_name = key, None else: raise KeyError(key) if ref_name in level_vars: dim_var = variables[level_vars[ref_name]] ref_var = dim_var.to_index_variable().get_level_variable(ref_name) else: ref_var = variables[ref_name] if var_name is None: virtual_var = ref_var var_name = key else: if ref_var.ndim == 1: date = ref_var.to_index() elif ref_var.ndim == 0: date = pd.Timestamp(ref_var.values) else: raise KeyError(key) if var_name == 'season': # TODO: move 'season' into pandas itself seasons = np.array(['DJF', 'MAM', 'JJA', 'SON']) month = date.month data = seasons[(month // 3) % 4] else: data = getattr(date, var_name) virtual_var = Variable(ref_var.dims, data) return ref_name, var_name, virtual_var def calculate_dimensions(variables): """Calculate the dimensions corresponding to a set of variables. Returns dictionary mapping from dimension names to sizes. Raises ValueError if any of the dimension sizes conflict. """ dims = OrderedDict() last_used = {} scalar_vars = set(k for k, v in iteritems(variables) if not v.dims) for k, var in iteritems(variables): for dim, size in zip(var.dims, var.shape): if dim in scalar_vars: raise ValueError('dimension %r already exists as a scalar ' 'variable' % dim) if dim not in dims: dims[dim] = size last_used[dim] = k elif dims[dim] != size: raise ValueError('conflicting sizes for dimension %r: ' 'length %s on %r and length %s on %r' % (dim, size, k, dims[dim], last_used[dim])) return dims def merge_indexes( indexes, # type: Dict[Any, Union[Any, List[Any]]] variables, # type: Dict[Any, Variable] coord_names, # type: Set append=False, # type: bool ): # type: (...) -> Tuple[OrderedDict[Any, Variable], Set] """Merge variables into multi-indexes. Not public API. Used in Dataset and DataArray set_index methods. """ vars_to_replace = {} vars_to_remove = [] for dim, var_names in indexes.items(): if isinstance(var_names, basestring): var_names = [var_names] names, labels, levels = [], [], [] current_index_variable = variables.get(dim) if current_index_variable is not None and append: current_index = current_index_variable.to_index() if isinstance(current_index, pd.MultiIndex): names.extend(current_index.names) labels.extend(current_index.labels) levels.extend(current_index.levels) else: names.append('%s_level_0' % dim) cat = pd.Categorical(current_index.values, ordered=True) labels.append(cat.codes) levels.append(cat.categories) for n in var_names: names.append(n) var = variables[n] if (current_index_variable is not None and var.dims != current_index_variable.dims): raise ValueError( "dimension mismatch between %r %s and %r %s" % (dim, current_index_variable.dims, n, var.dims)) else: cat = pd.Categorical(var.values, ordered=True) labels.append(cat.codes) levels.append(cat.categories) idx = pd.MultiIndex(labels=labels, levels=levels, names=names) vars_to_replace[dim] = IndexVariable(dim, idx) vars_to_remove.extend(var_names) new_variables = OrderedDict([(k, v) for k, v in iteritems(variables) if k not in vars_to_remove]) new_variables.update(vars_to_replace) new_coord_names = coord_names | set(vars_to_replace) new_coord_names -= set(vars_to_remove) return new_variables, new_coord_names def split_indexes( dims_or_levels, # type: Union[Any, List[Any]] variables, # type: Dict[Any, Variable] coord_names, # type: Set level_coords, # type: Dict[Any, Any] drop=False, # type: bool ): # type: (...) -> Tuple[OrderedDict[Any, Variable], Set] """Extract (multi-)indexes (levels) as variables. Not public API. Used in Dataset and DataArray reset_index methods. """ if isinstance(dims_or_levels, basestring): dims_or_levels = [dims_or_levels] dim_levels = defaultdict(list) dims = [] for k in dims_or_levels: if k in level_coords: dim_levels[level_coords[k]].append(k) else: dims.append(k) vars_to_replace = {} vars_to_create = OrderedDict() vars_to_remove = [] for d in dims: index = variables[d].to_index() if isinstance(index, pd.MultiIndex): dim_levels[d] = index.names else: vars_to_remove.append(d) if not drop: vars_to_create[d + '_'] = Variable(d, index) for d, levs in dim_levels.items(): index = variables[d].to_index() if len(levs) == index.nlevels: vars_to_remove.append(d) else: vars_to_replace[d] = IndexVariable(d, index.droplevel(levs)) if not drop: for lev in levs: idx = index.get_level_values(lev) vars_to_create[idx.name] = Variable(d, idx) new_variables = variables.copy() for v in set(vars_to_remove): del new_variables[v] new_variables.update(vars_to_replace) new_variables.update(vars_to_create) new_coord_names = (coord_names | set(vars_to_create)) - set(vars_to_remove) return new_variables, new_coord_names def _assert_empty(args, msg='%s'): if args: raise ValueError(msg % args) def as_dataset(obj): """Cast the given object to a Dataset. Handles Datasets, DataArrays and dictionaries of variables. A new Dataset object is only created if the provided object is not already one. """ if hasattr(obj, 'to_dataset'): obj = obj.to_dataset() if not isinstance(obj, Dataset): obj = Dataset(obj) return obj class DataVariables(Mapping, formatting.ReprMixin): def __init__(self, dataset): self._dataset = dataset def __iter__(self): return (key for key in self._dataset._variables if key not in self._dataset._coord_names) def __len__(self): return len(self._dataset._variables) - len(self._dataset._coord_names) def __contains__(self, key): return (key in self._dataset._variables and key not in self._dataset._coord_names) def __getitem__(self, key): if key not in self._dataset._coord_names: return self._dataset[key] else: raise KeyError(key) def __unicode__(self): return formatting.data_vars_repr(self) @property def variables(self): all_variables = self._dataset.variables return Frozen(OrderedDict((k, all_variables[k]) for k in self)) class _LocIndexer(object): def __init__(self, dataset): self.dataset = dataset def __getitem__(self, key): if not utils.is_dict_like(key): raise TypeError('can only lookup dictionaries from Dataset.loc') return self.dataset.sel(**key) class Dataset(Mapping, ImplementsDatasetReduce, BaseDataObject, formatting.ReprMixin): """A multi-dimensional, in memory, array database. A dataset resembles an in-memory representation of a NetCDF file, and consists of variables, coordinates and attributes which together form a self describing dataset. Dataset implements the mapping interface with keys given by variable names and values given by DataArray objects for each variable name. One dimensional variables with name equal to their dimension are index coordinates used for label based indexing. """ groupby_cls = groupby.DatasetGroupBy def __init__(self, data_vars=None, coords=None, attrs=None, compat='broadcast_equals'): """To load data from a file or file-like object, use the `open_dataset` function. Parameters ---------- data_vars : dict-like, optional A mapping from variable names to :py:class:`~xarray.DataArray` objects, :py:class:`~xarray.Variable` objects or tuples of the form ``(dims, data[, attrs])`` which can be used as arguments to create a new ``Variable``. Each dimension must have the same length in all variables in which it appears. coords : dict-like, optional Another mapping in the same form as the `variables` argument, except the each item is saved on the dataset as a "coordinate". These variables have an associated meaning: they describe constant/fixed/independent quantities, unlike the varying/measured/dependent quantities that belong in `variables`. Coordinates values may be given by 1-dimensional arrays or scalars, in which case `dims` do not need to be supplied: 1D arrays will be assumed to give index values along the dimension with the same name. attrs : dict-like, optional Global attributes to save on this dataset. compat : {'broadcast_equals', 'equals', 'identical'}, optional String indicating how to compare variables of the same name for potential conflicts when initializing this dataset: - 'broadcast_equals': all values must be equal when variables are broadcast against each other to ensure common dimensions. - 'equals': all values and dimensions must be the same. - 'identical': all values, dimensions and attributes must be the same. """ self._variables = OrderedDict() self._coord_names = set() self._dims = {} self._attrs = None self._file_obj = None if data_vars is None: data_vars = {} if coords is None: coords = {} if data_vars is not None or coords is not None: self._set_init_vars_and_dims(data_vars, coords, compat) if attrs is not None: self.attrs = attrs self._encoding = None self._initialized = True def _set_init_vars_and_dims(self, data_vars, coords, compat): """Set the initial value of Dataset variables and dimensions """ both_data_and_coords = [k for k in data_vars if k in coords] if both_data_and_coords: raise ValueError('variables %r are found in both data_vars and ' 'coords' % both_data_and_coords) variables, coord_names, dims = merge_data_and_coords( data_vars, coords, compat=compat) self._variables = variables self._coord_names = coord_names self._dims = dims @classmethod def load_store(cls, store, decoder=None): """Create a new dataset from the contents of a backends.*DataStore object """ variables, attributes = store.load() if decoder: variables, attributes = decoder(variables, attributes) obj = cls(variables, attrs=attributes) obj._file_obj = store return obj @property def variables(self): """Frozen dictionary of xarray.Variable objects constituting this dataset's data """ return Frozen(self._variables) def _attrs_copy(self): return None if self._attrs is None else OrderedDict(self._attrs) @property def attrs(self): """Dictionary of global attributes on this dataset """ if self._attrs is None: self._attrs = OrderedDict() return self._attrs @attrs.setter def attrs(self, value): self._attrs = OrderedDict(value) @property def encoding(self): """Dictionary of global encoding attributes on this dataset """ if self._encoding is None: self._encoding = {} return self._encoding @encoding.setter def encoding(self, value): self._encoding = dict(value) @property def dims(self): """Mapping from dimension names to lengths. Cannot be modified directly, but is updated when adding new variables. Note that type of this object differs from `DataArray.dims`. See `Dataset.sizes` and `DataArray.sizes` for consistently named properties. """ return Frozen(SortedKeysDict(self._dims)) @property def sizes(self): """Mapping from dimension names to lengths. Cannot be modified directly, but is updated when adding new variables. This is an alias for `Dataset.dims` provided for the benefit of consistency with `DataArray.sizes`. See also -------- DataArray.sizes """ return self.dims def load(self): """Manually trigger loading of this dataset's data from disk or a remote source into memory and return this dataset. Normally, it should not be necessary to call this method in user code, because all xarray functions should either work on deferred data or load data automatically. However, this method can be necessary when working with many file objects on disk. """ # access .data to coerce everything to numpy or dask arrays lazy_data = {k: v._data for k, v in self.variables.items() if isinstance(v._data, dask_array_type)} if lazy_data: import dask.array as da # evaluate all the dask arrays simultaneously evaluated_data = da.compute(*lazy_data.values()) for k, data in zip(lazy_data, evaluated_data): self.variables[k].data = data # load everything else sequentially for k, v in self.variables.items(): if k not in lazy_data: v.load() return self def compute(self): """Manually trigger loading of this dataset's data from disk or a remote source into memory and return a new dataset. The original is left unaltered. Normally, it should not be necessary to call this method in user code, because all xarray functions should either work on deferred data or load data automatically. However, this method can be necessary when working with many file objects on disk. """ new = self.copy(deep=False) return new.load() @classmethod def _construct_direct(cls, variables, coord_names, dims=None, attrs=None, file_obj=None, encoding=None): """Shortcut around __init__ for internal use when we want to skip costly validation """ obj = object.__new__(cls) obj._variables = variables obj._coord_names = coord_names obj._dims = dims obj._attrs = attrs obj._file_obj = file_obj obj._encoding = encoding obj._initialized = True return obj __default_attrs = object() @classmethod def _from_vars_and_coord_names(cls, variables, coord_names, attrs=None): dims = dict(calculate_dimensions(variables)) return cls._construct_direct(variables, coord_names, dims, attrs) def _replace_vars_and_dims(self, variables, coord_names=None, dims=None, attrs=__default_attrs, inplace=False): """Fastpath constructor for internal use. Preserves coord names and attributes. If not provided explicitly, dimensions are recalculated from the supplied variables. The arguments are *not* copied when placed on the new dataset. It is up to the caller to ensure that they have the right type and are not used elsewhere. Parameters ---------- variables : OrderedDict coord_names : set or None, optional attrs : OrderedDict or None, optional Returns ------- new : Dataset """ if dims is None: dims = calculate_dimensions(variables) if inplace: self._dims = dims self._variables = variables if coord_names is not None: self._coord_names = coord_names if attrs is not self.__default_attrs: self._attrs = attrs obj = self else: if coord_names is None: coord_names = self._coord_names.copy() if attrs is self.__default_attrs: attrs = self._attrs_copy() obj = self._construct_direct(variables, coord_names, dims, attrs) return obj def _replace_indexes(self, indexes): if not len(indexes): return self variables = self._variables.copy() for name, idx in indexes.items(): variables[name] = IndexVariable(name, idx) obj = self._replace_vars_and_dims(variables) # switch from dimension to level names, if necessary dim_names = {} for dim, idx in indexes.items(): if not isinstance(idx, pd.MultiIndex) and idx.name != dim: dim_names[dim] = idx.name if dim_names: obj = obj.rename(dim_names) return obj def copy(self, deep=False): """Returns a copy of this dataset. If `deep=True`, a deep copy is made of each of the component variables. Otherwise, a shallow copy of each of the component variable is made, so that the underlying memory region of the new dataset is the same as in the original dataset. """ variables = OrderedDict((k, v.copy(deep=deep)) for k, v in iteritems(self._variables)) # skip __init__ to avoid costly validation return self._construct_direct(variables, self._coord_names.copy(), self._dims.copy(), self._attrs_copy()) def _subset_with_all_valid_coords(self, variables, coord_names, attrs): needed_dims = set() for v in variables.values(): needed_dims.update(v.dims) for k in self._coord_names: if set(self.variables[k].dims) <= needed_dims: variables[k] = self._variables[k] coord_names.add(k) dims = dict((k, self._dims[k]) for k in needed_dims) return self._construct_direct(variables, coord_names, dims, attrs) @property def _level_coords(self): """Return a mapping of all MultiIndex levels and their corresponding coordinate name. """ level_coords = OrderedDict() for cname in self._coord_names: var = self.variables[cname] if var.ndim == 1: level_names = var.to_index_variable().level_names if level_names is not None: dim, = var.dims level_coords.update({lname: dim for lname in level_names}) return level_coords def _copy_listed(self, names): """Create a new Dataset with the listed variables from this dataset and the all relevant coordinates. Skips all validation. """ variables = OrderedDict() coord_names = set() for name in names: try: variables[name] = self._variables[name] except KeyError: ref_name, var_name, var = _get_virtual_variable( self._variables, name, self._level_coords, self.dims) variables[var_name] = var if ref_name in self._coord_names or ref_name in self.dims: coord_names.add(var_name) return self._subset_with_all_valid_coords(variables, coord_names, attrs=self.attrs.copy()) def _construct_dataarray(self, name): """Construct a DataArray by indexing this dataset """ from .dataarray import DataArray try: variable = self._variables[name] except KeyError: _, name, variable = _get_virtual_variable( self._variables, name, self._level_coords, self.dims) coords = OrderedDict() needed_dims = set(variable.dims) for k in self.coords: if set(self.variables[k].dims) <= needed_dims: coords[k] = self.variables[k] return DataArray(variable, coords, name=name, fastpath=True) def __copy__(self): return self.copy(deep=False) def __deepcopy__(self, memo=None): # memo does nothing but is required for compatibility with # copy.deepcopy return self.copy(deep=True) @property def _attr_sources(self): """List of places to look-up items for attribute-style access""" return [self, LevelCoordinatesSource(self), self.attrs] def __contains__(self, key): """The 'in' operator will return true or false depending on whether 'key' is an array in the dataset or not. """ return key in self._variables def __len__(self): return len(self._variables) def __iter__(self): return iter(self._variables) @property def nbytes(self): return sum(v.nbytes for v in self.variables.values()) @property def loc(self): """Attribute for location based indexing. Only supports __getitem__, and only when the key is a dict of the form {dim: labels}. """ return _LocIndexer(self) def __getitem__(self, key): """Access variables or coordinates this dataset as a :py:class:`~xarray.DataArray`. Indexing with a list of names will return a new ``Dataset`` object. """ if utils.is_dict_like(key): return self.isel(**key) if hashable(key): return self._construct_dataarray(key) else: return self._copy_listed(np.asarray(key)) def __setitem__(self, key, value): """Add an array to this dataset. If value is a `DataArray`, call its `select_vars()` method, rename it to `key` and merge the contents of the resulting dataset into this dataset. If value is an `Variable` object (or tuple of form ``(dims, data[, attrs])``), add it to this dataset as a new variable. """ if utils.is_dict_like(key): raise NotImplementedError('cannot yet use a dictionary as a key ' 'to set Dataset values') self.update({key: value}) def __delitem__(self, key): """Remove a variable from this dataset. """ del self._variables[key] self._coord_names.discard(key) # mutable objects should not be hashable __hash__ = None def _all_compat(self, other, compat_str): """Helper function for equals and identical""" # some stores (e.g., scipy) do not seem to preserve order, so don't # require matching order for equality def compat(x, y): return getattr(x, compat_str)(y) return (self._coord_names == other._coord_names and utils.dict_equiv(self._variables, other._variables, compat=compat)) def broadcast_equals(self, other): """Two Datasets are broadcast equal if they are equal after broadcasting all variables against each other. For example, variables that are scalar in one dataset but non-scalar in the other dataset can still be broadcast equal if the the non-scalar variable is a constant. See Also -------- Dataset.equals Dataset.identical """ try: return self._all_compat(other, 'broadcast_equals') except (TypeError, AttributeError): return False def equals(self, other): """Two Datasets are equal if they have matching variables and coordinates, all of which are equal. Datasets can still be equal (like pandas objects) if they have NaN values in the same locations. This method is necessary because `v1 == v2` for ``Dataset`` does element-wise comparisons (like numpy.ndarrays). See Also -------- Dataset.broadcast_equals Dataset.identical """ try: return self._all_compat(other, 'equals') except (TypeError, AttributeError): return False def identical(self, other): """Like equals, but also checks all dataset attributes and the attributes on all variables and coordinates. See Also -------- Dataset.broadcast_equals Dataset.equals """ try: return (utils.dict_equiv(self.attrs, other.attrs) and self._all_compat(other, 'identical')) except (TypeError, AttributeError): return False @property def indexes(self): """OrderedDict of pandas.Index objects used for label based indexing """ return Indexes(self._variables, self._dims) @property def coords(self): """Dictionary of xarray.DataArray objects corresponding to coordinate variables """ return DatasetCoordinates(self) @property def data_vars(self): """Dictionary of xarray.DataArray objects corresponding to data variables """ return DataVariables(self) def set_coords(self, names, inplace=False): """Given names of one or more variables, set them as coordinates Parameters ---------- names : str or list of str Name(s) of variables in this dataset to convert into coordinates. inplace : bool, optional If True, modify this dataset inplace. Otherwise, create a new object. Returns ------- Dataset """ # TODO: allow inserting new coordinates with this method, like # DataFrame.set_index? # nb. check in self._variables, not self.data_vars to insure that the # operation is idempotent if isinstance(names, basestring): names = [names] self._assert_all_in_dataset(names) obj = self if inplace else self.copy() obj._coord_names.update(names) return obj def reset_coords(self, names=None, drop=False, inplace=False): """Given names of coordinates, reset them to become variables Parameters ---------- names : str or list of str, optional Name(s) of non-index coordinates in this dataset to reset into variables. By default, all non-index coordinates are reset. drop : bool, optional If True, remove coordinates instead of converting them into variables. inplace : bool, optional If True, modify this dataset inplace. Otherwise, create a new object. Returns ------- Dataset """ if names is None: names = self._coord_names - set(self.dims) else: if isinstance(names, basestring): names = [names] self._assert_all_in_dataset(names) bad_coords = set(names) & set(self.dims) if bad_coords: raise ValueError( 'cannot remove index coordinates with reset_coords: %s' % bad_coords) obj = self if inplace else self.copy() obj._coord_names.difference_update(names) if drop: for name in names: del obj._variables[name] return obj def dump_to_store(self, store, encoder=None, sync=True, encoding=None, unlimited_dims=None): """Store dataset contents to a backends.*DataStore object.""" if encoding is None: encoding = {} variables, attrs = conventions.encode_dataset_coordinates(self) check_encoding = set() for k, enc in encoding.items(): # no need to shallow copy the variable again; that already happened # in encode_dataset_coordinates variables[k].encoding = enc check_encoding.add(k) if encoder: variables, attrs = encoder(variables, attrs) store.store(variables, attrs, check_encoding, unlimited_dims=unlimited_dims) if sync: store.sync() def to_netcdf(self, path=None, mode='w', format=None, group=None, engine=None, encoding=None, unlimited_dims=None): """Write dataset contents to a netCDF file. Parameters ---------- path : str, optional Path to which to save this dataset. If no path is provided, this function returns the resulting netCDF file as a bytes object; in this case, we need to use scipy.io.netcdf, which does not support netCDF version 4 (the default format becomes NETCDF3_64BIT). mode : {'w', 'a'}, optional Write ('w') or append ('a') mode. If mode='w', any existing file at this location will be overwritten. format : {'NETCDF4', 'NETCDF4_CLASSIC', 'NETCDF3_64BIT', 'NETCDF3_CLASSIC'}, optional File format for the resulting netCDF file: * NETCDF4: Data is stored in an HDF5 file, using netCDF4 API features. * NETCDF4_CLASSIC: Data is stored in an HDF5 file, using only netCDF 3 compatible API features. * NETCDF3_64BIT: 64-bit offset version of the netCDF 3 file format, which fully supports 2+ GB files, but is only compatible with clients linked against netCDF version 3.6.0 or later. * NETCDF3_CLASSIC: The classic netCDF 3 file format. It does not handle 2+ GB files very well. All formats are supported by the netCDF4-python library. scipy.io.netcdf only supports the last two formats. The default format is NETCDF4 if you are saving a file to disk and have the netCDF4-python library available. Otherwise, xarray falls back to using scipy to write netCDF files and defaults to the NETCDF3_64BIT format (scipy does not support netCDF4). group : str, optional Path to the netCDF4 group in the given file to open (only works for format='NETCDF4'). The group(s) will be created if necessary. engine : {'netcdf4', 'scipy', 'h5netcdf'}, optional Engine to use when writing netCDF files. If not provided, the default engine is chosen based on available dependencies, with a preference for 'netcdf4' if writing to a file on disk. encoding : dict, optional Nested dictionary with variable names as keys and dictionaries of variable specific encodings as values, e.g., ``{'my_variable': {'dtype': 'int16', 'scale_factor': 0.1, 'zlib': True}, ...}`` unlimited_dims : sequence of str, optional Dimension(s) that should be serialized as unlimited dimensions. By default, no dimensions are treated as unlimited dimensions. Note that unlimited_dims may also be set via ``dataset.encoding['unlimited_dims']``. """ if encoding is None: encoding = {} from ..backends.api import to_netcdf return to_netcdf(self, path, mode, format=format, group=group, engine=engine, encoding=encoding, unlimited_dims=unlimited_dims) def __unicode__(self): return formatting.dataset_repr(self) def info(self, buf=None): """ Concise summary of a Dataset variables and attributes. Parameters ---------- buf : writable buffer, defaults to sys.stdout See Also -------- pandas.DataFrame.assign netCDF's ncdump """ if buf is None: # pragma: no cover buf = sys.stdout lines = [] lines.append(u'xarray.Dataset {') lines.append(u'dimensions:') for name, size in self.dims.items(): lines.append(u'\t{name} = {size} ;'.format(name=name, size=size)) lines.append(u'\nvariables:') for name, da in self.variables.items(): dims = u', '.join(da.dims) lines.append(u'\t{type} {name}({dims}) ;'.format( type=da.dtype, name=name, dims=dims)) for k, v in da.attrs.items(): lines.append(u'\t\t{name}:{k} = {v} ;'.format(name=name, k=k, v=v)) lines.append(u'\n// global attributes:') for k, v in self.attrs.items(): lines.append(u'\t:{k} = {v} ;'.format(k=k, v=v)) lines.append(u'}') buf.write(u'\n'.join(lines)) @property def chunks(self): """Block dimensions for this dataset's data or None if it's not a dask array. """ chunks = {} for v in self.variables.values(): if v.chunks is not None: for dim, c in zip(v.dims, v.chunks): if dim in chunks and c != chunks[dim]: raise ValueError('inconsistent chunks') chunks[dim] = c return Frozen(SortedKeysDict(chunks)) def chunk(self, chunks=None, name_prefix='xarray-', token=None, lock=False): """Coerce all arrays in this dataset into dask arrays with the given chunks. Non-dask arrays in this dataset will be converted to dask arrays. Dask arrays will be rechunked to the given chunk sizes. If neither chunks is not provided for one or more dimensions, chunk sizes along that dimension will not be updated; non-dask arrays will be converted into dask arrays with a single block. Parameters ---------- chunks : int or dict, optional Chunk sizes along each dimension, e.g., ``5`` or ``{'x': 5, 'y': 5}``. name_prefix : str, optional Prefix for the name of any new dask arrays. token : str, optional Token uniquely identifying this dataset. lock : optional Passed on to :py:func:`dask.array.from_array`, if the array is not already as dask array. Returns ------- chunked : xarray.Dataset """ try: from dask.base import tokenize except ImportError: import dask # raise the usual error if dask is entirely missing raise ImportError('xarray requires dask version 0.6 or newer') if isinstance(chunks, Number): chunks = dict.fromkeys(self.dims, chunks) if chunks is not None: bad_dims = [d for d in chunks if d not in self.dims] if bad_dims: raise ValueError('some chunks keys are not dimensions on this ' 'object: %s' % bad_dims) def selkeys(dict_, keys): if dict_ is None: return None return dict((d, dict_[d]) for d in keys if d in dict_) def maybe_chunk(name, var, chunks): chunks = selkeys(chunks, var.dims) if not chunks: chunks = None if var.ndim > 0: token2 = tokenize(name, token if token else var._data) name2 = '%s%s-%s' % (name_prefix, name, token2) return var.chunk(chunks, name=name2, lock=lock) else: return var variables = OrderedDict([(k, maybe_chunk(k, v, chunks)) for k, v in self.variables.items()]) return self._replace_vars_and_dims(variables) def isel(self, drop=False, **indexers): """Returns a new dataset with each array indexed along the specified dimension(s). This method selects values from each array using its `__getitem__` method, except this method does not require knowing the order of each array's dimensions. Parameters ---------- drop : bool, optional If ``drop=True``, drop coordinates variables indexed by integers instead of making them scalar. **indexers : {dim: indexer, ...} Keyword arguments with names matching dimensions and values given by integers, slice objects or arrays. Returns ------- obj : Dataset A new Dataset with the same contents as this dataset, except each array and dimension is indexed by the appropriate indexers. In general, each array's data will be a view of the array's data in this dataset, unless numpy fancy indexing was triggered by using an array indexer, in which case the data will be a copy. See Also -------- Dataset.sel Dataset.sel_points Dataset.isel_points DataArray.isel """ invalid = [k for k in indexers if k not in self.dims] if invalid: raise ValueError("dimensions %r do not exist" % invalid) # all indexers should be int, slice or np.ndarrays indexers = [(k, (np.asarray(v) if not isinstance(v, (int, np.integer, slice)) else v)) for k, v in iteritems(indexers)] variables = OrderedDict() for name, var in iteritems(self._variables): var_indexers = dict((k, v) for k, v in indexers if k in var.dims) new_var = var.isel(**var_indexers) if not (drop and name in var_indexers): variables[name] = new_var coord_names = set(self._coord_names) & set(variables) return self._replace_vars_and_dims(variables, coord_names=coord_names) def sel(self, method=None, tolerance=None, drop=False, **indexers): """Returns a new dataset with each array indexed by tick labels along the specified dimension(s). In contrast to `Dataset.isel`, indexers for this method should use labels instead of integers. Under the hood, this method is powered by using pandas's powerful Index objects. This makes label based indexing essentially just as fast as using integer indexing. It also means this method uses pandas's (well documented) logic for indexing. This means you can use string shortcuts for datetime indexes (e.g., '2000-01' to select all values in January 2000). It also means that slices are treated as inclusive of both the start and stop values, unlike normal Python indexing. Parameters ---------- method : {None, 'nearest', 'pad'/'ffill', 'backfill'/'bfill'}, optional Method to use for inexact matches (requires pandas>=0.16): * None (default): only exact matches * pad / ffill: propagate last valid index value forward * backfill / bfill: propagate next valid index value backward * nearest: use nearest valid index value tolerance : optional Maximum distance between original and new labels for inexact matches. The values of the index at the matching locations most satisfy the equation ``abs(index[indexer] - target) <= tolerance``. Requires pandas>=0.17. drop : bool, optional If ``drop=True``, drop coordinates variables in `indexers` instead of making them scalar. **indexers : {dim: indexer, ...} Keyword arguments with names matching dimensions and values given by scalars, slices or arrays of tick labels. For dimensions with multi-index, the indexer may also be a dict-like object with keys matching index level names. Returns ------- obj : Dataset A new Dataset with the same contents as this dataset, except each variable and dimension is indexed by the appropriate indexers. In general, each variable's data will be a view of the variable's data in this dataset, unless numpy fancy indexing was triggered by using an array indexer, in which case the data will be a copy. See Also -------- Dataset.isel Dataset.sel_points Dataset.isel_points DataArray.sel """ pos_indexers, new_indexes = indexing.remap_label_indexers( self, indexers, method=method, tolerance=tolerance ) result = self.isel(drop=drop, **pos_indexers) return result._replace_indexes(new_indexes) def isel_points(self, dim='points', **indexers): # type: (...) -> Dataset """Returns a new dataset with each array indexed pointwise along the specified dimension(s). This method selects pointwise values from each array and is akin to the NumPy indexing behavior of `arr[[0, 1], [0, 1]]`, except this method does not require knowing the order of each array's dimensions. Parameters ---------- dim : str or DataArray or pandas.Index or other list-like object, optional Name of the dimension to concatenate along. If dim is provided as a string, it must be a new dimension name, in which case it is added along axis=0. If dim is provided as a DataArray or Index or list-like object, its name, which must not be present in the dataset, is used as the dimension to concatenate along and the values are added as a coordinate. **indexers : {dim: indexer, ...} Keyword arguments with names matching dimensions and values given by array-like objects. All indexers must be the same length and 1 dimensional. Returns ------- obj : Dataset A new Dataset with the same contents as this dataset, except each array and dimension is indexed by the appropriate indexers. With pointwise indexing, the new Dataset will always be a copy of the original. See Also -------- Dataset.sel Dataset.isel Dataset.sel_points DataArray.isel_points """ from .dataarray import DataArray indexer_dims = set(indexers) def take(variable, slices): # Note: remove helper function when once when numpy # supports vindex https://github.com/numpy/numpy/pull/6075 if hasattr(variable.data, 'vindex'): # Special case for dask backed arrays to use vectorised list indexing sel = variable.data.vindex[slices] else: # Otherwise assume backend is numpy array with 'fancy' indexing sel = variable.data[slices] return sel def relevant_keys(mapping): return [k for k, v in mapping.items() if any(d in indexer_dims for d in v.dims)] coords = relevant_keys(self.coords) indexers = [(k, np.asarray(v)) for k, v in iteritems(indexers)] indexers_dict = dict(indexers) non_indexed_dims = set(self.dims) - indexer_dims # All the indexers should be iterables # Check that indexers are valid dims, integers, and 1D for k, v in indexers: if k not in self.dims: raise ValueError("dimension %s does not exist" % k) if v.dtype.kind != 'i': raise TypeError('Indexers must be integers') if v.ndim != 1: raise ValueError('Indexers must be 1 dimensional') # all the indexers should have the same length lengths = set(len(v) for k, v in indexers) if len(lengths) > 1: raise ValueError('All indexers must be the same length') # Existing dimensions are not valid choices for the dim argument if isinstance(dim, basestring): if dim in self.dims: # dim is an invalid string raise ValueError('Existing dimension names are not valid ' 'choices for the dim argument in sel_points') elif hasattr(dim, 'dims'): # dim is a DataArray or Coordinate if dim.name in self.dims: # dim already exists raise ValueError('Existing dimensions are not valid choices ' 'for the dim argument in sel_points') # Set the new dim_name, and optionally the new dim coordinate # dim is either an array-like or a string if not utils.is_scalar(dim): # dim is array like get name or assign 'points', get as variable dim_name = 'points' if not hasattr(dim, 'name') else dim.name dim_coord = as_variable(dim, name=dim_name) else: # dim is a string dim_name = dim dim_coord = None reordered = self.transpose(*(list(indexer_dims) + list(non_indexed_dims))) variables = OrderedDict() for name, var in reordered.variables.items(): if name in indexers_dict or any(d in indexer_dims for d in var.dims): # slice if var is an indexer or depends on an indexed dim slc = [indexers_dict[k] if k in indexers_dict else slice(None) for k in var.dims] var_dims = [dim_name] + [d for d in var.dims if d in non_indexed_dims] selection = take(var, tuple(slc)) var_subset = type(var)(var_dims, selection, var.attrs) variables[name] = var_subset else: # If not indexed just add it back to variables or coordinates variables[name] = var coord_names = set(coords) & set(variables) dset = self._replace_vars_and_dims(variables, coord_names=coord_names) # Add the dim coord to the new dset. Must be done after creation # because_replace_vars_and_dims can only access existing coords, # not add new ones if dim_coord is not None: dset.coords[dim_name] = dim_coord return dset def sel_points(self, dim='points', method=None, tolerance=None, **indexers): """Returns a new dataset with each array indexed pointwise by tick labels along the specified dimension(s). In contrast to `Dataset.isel_points`, indexers for this method should use labels instead of integers. In contrast to `Dataset.sel`, this method selects points along the diagonal of multi-dimensional arrays, not the intersection. Parameters ---------- dim : str or DataArray or pandas.Index or other list-like object, optional Name of the dimension to concatenate along. If dim is provided as a string, it must be a new dimension name, in which case it is added along axis=0. If dim is provided as a DataArray or Index or list-like object, its name, which must not be present in the dataset, is used as the dimension to concatenate along and the values are added as a coordinate. method : {None, 'nearest', 'pad'/'ffill', 'backfill'/'bfill'}, optional Method to use for inexact matches (requires pandas>=0.16): * None (default): only exact matches * pad / ffill: propagate last valid index value forward * backfill / bfill: propagate next valid index value backward * nearest: use nearest valid index value tolerance : optional Maximum distance between original and new labels for inexact matches. The values of the index at the matching locations most satisfy the equation ``abs(index[indexer] - target) <= tolerance``. Requires pandas>=0.17. **indexers : {dim: indexer, ...} Keyword arguments with names matching dimensions and values given by array-like objects. All indexers must be the same length and 1 dimensional. Returns ------- obj : Dataset A new Dataset with the same contents as this dataset, except each array and dimension is indexed by the appropriate indexers. With pointwise indexing, the new Dataset will always be a copy of the original. See Also -------- Dataset.sel Dataset.isel Dataset.isel_points DataArray.sel_points """ pos_indexers, _ = indexing.remap_label_indexers( self, indexers, method=method, tolerance=tolerance ) return self.isel_points(dim=dim, **pos_indexers) def reindex_like(self, other, method=None, tolerance=None, copy=True): """Conform this object onto the indexes of another object, filling in missing values with NaN. Parameters ---------- other : Dataset or DataArray Object with an 'indexes' attribute giving a mapping from dimension names to pandas.Index objects, which provides coordinates upon which to index the variables in this dataset. The indexes on this other object need not be the same as the indexes on this dataset. Any mis-matched index values will be filled in with NaN, and any mis-matched dimension names will simply be ignored. method : {None, 'nearest', 'pad'/'ffill', 'backfill'/'bfill'}, optional Method to use for filling index values from other not found in this dataset: * None (default): don't fill gaps * pad / ffill: propagate last valid index value forward * backfill / bfill: propagate next valid index value backward * nearest: use nearest valid index value (requires pandas>=0.16) tolerance : optional Maximum distance between original and new labels for inexact matches. The values of the index at the matching locations most satisfy the equation ``abs(index[indexer] - target) <= tolerance``. Requires pandas>=0.17. copy : bool, optional If ``copy=True``, data in the return value is always copied. If ``copy=False`` and reindexing is unnecessary, or can be performed with only slice operations, then the output may share memory with the input. In either case, a new xarray object is always returned. Returns ------- reindexed : Dataset Another dataset, with this dataset's data but coordinates from the other object. See Also -------- Dataset.reindex align """ indexers = alignment.reindex_like_indexers(self, other) return self.reindex(method=method, copy=copy, tolerance=tolerance, **indexers) def reindex(self, indexers=None, method=None, tolerance=None, copy=True, **kw_indexers): """Conform this object onto a new set of indexes, filling in missing values with NaN. Parameters ---------- indexers : dict. optional Dictionary with keys given by dimension names and values given by arrays of coordinates tick labels. Any mis-matched coordinate values will be filled in with NaN, and any mis-matched dimension names will simply be ignored. method : {None, 'nearest', 'pad'/'ffill', 'backfill'/'bfill'}, optional Method to use for filling index values in ``indexers`` not found in this dataset: * None (default): don't fill gaps * pad / ffill: propagate last valid index value forward * backfill / bfill: propagate next valid index value backward * nearest: use nearest valid index value (requires pandas>=0.16) tolerance : optional Maximum distance between original and new labels for inexact matches. The values of the index at the matching locations most satisfy the equation ``abs(index[indexer] - target) <= tolerance``. Requires pandas>=0.17. copy : bool, optional If ``copy=True``, data in the return value is always copied. If ``copy=False`` and reindexing is unnecessary, or can be performed with only slice operations, then the output may share memory with the input. In either case, a new xarray object is always returned. **kw_indexers : optional Keyword arguments in the same form as ``indexers``. Returns ------- reindexed : Dataset Another dataset, with this dataset's data but replaced coordinates. See Also -------- Dataset.reindex_like align pandas.Index.get_indexer """ indexers = utils.combine_pos_and_kw_args(indexers, kw_indexers, 'reindex') bad_dims = [d for d in indexers if d not in self.dims] if bad_dims: raise ValueError('invalid reindex dimensions: %s' % bad_dims) variables = alignment.reindex_variables( self.variables, self.sizes, self.indexes, indexers, method, tolerance, copy=copy) coord_names = set(self._coord_names) coord_names.update(indexers) return self._replace_vars_and_dims(variables, coord_names) def rename(self, name_dict, inplace=False): """Returns a new object with renamed variables and dimensions. Parameters ---------- name_dict : dict-like Dictionary whose keys are current variable or dimension names and whose values are the desired names. inplace : bool, optional If True, rename variables and dimensions in-place. Otherwise, return a new dataset object. Returns ------- renamed : Dataset Dataset with renamed variables and dimensions. See Also -------- Dataset.swap_dims DataArray.rename """ for k, v in name_dict.items(): if k not in self and k not in self.dims: raise ValueError("cannot rename %r because it is not a " "variable or dimension in this dataset" % k) if v in self and k != v: raise ValueError('the new name %r already exists' % v) variables = OrderedDict() coord_names = set() for k, v in iteritems(self._variables): name = name_dict.get(k, k) dims = tuple(name_dict.get(dim, dim) for dim in v.dims) var = v.copy(deep=False) var.dims = dims variables[name] = var if k in self._coord_names: coord_names.add(name) dims = OrderedDict((name_dict.get(k, k), v) for k, v in self.dims.items()) return self._replace_vars_and_dims(variables, coord_names, dims=dims, inplace=inplace) def swap_dims(self, dims_dict, inplace=False): """Returns a new object with swapped dimensions. Parameters ---------- dims_dict : dict-like Dictionary whose keys are current dimension names and whose values are new names. Each value must already be a variable in the dataset. inplace : bool, optional If True, swap dimensions in-place. Otherwise, return a new dataset object. Returns ------- renamed : Dataset Dataset with swapped dimensions. See Also -------- Dataset.rename DataArray.swap_dims """ for k, v in dims_dict.items(): if k not in self.dims: raise ValueError('cannot swap from dimension %r because it is ' 'not an existing dimension' % k) if self.variables[v].dims != (k,): raise ValueError('replacement dimension %r is not a 1D ' 'variable along the old dimension %r' % (v, k)) result_dims = set(dims_dict.get(dim, dim) for dim in self.dims) variables = OrderedDict() coord_names = self._coord_names.copy() coord_names.update(dims_dict.values()) for k, v in iteritems(self.variables): dims = tuple(dims_dict.get(dim, dim) for dim in v.dims) if k in result_dims: var = v.to_index_variable() else: var = v.to_base_variable() var.dims = dims variables[k] = var return self._replace_vars_and_dims(variables, coord_names, inplace=inplace) def set_index(self, append=False, inplace=False, **indexes): """Set Dataset (multi-)indexes using one or more existing coordinates or variables. Parameters ---------- append : bool, optional If True, append the supplied index(es) to the existing index(es). Otherwise replace the existing index(es) (default). inplace : bool, optional If True, set new index(es) in-place. Otherwise, return a new Dataset object. **indexes : {dim: index, ...} Keyword arguments with names matching dimensions and values given by (lists of) the names of existing coordinates or variables to set as new (multi-)index. Returns ------- obj : Dataset Another dataset, with this dataset's data but replaced coordinates. See Also -------- Dataset.reset_index """ variables, coord_names = merge_indexes(indexes, self._variables, self._coord_names, append=append) return self._replace_vars_and_dims(variables, coord_names=coord_names, inplace=inplace) def reset_index(self, dims_or_levels, drop=False, inplace=False): """Reset the specified index(es) or multi-index level(s). Parameters ---------- dims_or_levels : str or list Name(s) of the dimension(s) and/or multi-index level(s) that will be reset. drop : bool, optional If True, remove the specified indexes and/or multi-index levels instead of extracting them as new coordinates (default: False). inplace : bool, optional If True, modify the dataset in-place. Otherwise, return a new Dataset object. Returns ------- obj : Dataset Another dataset, with this dataset's data but replaced coordinates. See Also -------- Dataset.set_index """ variables, coord_names = split_indexes(dims_or_levels, self._variables, self._coord_names, self._level_coords, drop=drop) return self._replace_vars_and_dims(variables, coord_names=coord_names, inplace=inplace) def reorder_levels(self, inplace=False, **dim_order): """Rearrange index levels using input order. Parameters ---------- inplace : bool, optional If True, modify the dataset in-place. Otherwise, return a new DataArray object. **dim_order : optional Keyword arguments with names matching dimensions and values given by lists representing new level orders. Every given dimension must have a multi-index. Returns ------- obj : Dataset Another dataset, with this dataset's data but replaced coordinates. """ replace_variables = {} for dim, order in dim_order.items(): coord = self._variables[dim] index = coord.to_index() if not isinstance(index, pd.MultiIndex): raise ValueError("coordinate %r has no MultiIndex" % dim) replace_variables[dim] = IndexVariable(coord.dims, index.reorder_levels(order)) variables = self._variables.copy() variables.update(replace_variables) return self._replace_vars_and_dims(variables, inplace=inplace) def _stack_once(self, dims, new_dim): variables = OrderedDict() for name, var in self.variables.items(): if name not in dims: if any(d in var.dims for d in dims): add_dims = [d for d in dims if d not in var.dims] vdims = list(var.dims) + add_dims shape = [self.dims[d] for d in vdims] exp_var = var.expand_dims(vdims, shape) stacked_var = exp_var.stack(**{new_dim: dims}) variables[name] = stacked_var else: variables[name] = var.copy(deep=False) # consider dropping levels that are unused? levels = [self.get_index(dim) for dim in dims] if hasattr(pd, 'RangeIndex'): # RangeIndex levels in a MultiIndex are broken for appending in # pandas before v0.19.0 levels = [pd.Int64Index(level) if isinstance(level, pd.RangeIndex) else level for level in levels] idx = utils.multiindex_from_product_levels(levels, names=dims) variables[new_dim] = IndexVariable(new_dim, idx) coord_names = set(self._coord_names) - set(dims) | set([new_dim]) return self._replace_vars_and_dims(variables, coord_names) def stack(self, **dimensions): """ Stack any number of existing dimensions into a single new dimension. New dimensions will be added at the end, and the corresponding coordinate variables will be combined into a MultiIndex. Parameters ---------- **dimensions : keyword arguments of the form new_name=(dim1, dim2, ...) Names of new dimensions, and the existing dimensions that they replace. Returns ------- stacked : Dataset Dataset with stacked data. See also -------- Dataset.unstack """ result = self for new_dim, dims in dimensions.items(): result = result._stack_once(dims, new_dim) return result def unstack(self, dim): """ Unstack an existing dimension corresponding to a MultiIndex into multiple new dimensions. New dimensions will be added at the end. Parameters ---------- dim : str Name of the existing dimension to unstack. Returns ------- unstacked : Dataset Dataset with unstacked data. See also -------- Dataset.stack """ if dim not in self.dims: raise ValueError('invalid dimension: %s' % dim) index = self.get_index(dim) if not isinstance(index, pd.MultiIndex): raise ValueError('cannot unstack a dimension that does not have ' 'a MultiIndex') full_idx = pd.MultiIndex.from_product(index.levels, names=index.names) obj = self.reindex(copy=False, **{dim: full_idx}) new_dim_names = index.names new_dim_sizes = [lev.size for lev in index.levels] variables = OrderedDict() for name, var in obj.variables.items(): if name != dim: if dim in var.dims: new_dims = OrderedDict(zip(new_dim_names, new_dim_sizes)) variables[name] = var.unstack(**{dim: new_dims}) else: variables[name] = var for name, lev in zip(new_dim_names, index.levels): variables[name] = IndexVariable(name, lev) coord_names = set(self._coord_names) - set([dim]) | set(new_dim_names) return self._replace_vars_and_dims(variables, coord_names) def update(self, other, inplace=True): """Update this dataset's variables with those from another dataset. Parameters ---------- other : Dataset or castable to Dataset Dataset or variables with which to update this dataset. inplace : bool, optional If True, merge the other dataset into this dataset in-place. Otherwise, return a new dataset object. Returns ------- updated : Dataset Updated dataset. Raises ------ ValueError If any dimensions would have inconsistent sizes in the updated dataset. """ variables, coord_names, dims = dataset_update_method(self, other) return self._replace_vars_and_dims(variables, coord_names, dims, inplace=inplace) def merge(self, other, inplace=False, overwrite_vars=frozenset(), compat='no_conflicts', join='outer'): """Merge the arrays of two datasets into a single dataset. This method generally not allow for overriding data, with the exception of attributes, which are ignored on the second dataset. Variables with the same name are checked for conflicts via the equals or identical methods. Parameters ---------- other : Dataset or castable to Dataset Dataset or variables to merge with this dataset. inplace : bool, optional If True, merge the other dataset into this dataset in-place. Otherwise, return a new dataset object. overwrite_vars : str or sequence, optional If provided, update variables of these name(s) without checking for conflicts in this dataset. compat : {'broadcast_equals', 'equals', 'identical', 'no_conflicts'}, optional String indicating how to compare variables of the same name for potential conflicts: - 'broadcast_equals': all values must be equal when variables are broadcast against each other to ensure common dimensions. - 'equals': all values and dimensions must be the same. - 'identical': all values, dimensions and attributes must be the same. - 'no_conflicts': only values which are not null in both datasets must be equal. The returned dataset then contains the combination of all non-null values. join : {'outer', 'inner', 'left', 'right'}, optional Method for joining ``self`` and ``other`` along shared dimensions: - 'outer': use the union of the indexes - 'inner': use the intersection of the indexes - 'left': use indexes from ``self`` - 'right': use indexes from ``other`` Returns ------- merged : Dataset Merged dataset. Raises ------ MergeError If any variables conflict (see ``compat``). """ variables, coord_names, dims = dataset_merge_method( self, other, overwrite_vars=overwrite_vars, compat=compat, join=join) return self._replace_vars_and_dims(variables, coord_names, dims, inplace=inplace) def _assert_all_in_dataset(self, names, virtual_okay=False): bad_names = set(names) - set(self._variables) if virtual_okay: bad_names -= self.virtual_variables if bad_names: raise ValueError('One or more of the specified variables ' 'cannot be found in this dataset') def drop(self, labels, dim=None): """Drop variables or index labels from this dataset. Parameters ---------- labels : scalar or list of scalars Name(s) of variables or index labels to drop. dim : None or str, optional Dimension along which to drop index labels. By default (if ``dim is None``), drops variables rather than index labels. Returns ------- dropped : Dataset """ if utils.is_scalar(labels): labels = [labels] if dim is None: return self._drop_vars(labels) else: try: index = self.indexes[dim] except KeyError: raise ValueError( 'dimension %r does not have coordinate labels' % dim) new_index = index.drop(labels) return self.loc[{dim: new_index}] def _drop_vars(self, names): self._assert_all_in_dataset(names) drop = set(names) variables = OrderedDict((k, v) for k, v in iteritems(self._variables) if k not in drop) coord_names = set(k for k in self._coord_names if k in variables) return self._replace_vars_and_dims(variables, coord_names) def transpose(self, *dims): """Return a new Dataset object with all array dimensions transposed. Although the order of dimensions on each array will change, the dataset dimensions themselves will remain in fixed (sorted) order. Parameters ---------- *dims : str, optional By default, reverse the dimensions on each array. Otherwise, reorder the dimensions to this order. Returns ------- transposed : Dataset Each array in the dataset (including) coordinates will be transposed to the given order. Notes ----- Although this operation returns a view of each array's data, it is not lazy -- the data will be fully loaded into memory. See Also -------- numpy.transpose DataArray.transpose """ if dims: if set(dims) ^ set(self.dims): raise ValueError('arguments to transpose (%s) must be ' 'permuted dataset dimensions (%s)' % (dims, tuple(self.dims))) ds = self.copy() for name, var in iteritems(self._variables): var_dims = tuple(dim for dim in dims if dim in var.dims) ds._variables[name] = var.transpose(*var_dims) return ds @property def T(self): return self.transpose() def dropna(self, dim, how='any', thresh=None, subset=None): """Returns a new dataset with dropped labels for missing values along the provided dimension. Parameters ---------- dim : str Dimension along which to drop missing values. Dropping along multiple dimensions simultaneously is not yet supported. how : {'any', 'all'}, optional * any : if any NA values are present, drop that label * all : if all values are NA, drop that label thresh : int, default None If supplied, require this many non-NA values. subset : sequence, optional Subset of variables to check for missing values. By default, all variables in the dataset are checked. Returns ------- Dataset """ # TODO: consider supporting multiple dimensions? Or not, given that # there are some ugly edge cases, e.g., pandas's dropna differs # depending on the order of the supplied axes. if dim not in self.dims: raise ValueError('%s must be a single dataset dimension' % dim) if subset is None: subset = list(self.data_vars) count = np.zeros(self.dims[dim], dtype=np.int64) size = 0 for k in subset: array = self._variables[k] if dim in array.dims: dims = [d for d in array.dims if d != dim] count += array.count(dims) size += np.prod([self.dims[d] for d in dims]) if thresh is not None: mask = count >= thresh elif how == 'any': mask = count == size elif how == 'all': mask = count > 0 elif how is not None: raise ValueError('invalid how option: %s' % how) else: raise TypeError('must specify how or thresh') return self.isel(**{dim: mask}) def fillna(self, value): """Fill missing values in this object. This operation follows the normal broadcasting and alignment rules that xarray uses for binary arithmetic, except the result is aligned to this object (``join='left'``) instead of aligned to the intersection of index coordinates (``join='inner'``). Parameters ---------- value : scalar, ndarray, DataArray, dict or Dataset Used to fill all matching missing values in this dataset's data variables. Scalars, ndarrays or DataArrays arguments are used to fill all data with aligned coordinates (for DataArrays). Dictionaries or datasets match data variables and then align coordinates if necessary. Returns ------- Dataset """ if utils.is_dict_like(value): value_keys = getattr(value, 'data_vars', value).keys() if not set(value_keys) <= set(self.data_vars.keys()): raise ValueError('all variables in the argument to `fillna` ' 'must be contained in the original dataset') out = ops.fillna(self, value) return out def combine_first(self, other): """Combine two Datasets, default to data_vars of self. The new coordinates follow the normal broadcasting and alignment rules of ``join='outer'``. Vacant cells in the expanded coordinates are filled with np.nan. Parameters ---------- other : DataArray Used to fill all matching missing values in this array. Returns ------- DataArray """ out = ops.fillna(self, other, join="outer", dataset_join="outer") return out def reduce(self, func, dim=None, keep_attrs=False, numeric_only=False, allow_lazy=False, **kwargs): """Reduce this dataset by applying `func` along some dimension(s). Parameters ---------- func : function Function which can be called in the form `f(x, axis=axis, **kwargs)` to return the result of reducing an np.ndarray over an integer valued axis. dim : str or sequence of str, optional Dimension(s) over which to apply `func`. By default `func` is applied over all dimensions. keep_attrs : bool, optional If True, the dataset's attributes (`attrs`) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes. numeric_only : bool, optional If True, only apply ``func`` to variables with a numeric dtype. **kwargs : dict Additional keyword arguments passed on to ``func``. Returns ------- reduced : Dataset Dataset with this object's DataArrays replaced with new DataArrays of summarized data and the indicated dimension(s) removed. """ if isinstance(dim, basestring): dims = set([dim]) elif dim is None: dims = set(self.dims) else: dims = set(dim) missing_dimensions = [dim for dim in dims if dim not in self.dims] if missing_dimensions: raise ValueError('Dataset does not contain the dimensions: %s' % missing_dimensions) variables = OrderedDict() for name, var in iteritems(self._variables): reduce_dims = [dim for dim in var.dims if dim in dims] if reduce_dims or not var.dims: if name not in self.coords: if (not numeric_only or np.issubdtype(var.dtype, np.number) or var.dtype == np.bool_): if len(reduce_dims) == 1: # unpack dimensions for the benefit of functions # like np.argmin which can't handle tuple arguments reduce_dims, = reduce_dims elif len(reduce_dims) == var.ndim: # prefer to aggregate over axis=None rather than # axis=(0, 1) if they will be equivalent, because # the former is often more efficient reduce_dims = None variables[name] = var.reduce(func, dim=reduce_dims, keep_attrs=keep_attrs, allow_lazy=allow_lazy, **kwargs) else: variables[name] = var coord_names = set(k for k in self.coords if k in variables) attrs = self.attrs if keep_attrs else None return self._replace_vars_and_dims(variables, coord_names, attrs=attrs) def apply(self, func, keep_attrs=False, args=(), **kwargs): """Apply a function over the data variables in this dataset. Parameters ---------- func : function Function which can be called in the form `f(x, **kwargs)` to transform each DataArray `x` in this dataset into another DataArray. keep_attrs : bool, optional If True, the dataset's attributes (`attrs`) will be copied from the original object to the new one. If False, the new object will be returned without attributes. args : tuple, optional Positional arguments passed on to `func`. **kwargs : dict Keyword arguments passed on to `func`. Returns ------- applied : Dataset Resulting dataset from applying ``func`` over each data variable. """ variables = OrderedDict( (k, maybe_wrap_array(v, func(v, *args, **kwargs))) for k, v in iteritems(self.data_vars)) attrs = self.attrs if keep_attrs else None return type(self)(variables, attrs=attrs) def assign(self, **kwargs): """Assign new data variables to a Dataset, returning a new object with all the original variables in addition to the new ones. Parameters ---------- kwargs : keyword, value pairs keywords are the variables names. If the values are callable, they are computed on the Dataset and assigned to new data variables. If the values are not callable, (e.g. a DataArray, scalar, or array), they are simply assigned. Returns ------- ds : Dataset A new Dataset with the new variables in addition to all the existing variables. Notes ----- Since ``kwargs`` is a dictionary, the order of your arguments may not be preserved, and so the order of the new variables is not well defined. Assigning multiple variables within the same ``assign`` is possible, but you cannot reference other variables created within the same ``assign`` call. See Also -------- pandas.DataFrame.assign """ data = self.copy() # do all calculations first... results = data._calc_assign_results(kwargs) # ... and then assign data.update(results) return data def to_array(self, dim='variable', name=None): """Convert this dataset into an xarray.DataArray The data variables of this dataset will be broadcast against each other and stacked along the first axis of the new array. All coordinates of this dataset will remain coordinates. Parameters ---------- dim : str, optional Name of the new dimension. name : str, optional Name of the new data array. Returns ------- array : xarray.DataArray """ from .dataarray import DataArray data_vars = [self.variables[k] for k in self.data_vars] broadcast_vars = broadcast_variables(*data_vars) data = ops.stack([b.data for b in broadcast_vars], axis=0) coords = dict(self.coords) coords[dim] = list(self.data_vars) dims = (dim,) + broadcast_vars[0].dims return DataArray(data, coords, dims, attrs=self.attrs, name=name) def _to_dataframe(self, ordered_dims): columns = [k for k in self if k not in self.dims] data = [self._variables[k].expand_dims(ordered_dims).values.reshape(-1) for k in columns] index = self.coords.to_index(ordered_dims) return pd.DataFrame(OrderedDict(zip(columns, data)), index=index) def to_dataframe(self): """Convert this dataset into a pandas.DataFrame. Non-index variables in this dataset form the columns of the DataFrame. The DataFrame is be indexed by the Cartesian product of this dataset's indices. """ return self._to_dataframe(self.dims) @classmethod def from_dataframe(cls, dataframe): """Convert a pandas.DataFrame into an xarray.Dataset Each column will be converted into an independent variable in the Dataset. If the dataframe's index is a MultiIndex, it will be expanded into a tensor product of one-dimensional indices (filling in missing values with NaN). This method will produce a Dataset very similar to that on which the 'to_dataframe' method was called, except with possibly redundant dimensions (since all dataset variables will have the same dimensionality). """ # TODO: Add an option to remove dimensions along which the variables # are constant, to enable consistent serialization to/from a dataframe, # even if some variables have different dimensionality. if not dataframe.columns.is_unique: raise ValueError( 'cannot convert DataFrame with non-unique columns') idx = dataframe.index obj = cls() if hasattr(idx, 'levels'): # it's a multi-index # expand the DataFrame to include the product of all levels full_idx = pd.MultiIndex.from_product(idx.levels, names=idx.names) dataframe = dataframe.reindex(full_idx) dims = [name if name is not None else 'level_%i' % n for n, name in enumerate(idx.names)] for dim, lev in zip(dims, idx.levels): obj[dim] = (dim, lev) shape = [lev.size for lev in idx.levels] else: dims = (idx.name if idx.name is not None else 'index',) obj[dims[0]] = (dims, idx) shape = -1 for name, series in iteritems(dataframe): data = np.asarray(series).reshape(shape) obj[name] = (dims, data) return obj def to_dict(self): """ Convert this dataset to a dictionary following xarray naming conventions. Converts all variables and attributes to native Python objects Useful for coverting to json. To avoid datetime incompatibility use decode_times=False kwarg in xarrray.open_dataset. See also -------- Dataset.from_dict """ d = {'coords': {}, 'attrs': decode_numpy_dict_values(self.attrs), 'dims': dict(self.dims), 'data_vars': {}} for k in self.coords: data = ensure_us_time_resolution(self[k].values).tolist() d['coords'].update({ k: {'data': data, 'dims': self[k].dims, 'attrs': decode_numpy_dict_values(self[k].attrs)}}) for k in self.data_vars: data = ensure_us_time_resolution(self[k].values).tolist() d['data_vars'].update({ k: {'data': data, 'dims': self[k].dims, 'attrs': decode_numpy_dict_values(self[k].attrs)}}) return d @classmethod def from_dict(cls, d): """ Convert a dictionary into an xarray.Dataset. Input dict can take several forms:: d = {'t': {'dims': ('t'), 'data': t}, 'a': {'dims': ('t'), 'data': x}, 'b': {'dims': ('t'), 'data': y}} d = {'coords': {'t': {'dims': 't', 'data': t, 'attrs': {'units':'s'}}}, 'attrs': {'title': 'air temperature'}, 'dims': 't', 'data_vars': {'a': {'dims': 't', 'data': x, }, 'b': {'dims': 't', 'data': y}}} where 't' is the name of the dimesion, 'a' and 'b' are names of data variables and t, x, and y are lists, numpy.arrays or pandas objects. Parameters ---------- d : dict, with a minimum structure of {'var_0': {'dims': [..], \ 'data': [..]}, \ ...} Returns ------- obj : xarray.Dataset See also -------- Dataset.to_dict DataArray.from_dict """ if not set(['coords', 'data_vars']).issubset(set(d)): variables = d.items() else: import itertools variables = itertools.chain(d.get('coords', {}).items(), d.get('data_vars', {}).items()) try: variable_dict = OrderedDict([(k, (v['dims'], v['data'], v.get('attrs'))) for k, v in variables]) except KeyError as e: raise ValueError( "cannot convert dict without the key " "'{dims_data}'".format(dims_data=str(e.args[0]))) obj = cls(variable_dict) # what if coords aren't dims? coords = set(d.get('coords', {})) - set(d.get('dims', {})) obj = obj.set_coords(coords) obj.attrs.update(d.get('attrs', {})) return obj @staticmethod def _unary_op(f, keep_attrs=False): @functools.wraps(f) def func(self, *args, **kwargs): ds = self.coords.to_dataset() for k in self.data_vars: ds._variables[k] = f(self._variables[k], *args, **kwargs) if keep_attrs: ds._attrs = self._attrs return ds return func @staticmethod def _binary_op(f, reflexive=False, join=None): @functools.wraps(f) def func(self, other): if isinstance(other, groupby.GroupBy): return NotImplemented align_type = OPTIONS['arithmetic_join'] if join is None else join if hasattr(other, 'indexes'): self, other = align(self, other, join=align_type, copy=False) g = f if not reflexive else lambda x, y: f(y, x) ds = self._calculate_binary_op(g, other, join=align_type) return ds return func @staticmethod def _inplace_binary_op(f): @functools.wraps(f) def func(self, other): if isinstance(other, groupby.GroupBy): raise TypeError('in-place operations between a Dataset and ' 'a grouped object are not permitted') # we don't actually modify arrays in-place with in-place Dataset # arithmetic -- this lets us automatically align things if hasattr(other, 'indexes'): other = other.reindex_like(self, copy=False) g = ops.inplace_to_noninplace_op(f) ds = self._calculate_binary_op(g, other, inplace=True) self._replace_vars_and_dims(ds._variables, ds._coord_names, attrs=ds._attrs, inplace=True) return self return func def _calculate_binary_op(self, f, other, join='inner', inplace=False): def apply_over_both(lhs_data_vars, rhs_data_vars, lhs_vars, rhs_vars): if inplace and set(lhs_data_vars) != set(rhs_data_vars): raise ValueError('datasets must have the same data variables ' 'for in-place arithmetic operations: %s, %s' % (list(lhs_data_vars), list(rhs_data_vars))) dest_vars = OrderedDict() for k in lhs_data_vars: if k in rhs_data_vars: dest_vars[k] = f(lhs_vars[k], rhs_vars[k]) elif join in ["left", "outer"]: dest_vars[k] = f(lhs_vars[k], np.nan) for k in rhs_data_vars: if k not in dest_vars and join in ["right", "outer"]: dest_vars[k] = f(rhs_vars[k], np.nan) return dest_vars if utils.is_dict_like(other) and not isinstance(other, Dataset): # can't use our shortcut of doing the binary operation with # Variable objects, so apply over our data vars instead. new_data_vars = apply_over_both(self.data_vars, other, self.data_vars, other) return Dataset(new_data_vars) other_coords = getattr(other, 'coords', None) ds = self.coords.merge(other_coords) if isinstance(other, Dataset): new_vars = apply_over_both(self.data_vars, other.data_vars, self.variables, other.variables) else: other_variable = getattr(other, 'variable', other) new_vars = OrderedDict((k, f(self.variables[k], other_variable)) for k in self.data_vars) ds._variables.update(new_vars) ds._dims = calculate_dimensions(ds._variables) return ds def _copy_attrs_from(self, other): self.attrs = other.attrs for v in other.variables: if v in self.variables: self.variables[v].attrs = other.variables[v].attrs def diff(self, dim, n=1, label='upper'): """Calculate the n-th order discrete difference along given axis. Parameters ---------- dim : str, optional Dimension over which to calculate the finite difference. n : int, optional The number of times values are differenced. label : str, optional The new coordinate in dimension ``dim`` will have the values of either the minuend's or subtrahend's coordinate for values 'upper' and 'lower', respectively. Other values are not supported. Returns ------- difference : same type as caller The n-th order finite difference of this object. Examples -------- >>> ds = xr.Dataset({'foo': ('x', [5, 5, 6, 6])}) >>> ds.diff('x') Dimensions: (x: 3) Coordinates: * x (x) int64 1 2 3 Data variables: foo (x) int64 0 1 0 >>> ds.diff('x', 2) Dimensions: (x: 2) Coordinates: * x (x) int64 2 3 Data variables: foo (x) int64 1 -1 """ if n == 0: return self if n < 0: raise ValueError('order `n` must be non-negative but got {0}' ''.format(n)) # prepare slices kwargs_start = {dim: slice(None, -1)} kwargs_end = {dim: slice(1, None)} # prepare new coordinate if label == 'upper': kwargs_new = kwargs_end elif label == 'lower': kwargs_new = kwargs_start else: raise ValueError('The \'label\' argument has to be either ' '\'upper\' or \'lower\'') variables = OrderedDict() for name, var in iteritems(self.variables): if dim in var.dims: if name in self.data_vars: variables[name] = (var.isel(**kwargs_end) - var.isel(**kwargs_start)) else: variables[name] = var.isel(**kwargs_new) else: variables[name] = var difference = self._replace_vars_and_dims(variables) if n > 1: return difference.diff(dim, n - 1) else: return difference def shift(self, **shifts): """Shift this dataset by an offset along one or more dimensions. Only data variables are moved; coordinates stay in place. This is consistent with the behavior of ``shift`` in pandas. Parameters ---------- **shifts : keyword arguments of the form {dim: offset} Integer offset to shift along each of the given dimensions. Positive offsets shift to the right; negative offsets shift to the left. Returns ------- shifted : Dataset Dataset with the same coordinates and attributes but shifted data variables. See also -------- roll Examples -------- >>> ds = xr.Dataset({'foo': ('x', list('abcde'))}) >>> ds.shift(x=2) Dimensions: (x: 5) Coordinates: * x (x) int64 0 1 2 3 4 Data variables: foo (x) object nan nan 'a' 'b' 'c' """ invalid = [k for k in shifts if k not in self.dims] if invalid: raise ValueError("dimensions %r do not exist" % invalid) variables = OrderedDict() for name, var in iteritems(self.variables): if name in self.data_vars: var_shifts = dict((k, v) for k, v in shifts.items() if k in var.dims) variables[name] = var.shift(**var_shifts) else: variables[name] = var return self._replace_vars_and_dims(variables) def roll(self, **shifts): """Roll this dataset by an offset along one or more dimensions. Unlike shift, roll rotates all variables, including coordinates. The direction of rotation is consistent with :py:func:`numpy.roll`. Parameters ---------- **shifts : keyword arguments of the form {dim: offset} Integer offset to rotate each of the given dimensions. Positive offsets roll to the right; negative offsets roll to the left. Returns ------- rolled : Dataset Dataset with the same coordinates and attributes but rolled variables. See also -------- shift Examples -------- >>> ds = xr.Dataset({'foo': ('x', list('abcde'))}) >>> ds.roll(x=2) Dimensions: (x: 5) Coordinates: * x (x) int64 3 4 0 1 2 Data variables: foo (x) object 'd' 'e' 'a' 'b' 'c' """ invalid = [k for k in shifts if k not in self.dims] if invalid: raise ValueError("dimensions %r do not exist" % invalid) variables = OrderedDict() for name, var in iteritems(self.variables): var_shifts = dict((k, v) for k, v in shifts.items() if k in var.dims) variables[name] = var.roll(**var_shifts) return self._replace_vars_and_dims(variables) def quantile(self, q, dim=None, interpolation='linear', numeric_only=False, keep_attrs=False): """Compute the qth quantile of the data along the specified dimension. Returns the qth quantiles(s) of the array elements for each variable in the Dataset. Parameters ---------- q : float in range of [0,1] (or sequence of floats) Quantile to compute, which must be between 0 and 1 inclusive. dim : str or sequence of str, optional Dimension(s) over which to apply quantile. interpolation : {'linear', 'lower', 'higher', 'midpoint', 'nearest'} This optional parameter specifies the interpolation method to use when the desired quantile lies between two data points ``i < j``: * linear: ``i + (j - i) * fraction``, where ``fraction`` is the fractional part of the index surrounded by ``i`` and ``j``. * lower: ``i``. * higher: ``j``. * nearest: ``i`` or ``j``, whichever is nearest. * midpoint: ``(i + j) / 2``. keep_attrs : bool, optional If True, the dataset's attributes (`attrs`) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes. numeric_only : bool, optional If True, only apply ``func`` to variables with a numeric dtype. Returns ------- quantiles : Dataset If `q` is a single quantile, then the result is a scalar for each variable in data_vars. If multiple percentiles are given, first axis of the result corresponds to the quantile and a quantile dimension is added to the return Dataset. The other dimensions are the dimensions that remain after the reduction of the array. See Also -------- numpy.nanpercentile, pandas.Series.quantile, DataArray.quantile """ if isinstance(dim, basestring): dims = set([dim]) elif dim is None: dims = set(self.dims) else: dims = set(dim) _assert_empty([dim for dim in dims if dim not in self.dims], 'Dataset does not contain the dimensions: %s') q = np.asarray(q, dtype=np.float64) variables = OrderedDict() for name, var in iteritems(self.variables): reduce_dims = [dim for dim in var.dims if dim in dims] if reduce_dims or not var.dims: if name not in self.coords: if (not numeric_only or np.issubdtype(var.dtype, np.number) or var.dtype == np.bool_): if len(reduce_dims) == var.ndim: # prefer to aggregate over axis=None rather than # axis=(0, 1) if they will be equivalent, because # the former is often more efficient reduce_dims = None variables[name] = var.quantile( q, dim=reduce_dims, interpolation=interpolation) else: variables[name] = var # construct the new dataset coord_names = set(k for k in self.coords if k in variables) attrs = self.attrs if keep_attrs else None new = self._replace_vars_and_dims(variables, coord_names, attrs=attrs) if 'quantile' in new.dims: new.coords['quantile'] = Variable('quantile', q) else: new.coords['quantile'] = q return new @property def real(self): return self._unary_op(lambda x: x.real, keep_attrs=True)(self) @property def imag(self): return self._unary_op(lambda x: x.imag, keep_attrs=True)(self) def filter_by_attrs(self, **kwargs): """Returns a ``Dataset`` with variables that match specific conditions. Can pass in ``key=value`` or ``key=callable``. Variables are returned that contain all of the matches or callable returns True. If using a callable note that it should accept a single parameter only, the attribute value. Parameters ---------- **kwargs : key=value key : str Attribute name. value : callable or obj If value is a callable, it should return a boolean in the form of bool = func(attr) where attr is da.attrs[key]. Otherwise, value will be compared to the each DataArray's attrs[key]. Returns ------- new : Dataset New dataset with variables filtered by attribute. Examples -------- >>> # Create an example dataset: >>> import numpy as np >>> import pandas as pd >>> import xarray as xr >>> temp = 15 + 8 * np.random.randn(2, 2, 3) >>> precip = 10 * np.random.rand(2, 2, 3) >>> lon = [[-99.83, -99.32], [-99.79, -99.23]] >>> lat = [[42.25, 42.21], [42.63, 42.59]] >>> dims = ['x', 'y', 'time'] >>> temp_attr = dict(standard_name='air_potential_temperature') >>> precip_attr = dict(standard_name='convective_precipitation_flux') >>> ds = xr.Dataset({ ... 'temperature': (dims, temp, temp_attr), ... 'precipitation': (dims, precip, precip_attr)}, ... coords={ ... 'lon': (['x', 'y'], lon), ... 'lat': (['x', 'y'], lat), ... 'time': pd.date_range('2014-09-06', periods=3), ... 'reference_time': pd.Timestamp('2014-09-05')}) >>> # Get variables matching a specific standard_name. >>> ds.filter_by_attrs(standard_name='convective_precipitation_flux') Dimensions: (time: 3, x: 2, y: 2) Coordinates: * x (x) int64 0 1 * time (time) datetime64[ns] 2014-09-06 2014-09-07 2014-09-08 lat (x, y) float64 42.25 42.21 42.63 42.59 * y (y) int64 0 1 reference_time datetime64[ns] 2014-09-05 lon (x, y) float64 -99.83 -99.32 -99.79 -99.23 Data variables: precipitation (x, y, time) float64 4.178 2.307 6.041 6.046 0.06648 ... >>> # Get all variables that have a standard_name attribute. >>> standard_name = lambda v: v is not None >>> ds.filter_by_attrs(standard_name=standard_name) Dimensions: (time: 3, x: 2, y: 2) Coordinates: lon (x, y) float64 -99.83 -99.32 -99.79 -99.23 lat (x, y) float64 42.25 42.21 42.63 42.59 * x (x) int64 0 1 * y (y) int64 0 1 * time (time) datetime64[ns] 2014-09-06 2014-09-07 2014-09-08 reference_time datetime64[ns] 2014-09-05 Data variables: temperature (x, y, time) float64 25.86 20.82 6.954 23.13 10.25 11.68 ... precipitation (x, y, time) float64 5.702 0.9422 2.075 1.178 3.284 ... """ selection = [] for var_name, variable in self.data_vars.items(): for attr_name, pattern in kwargs.items(): attr_value = variable.attrs.get(attr_name) if ((callable(pattern) and pattern(attr_value)) or attr_value == pattern): selection.append(var_name) return self[selection] ops.inject_all_ops_and_reduce_methods(Dataset, array_only=False) ������������������������������������������������������xarray-0.9.1/xarray/core/extensions.py��������������������������������������������������������������0000644�1311115�0011610�00000007174�13043511243�020143� 0����������������������������������������������������������������������������������������������������ustar �shoyer��������������������������eng�����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������from __future__ import absolute_import from __future__ import division from __future__ import print_function import traceback import warnings from .dataarray import DataArray from .dataset import Dataset from .pycompat import PY2 class AccessorRegistrationWarning(Warning): """Warning for conflicts in accessor registration.""" class _CachedAccessor(object): """Custom property-like object (descriptor) for caching accessors.""" def __init__(self, name, accessor): self._name = name self._accessor = accessor def __get__(self, obj, cls): if obj is None: # we're accessing the attribute of the class, i.e., Dataset.geo return self._accessor try: accessor_obj = self._accessor(obj) except AttributeError: # __getattr__ on data object will swallow any AttributeErrors raised # when initializing the accessor, so we need to raise as something # else (GH933): msg = 'error initializing %r accessor.' % self._name if PY2: msg += ' Full traceback:\n' + traceback.format_exc() raise RuntimeError(msg) # Replace the property with the accessor object. Inspired by: # http://www.pydanny.com/cached-property.html # We need to use object.__setattr__ because we overwrite __setattr__ on # AttrAccessMixin. object.__setattr__(obj, self._name, accessor_obj) return accessor_obj def _register_accessor(name, cls): def decorator(accessor): if hasattr(cls, name): warnings.warn( 'registration of accessor %r under name %r for type %r is ' 'overriding a preexisting attribute with the same name.' % (accessor, name, cls), AccessorRegistrationWarning, stacklevel=2) setattr(cls, name, _CachedAccessor(name, accessor)) return accessor return decorator def register_dataarray_accessor(name): """Register a custom accessor on xarray.DataArray objects. Parameters ---------- name : str Name under which the accessor should be registered. A warning is issued if this name conflicts with a preexisting attribute. Examples -------- In your library code:: import xarray as xr @xr.register_dataset_accessor('geo') class GeoAccessor(object): def __init__(self, xarray_obj): self._obj = xarray_obj @property def center(self): # return the geographic center point of this dataset lon = self._obj.latitude lat = self._obj.longitude return (float(lon.mean()), float(lat.mean())) def plot(self): # plot this array's data on a map, e.g., using Cartopy pass Back in an interactive IPython session: >>> ds = xarray.Dataset({'longitude': np.linspace(0, 10), ... 'latitude': np.linspace(0, 20)}) >>> ds.geo.center (5.0, 10.0) >>> ds.geo.plot() # plots data on a map See also -------- register_dataset_accessor """ return _register_accessor(name, DataArray) def register_dataset_accessor(name): """Register a custom property on xarray.Dataset objects. Parameters ---------- name : str Name under which the accessor should be registered. A warning is issued if this name conflicts with a preexisting attribute. See also -------- register_dataarray_accessor """ return _register_accessor(name, Dataset) ����������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������xarray-0.9.1/xarray/core/formatting.py��������������������������������������������������������������0000644�1311115�0011610�00000032106�13043675344�020124� 0����������������������������������������������������������������������������������������������������ustar �shoyer��������������������������eng�����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������"""String formatting routines for __repr__. For the sake of sanity, we only do internal formatting with unicode, which can be returned by the __unicode__ special method. We use ReprMixin to provide the __repr__ method so that things can work on Python 2. """ from __future__ import absolute_import from __future__ import division from __future__ import print_function import contextlib from datetime import datetime, timedelta import functools import numpy as np import pandas as pd from pandas.tslib import OutOfBoundsDatetime from .options import OPTIONS from .pycompat import ( PY2, unicode_type, bytes_type, dask_array_type, OrderedDict, basestring) def pretty_print(x, numchars): """Given an object `x`, call `str(x)` and format the returned string so that it is numchars long, padding with trailing spaces or truncating with ellipses as necessary """ s = maybe_truncate(x, numchars) return s + ' ' * max(numchars - len(s), 0) def maybe_truncate(obj, maxlen=500): s = unicode_type(obj) if len(s) > maxlen: s = s[:(maxlen - 3)] + u'...' return s def wrap_indent(text, start='', length=None): if length is None: length = len(start) indent = '\n' + ' ' * length return start + indent.join(x for x in text.splitlines()) def ensure_valid_repr(string): """Ensure that the given value is valid for the result of __repr__. On Python 2, this means we need to convert unicode to bytes. We won't need this function once we drop Python 2.7 support. """ if PY2 and isinstance(string, unicode): string = string.encode('utf-8') return string class ReprMixin(object): """Mixin that defines __repr__ for a class that already has __unicode__.""" def __repr__(self): return ensure_valid_repr(self.__unicode__()) def _get_indexer_at_least_n_items(shape, n_desired): assert 0 < n_desired <= np.prod(shape) cum_items = np.cumprod(shape[::-1]) n_steps = np.argmax(cum_items >= n_desired) stop = int(np.ceil(float(n_desired) / np.r_[1, cum_items][n_steps])) indexer = ((0, ) * (len(shape) - 1 - n_steps) + (slice(stop), ) + (slice(None), ) * n_steps) return indexer def first_n_items(x, n_desired): """Returns the first n_desired items of an array""" # Unfortunately, we can't just do x.flat[:n_desired] here because x might # not be a numpy.ndarray. Moreover, access to elements of x could be very # expensive (e.g. if it's only available over DAP), so go out of our way to # get them in a single call to __getitem__ using only slices. if n_desired < 1: raise ValueError('must request at least one item') if x.size == 0: # work around for https://github.com/numpy/numpy/issues/5195 return [] if n_desired < x.size: indexer = _get_indexer_at_least_n_items(x.shape, n_desired) x = x[indexer] return np.asarray(x).flat[:n_desired] def last_item(x): """Returns the last item of an array in a list or an empty list.""" if x.size == 0: # work around for https://github.com/numpy/numpy/issues/5195 return [] indexer = (slice(-1, None),) * x.ndim return np.ravel(x[indexer]).tolist() def format_timestamp(t): """Cast given object to a Timestamp and return a nicely formatted string""" # Timestamp is only valid for 1678 to 2262 try: datetime_str = unicode_type(pd.Timestamp(t)) except OutOfBoundsDatetime: datetime_str = unicode_type(t) try: date_str, time_str = datetime_str.split() except ValueError: # catch NaT and others that don't split nicely return datetime_str else: if time_str == '00:00:00': return date_str else: return '%sT%s' % (date_str, time_str) def format_timedelta(t, timedelta_format=None): """Cast given object to a Timestamp and return a nicely formatted string""" timedelta_str = unicode_type(pd.Timedelta(t)) try: days_str, time_str = timedelta_str.split(' days ') except ValueError: # catch NaT and others that don't split nicely return timedelta_str else: if timedelta_format == 'date': return days_str + ' days' elif timedelta_format == 'time': return time_str else: return timedelta_str def format_item(x, timedelta_format=None, quote_strings=True): """Returns a succinct summary of an object as a string""" if isinstance(x, (np.datetime64, datetime)): return format_timestamp(x) if isinstance(x, (np.timedelta64, timedelta)): return format_timedelta(x, timedelta_format=timedelta_format) elif isinstance(x, (unicode_type, bytes_type)): return repr(x) if quote_strings else x elif isinstance(x, (float, np.float)): return u'{0:.4}'.format(x) else: return unicode_type(x) def format_items(x): """Returns a succinct summaries of all items in a sequence as strings""" x = np.asarray(x) timedelta_format = 'datetime' if np.issubdtype(x.dtype, np.timedelta64): x = np.asarray(x, dtype='timedelta64[ns]') day_part = (x[~pd.isnull(x)] .astype('timedelta64[D]') .astype('timedelta64[ns]')) time_needed = x != day_part day_needed = day_part != np.timedelta64(0, 'ns') if np.logical_not(day_needed).all(): timedelta_format = 'time' elif np.logical_not(time_needed).all(): timedelta_format = 'date' formatted = [format_item(xi, timedelta_format) for xi in x] return formatted def format_array_flat(items_ndarray, max_width): """Return a formatted string for as many items in the flattened version of items_ndarray that will fit within max_width characters """ # every item will take up at least two characters, but we always want to # print at least one item max_possibly_relevant = max(int(np.ceil(max_width / 2.0)), 1) relevant_items = first_n_items(items_ndarray, max_possibly_relevant) pprint_items = format_items(relevant_items) cum_len = np.cumsum([len(s) + 1 for s in pprint_items]) - 1 if (max_possibly_relevant < items_ndarray.size or (cum_len > max_width).any()): end_padding = u' ...' count = max(np.argmax((cum_len + len(end_padding)) > max_width), 1) pprint_items = pprint_items[:count] else: end_padding = u'' pprint_str = u' '.join(pprint_items) + end_padding return pprint_str def _summarize_var_or_coord(name, var, col_width, show_values=True, marker=' ', max_width=None): if max_width is None: max_width = OPTIONS['display_width'] first_col = pretty_print(u' %s %s ' % (marker, name), col_width) if var.dims: dims_str = u'(%s) ' % u', '.join(map(unicode_type, var.dims)) else: dims_str = u'' front_str = u'%s%s%s ' % (first_col, dims_str, var.dtype) if show_values: values_str = format_array_flat(var, max_width - len(front_str)) else: values_str = u'...' return front_str + values_str def _summarize_coord_multiindex(coord, col_width, marker): first_col = pretty_print(u' %s %s ' % (marker, coord.name), col_width) return u'%s(%s) MultiIndex' % (first_col, unicode_type(coord.dims[0])) def _summarize_coord_levels(coord, col_width, marker=u'-'): relevant_coord = coord[:30] return u'\n'.join( [_summarize_var_or_coord(lname, relevant_coord.get_level_variable(lname), col_width, marker=marker) for lname in coord.level_names]) def _not_remote(var): """Helper function to identify if array is positively identifiable as coming from a remote source. """ source = var.encoding.get('source') if source and source.startswith('http') and not var._in_memory: return False return True def summarize_var(name, var, col_width): show_values = _not_remote(var) return _summarize_var_or_coord(name, var, col_width, show_values) def summarize_coord(name, var, col_width): is_index = name in var.dims show_values = is_index or _not_remote(var) marker = u'*' if is_index else u' ' if is_index: coord = var.variable.to_index_variable() if coord.level_names is not None: return u'\n'.join( [_summarize_coord_multiindex(coord, col_width, marker), _summarize_coord_levels(coord, col_width)]) return _summarize_var_or_coord(name, var, col_width, show_values, marker) def summarize_attr(key, value, col_width=None): # ignore col_width for now to more clearly distinguish attributes return u' %s: %s' % (key, maybe_truncate(value)) EMPTY_REPR = u' *empty*' def _get_col_items(mapping): """Get all column items to format, including both keys of `mapping` and MultiIndex levels if any. """ from .variable import IndexVariable col_items = [] for k, v in mapping.items(): col_items.append(k) var = getattr(v, 'variable', v) if isinstance(var, IndexVariable): level_names = var.to_index_variable().level_names if level_names is not None: col_items += list(level_names) return col_items def _calculate_col_width(col_items): max_name_length = (max(len(unicode_type(s)) for s in col_items) if col_items else 0) col_width = max(max_name_length, 7) + 6 return col_width def _mapping_repr(mapping, title, summarizer, col_width=None): if col_width is None: col_width = _calculate_col_width(mapping) summary = [u'%s:' % title] if mapping: summary += [summarizer(k, v, col_width) for k, v in mapping.items()] else: summary += [EMPTY_REPR] return u'\n'.join(summary) data_vars_repr = functools.partial(_mapping_repr, title=u'Data variables', summarizer=summarize_var) attrs_repr = functools.partial(_mapping_repr, title=u'Attributes', summarizer=summarize_attr) def coords_repr(coords, col_width=None): if col_width is None: col_width = _calculate_col_width(_get_col_items(coords)) return _mapping_repr(coords, title=u'Coordinates', summarizer=summarize_coord, col_width=col_width) def indexes_repr(indexes): summary = [] for k, v in indexes.items(): summary.append(wrap_indent(repr(v), '%s: ' % k)) return u'\n'.join(summary) def dim_summary(obj): elements = [u'%s: %s' % (k, v) for k, v in obj.sizes.items()] return u', '.join(elements) def unindexed_dims_repr(dims, coords): unindexed_dims = [d for d in dims if d not in coords] if unindexed_dims: dims_str = u', '.join(u'%s' % d for d in unindexed_dims) return u'Dimensions without coordinates: ' + dims_str else: return None @contextlib.contextmanager def set_numpy_options(*args, **kwargs): original = np.get_printoptions() np.set_printoptions(*args, **kwargs) yield np.set_printoptions(**original) def short_array_repr(array): array = np.asarray(array) # default to lower precision so a full (abbreviated) line can fit on # one line with the default display_width options = { 'precision': 6, 'linewidth': OPTIONS['display_width'], 'threshold': 200, } if array.ndim < 3: edgeitems = 3 elif array.ndim == 3: edgeitems = 2 else: edgeitems = 1 options['edgeitems'] = edgeitems with set_numpy_options(**options): return repr(array) def array_repr(arr): # used for DataArray, Variable and IndexVariable if hasattr(arr, 'name') and arr.name is not None: name_str = '%r ' % arr.name else: name_str = u'' summary = [u'' % (type(arr).__name__, name_str, dim_summary(arr))] if isinstance(getattr(arr, 'variable', arr)._data, dask_array_type): summary.append(repr(arr.data)) elif arr._in_memory or arr.size < 1e5: summary.append(short_array_repr(arr.values)) else: summary.append(u'[%s values with dtype=%s]' % (arr.size, arr.dtype)) if hasattr(arr, 'coords'): if arr.coords: summary.append(repr(arr.coords)) unindexed_dims_str = unindexed_dims_repr(arr.dims, arr.coords) if unindexed_dims_str: summary.append(unindexed_dims_str) if arr.attrs: summary.append(attrs_repr(arr.attrs)) return u'\n'.join(summary) def dataset_repr(ds): summary = [u'' % type(ds).__name__] col_width = _calculate_col_width(_get_col_items(ds)) dims_start = pretty_print(u'Dimensions:', col_width) summary.append(u'%s(%s)' % (dims_start, dim_summary(ds))) if ds.coords: summary.append(coords_repr(ds.coords, col_width=col_width)) unindexed_dims_str = unindexed_dims_repr(ds.dims, ds.coords) if unindexed_dims_str: summary.append(unindexed_dims_str) summary.append(data_vars_repr(ds.data_vars, col_width=col_width)) if ds.attrs: summary.append(attrs_repr(ds.attrs)) return u'\n'.join(summary) ����������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������xarray-0.9.1/xarray/core/groupby.py�����������������������������������������������������������������0000644�1311115�0011610�00000061114�13043543222�017427� 0����������������������������������������������������������������������������������������������������ustar �shoyer��������������������������eng�����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������from __future__ import absolute_import from __future__ import division from __future__ import print_function import functools import numpy as np import pandas as pd from . import nputils from . import ops from .combine import concat from .common import ( ImplementsArrayReduce, ImplementsDatasetReduce, _maybe_promote, ) from .pycompat import range, zip from .utils import hashable, peek_at, maybe_wrap_array, safe_cast_to_index from .variable import as_variable, Variable, IndexVariable def unique_value_groups(ar, sort=True): """Group an array by its unique values. Parameters ---------- ar : array-like Input array. This will be flattened if it is not already 1-D. sort : boolean, optional Whether or not to sort unique values. Returns ------- values : np.ndarray Sorted, unique values as returned by `np.unique`. indices : list of lists of int Each element provides the integer indices in `ar` with values given by the corresponding value in `unique_values`. """ inverse, values = pd.factorize(ar, sort=sort) groups = [[] for _ in range(len(values))] for n, g in enumerate(inverse): if g >= 0: # pandas uses -1 to mark NaN, but doesn't include them in values groups[g].append(n) return values, groups def _get_fill_value(dtype): """Return a fill value that appropriately promotes types when used with np.concatenate """ dtype, fill_value = _maybe_promote(dtype) return fill_value def _dummy_copy(xarray_obj): from .dataset import Dataset from .dataarray import DataArray if isinstance(xarray_obj, Dataset): res = Dataset(dict((k, _get_fill_value(v.dtype)) for k, v in xarray_obj.data_vars.items()), dict((k, _get_fill_value(v.dtype)) for k, v in xarray_obj.coords.items() if k not in xarray_obj.dims), xarray_obj.attrs) elif isinstance(xarray_obj, DataArray): res = DataArray(_get_fill_value(xarray_obj.dtype), dict((k, _get_fill_value(v.dtype)) for k, v in xarray_obj.coords.items() if k not in xarray_obj.dims), dims=[], name=xarray_obj.name, attrs=xarray_obj.attrs) else: # pragma: no cover raise AssertionError return res def _is_one_or_none(obj): return obj == 1 or obj is None def _consolidate_slices(slices): """Consolidate adjacent slices in a list of slices. """ result = [] for slice_ in slices: if not isinstance(slice_, slice): raise ValueError('list element is not a slice: %r' % slice_) if (result and last_slice.stop == slice_.start and _is_one_or_none(last_slice.step) and _is_one_or_none(slice_.step)): last_slice = slice(last_slice.start, slice_.stop, slice_.step) result[-1] = last_slice else: result.append(slice_) last_slice = slice_ return result def _inverse_permutation_indices(positions): """Like inverse_permutation, but also handles slices. Parameters ---------- positions : list of np.ndarray or slice objects. If slice objects, all are assumed to be slices. Returns ------- np.ndarray of indices or None, if no permutation is necessary. """ if not positions: return None if isinstance(positions[0], slice): positions = _consolidate_slices(positions) if positions == slice(None): return None positions = [np.arange(sl.start, sl.stop, sl.step) for sl in positions] indices = nputils.inverse_permutation(np.concatenate(positions)) return indices class _DummyGroup(object): """Class for keeping track of grouped dimensions without coordinates. Should not be user visible. """ def __init__(self, obj, name, coords): self.name = name self.coords = coords self.dims = (name,) self.ndim = 1 self.size = obj.sizes[name] self.values = range(self.size) def _ensure_1d(group, obj): if group.ndim != 1: # try to stack the dims of the group into a single dim orig_dims = group.dims stacked_dim = 'stacked_' + '_'.join(orig_dims) # these dimensions get created by the stack operation inserted_dims = [dim for dim in group.dims if dim not in group.coords] # the copy is necessary here, otherwise read only array raises error # in pandas: https://github.com/pydata/pandas/issues/12813 group = group.stack(**{stacked_dim: orig_dims}).copy() obj = obj.stack(**{stacked_dim: orig_dims}) else: stacked_dim = None inserted_dims = [] return group, obj, stacked_dim, inserted_dims def _unique_and_monotonic(group): if isinstance(group, _DummyGroup): return True else: index = safe_cast_to_index(group) return index.is_unique and index.is_monotonic class GroupBy(object): """A object that implements the split-apply-combine pattern. Modeled after `pandas.GroupBy`. The `GroupBy` object can be iterated over (unique_value, grouped_array) pairs, but the main way to interact with a groupby object are with the `apply` or `reduce` methods. You can also directly call numpy methods like `mean` or `std`. You should create a GroupBy object by using the `DataArray.groupby` or `Dataset.groupby` methods. See Also -------- Dataset.groupby DataArray.groupby """ def __init__(self, obj, group, squeeze=False, grouper=None, bins=None, cut_kwargs={}): """Create a GroupBy object Parameters ---------- obj : Dataset or DataArray Object to group. group : DataArray Array with the group values. squeeze : boolean, optional If "group" is a coordinate of object, `squeeze` controls whether the subarrays have a dimension of length 1 along that coordinate or if the dimension is squeezed out. grouper : pd.Grouper, optional Used for grouping values along the `group` array. bins : array-like, optional If `bins` is specified, the groups will be discretized into the specified bins by `pandas.cut`. cut_kwargs : dict, optional Extra keyword arguments to pass to `pandas.cut` """ from .dataarray import DataArray if grouper is not None and bins is not None: raise TypeError("can't specify both `grouper` and `bins`") if not isinstance(group, (DataArray, IndexVariable)): if not hashable(group): raise TypeError('`group` must be an xarray.DataArray or the ' 'name of an xarray variable or dimension') group = obj[group] if group.name not in obj and group.name in obj.dims: # DummyGroups should not appear on groupby results group = _DummyGroup(obj, group.name, group.coords) if getattr(group, 'name', None) is None: raise ValueError('`group` must have a name') group, obj, stacked_dim, inserted_dims = _ensure_1d(group, obj) group_dim, = group.dims expected_size = obj.sizes[group_dim] if group.size != expected_size: raise ValueError('the group variable\'s length does not ' 'match the length of this variable along its ' 'dimension') full_index = None if bins is not None: binned = pd.cut(group.values, bins, **cut_kwargs) new_dim_name = group.name + '_bins' group = DataArray(binned, group.coords, name=new_dim_name) full_index = binned.categories if grouper is not None: index = safe_cast_to_index(group) if not index.is_monotonic: # TODO: sort instead of raising an error raise ValueError('index must be monotonic for resampling') s = pd.Series(np.arange(index.size), index) first_items = s.groupby(grouper).first() if first_items.isnull().any(): full_index = first_items.index first_items = first_items.dropna() sbins = first_items.values.astype(np.int64) group_indices = ([slice(i, j) for i, j in zip(sbins[:-1], sbins[1:])] + [slice(sbins[-1], None)]) unique_coord = IndexVariable(group.name, first_items.index) elif group.dims == (group.name,) and _unique_and_monotonic(group): # no need to factorize group_indices = np.arange(group.size) if not squeeze: # use slices to do views instead of fancy indexing # equivalent to: group_indices = group_indices.reshape(-1, 1) group_indices = [slice(i, i + 1) for i in group_indices] unique_coord = group else: # look through group to find the unique values unique_values, group_indices = unique_value_groups( safe_cast_to_index(group), sort=(bins is None)) unique_coord = IndexVariable(group.name, unique_values) # specification for the groupby operation self._obj = obj self._group = group self._group_dim = group_dim self._group_indices = group_indices self._unique_coord = unique_coord self._stacked_dim = stacked_dim self._inserted_dims = inserted_dims self._full_index = full_index # cached attributes self._groups = None @property def groups(self): # provided to mimic pandas.groupby if self._groups is None: self._groups = dict(zip(self._unique_coord.values, self._group_indices)) return self._groups def __len__(self): return self._unique_coord.size def __iter__(self): return zip(self._unique_coord.values, self._iter_grouped()) def _iter_grouped(self): """Iterate over each element in this group""" for indices in self._group_indices: yield self._obj.isel(**{self._group_dim: indices}) def _infer_concat_args(self, applied_example): if self._group_dim in applied_example.dims: coord = self._group positions = self._group_indices else: coord = self._unique_coord positions = None dim, = coord.dims if isinstance(coord, _DummyGroup): coord = None return coord, dim, positions @staticmethod def _binary_op(f, reflexive=False, **ignored_kwargs): @functools.wraps(f) def func(self, other): g = f if not reflexive else lambda x, y: f(y, x) applied = self._yield_binary_applied(g, other) combined = self._combine(applied) return combined return func def _yield_binary_applied(self, func, other): dummy = None for group_value, obj in self: try: other_sel = other.sel(**{self._group.name: group_value}) except AttributeError: raise TypeError('GroupBy objects only support binary ops ' 'when the other argument is a Dataset or ' 'DataArray') except (KeyError, ValueError): if self._group.name not in other.dims: raise ValueError('incompatible dimensions for a grouped ' 'binary operation: the group variable %r ' 'is not a dimension on the other argument' % self._group.name) if dummy is None: dummy = _dummy_copy(other) other_sel = dummy result = func(obj, other_sel) yield result def _maybe_restore_empty_groups(self, combined): """Our index contained empty groups (e.g., from a resampling). If we reduced on that dimension, we want to restore the full index. """ if (self._full_index is not None and self._group.name in combined.dims): indexers = {self._group.name: self._full_index} combined = combined.reindex(**indexers) return combined def _maybe_unstack(self, obj): """This gets called if we are applying on an array with a multidimensional group.""" if self._stacked_dim is not None and self._stacked_dim in obj.dims: obj = obj.unstack(self._stacked_dim) for dim in self._inserted_dims: if dim in obj.coords: del obj.coords[dim] return obj def fillna(self, value): """Fill missing values in this object by group. This operation follows the normal broadcasting and alignment rules that xarray uses for binary arithmetic, except the result is aligned to this object (``join='left'``) instead of aligned to the intersection of index coordinates (``join='inner'``). Parameters ---------- value : valid type for the grouped object's fillna method Used to fill all matching missing values by group. Returns ------- same type as the grouped object See also -------- Dataset.fillna DataArray.fillna """ out = ops.fillna(self, value) return out def where(self, cond): """Return an object of the same shape with all entries where cond is True and all other entries masked. This operation follows the normal broadcasting and alignment rules that xarray uses for binary arithmetic. Parameters ---------- cond : DataArray or Dataset Returns ------- same type as the grouped object See also -------- Dataset.where """ return self._where(cond) def _first_or_last(self, op, skipna, keep_attrs): if isinstance(self._group_indices[0], (int, np.integer)): # NB. this is currently only used for reductions along an existing # dimension return self._obj return self.reduce(op, self._group_dim, skipna=skipna, keep_attrs=keep_attrs, allow_lazy=True) def first(self, skipna=None, keep_attrs=True): """Return the first element of each group along the group dimension """ return self._first_or_last(ops.first, skipna, keep_attrs) def last(self, skipna=None, keep_attrs=True): """Return the last element of each group along the group dimension """ return self._first_or_last(ops.last, skipna, keep_attrs) def assign_coords(self, **kwargs): """Assign coordinates by group. See also -------- Dataset.assign_coords """ return self.apply(lambda ds: ds.assign_coords(**kwargs)) def _maybe_reorder(xarray_obj, dim, positions): order = _inverse_permutation_indices(positions) if order is None: return xarray_obj else: return xarray_obj[{dim: order}] class DataArrayGroupBy(GroupBy, ImplementsArrayReduce): """GroupBy object specialized to grouping DataArray objects """ def _iter_grouped_shortcut(self): """Fast version of `_iter_grouped` that yields Variables without metadata """ var = self._obj.variable for indices in self._group_indices: yield var[{self._group_dim: indices}] def _concat_shortcut(self, applied, dim, positions=None): # nb. don't worry too much about maintaining this method -- it does # speed things up, but it's not very interpretable and there are much # faster alternatives (e.g., doing the grouped aggregation in a # compiled language) stacked = Variable.concat(applied, dim, shortcut=True) reordered = _maybe_reorder(stacked, dim, positions) result = self._obj._replace_maybe_drop_dims(reordered) return result def _restore_dim_order(self, stacked): def lookup_order(dimension): if dimension == self._group.name: dimension, = self._group.dims if dimension in self._obj.dims: axis = self._obj.get_axis_num(dimension) else: axis = 1e6 # some arbitrarily high value return axis new_order = sorted(stacked.dims, key=lookup_order) return stacked.transpose(*new_order) def apply(self, func, shortcut=False, **kwargs): """Apply a function over each array in the group and concatenate them together into a new array. `func` is called like `func(ar, *args, **kwargs)` for each array `ar` in this group. Apply uses heuristics (like `pandas.GroupBy.apply`) to figure out how to stack together the array. The rule is: 1. If the dimension along which the group coordinate is defined is still in the first grouped array after applying `func`, then stack over this dimension. 2. Otherwise, stack over the new dimension given by name of this grouping (the argument to the `groupby` function). Parameters ---------- func : function Callable to apply to each array. shortcut : bool, optional Whether or not to shortcut evaluation under the assumptions that: (1) The action of `func` does not depend on any of the array metadata (attributes or coordinates) but only on the data and dimensions. (2) The action of `func` creates arrays with homogeneous metadata, that is, with the same dimensions and attributes. If these conditions are satisfied `shortcut` provides significant speedup. This should be the case for many common groupby operations (e.g., applying numpy ufuncs). **kwargs Used to call `func(ar, **kwargs)` for each array `ar`. Returns ------- applied : DataArray or DataArray The result of splitting, applying and combining this array. """ if shortcut: grouped = self._iter_grouped_shortcut() else: grouped = self._iter_grouped() applied = (maybe_wrap_array(arr, func(arr, **kwargs)) for arr in grouped) return self._combine(applied, shortcut=shortcut) def _combine(self, applied, shortcut=False): """Recombine the applied objects like the original.""" applied_example, applied = peek_at(applied) coord, dim, positions = self._infer_concat_args(applied_example) if shortcut: combined = self._concat_shortcut(applied, dim, positions) else: combined = concat(applied, dim) combined = _maybe_reorder(combined, dim, positions) if isinstance(combined, type(self._obj)): # only restore dimension order for arrays combined = self._restore_dim_order(combined) if coord is not None: if shortcut: combined._coords[coord.name] = as_variable(coord, copy=True) else: combined.coords[coord.name] = coord combined = self._maybe_restore_empty_groups(combined) combined = self._maybe_unstack(combined) return combined def reduce(self, func, dim=None, axis=None, keep_attrs=False, shortcut=True, **kwargs): """Reduce the items in this group by applying `func` along some dimension(s). Parameters ---------- func : function Function which can be called in the form `func(x, axis=axis, **kwargs)` to return the result of collapsing an np.ndarray over an integer valued axis. dim : str or sequence of str, optional Dimension(s) over which to apply `func`. axis : int or sequence of int, optional Axis(es) over which to apply `func`. Only one of the 'dimension' and 'axis' arguments can be supplied. If neither are supplied, then `func` is calculated over all dimension for each group item. keep_attrs : bool, optional If True, the datasets's attributes (`attrs`) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes. **kwargs : dict Additional keyword arguments passed on to `func`. Returns ------- reduced : Array Array with summarized data and the indicated dimension(s) removed. """ def reduce_array(ar): return ar.reduce(func, dim, axis, keep_attrs=keep_attrs, **kwargs) return self.apply(reduce_array, shortcut=shortcut) ops.inject_reduce_methods(DataArrayGroupBy) ops.inject_binary_ops(DataArrayGroupBy) class DatasetGroupBy(GroupBy, ImplementsDatasetReduce): def apply(self, func, **kwargs): """Apply a function over each Dataset in the group and concatenate them together into a new Dataset. `func` is called like `func(ds, *args, **kwargs)` for each dataset `ds` in this group. Apply uses heuristics (like `pandas.GroupBy.apply`) to figure out how to stack together the datasets. The rule is: 1. If the dimension along which the group coordinate is defined is still in the first grouped item after applying `func`, then stack over this dimension. 2. Otherwise, stack over the new dimension given by name of this grouping (the argument to the `groupby` function). Parameters ---------- func : function Callable to apply to each sub-dataset. **kwargs Used to call `func(ds, **kwargs)` for each sub-dataset `ar`. Returns ------- applied : Dataset or DataArray The result of splitting, applying and combining this dataset. """ kwargs.pop('shortcut', None) # ignore shortcut if set (for now) applied = (func(ds, **kwargs) for ds in self._iter_grouped()) return self._combine(applied) def _combine(self, applied): """Recombine the applied objects like the original.""" applied_example, applied = peek_at(applied) coord, dim, positions = self._infer_concat_args(applied_example) combined = concat(applied, dim) combined = _maybe_reorder(combined, dim, positions) if coord is not None: combined[coord.name] = coord combined = self._maybe_restore_empty_groups(combined) combined = self._maybe_unstack(combined) return combined def reduce(self, func, dim=None, keep_attrs=False, **kwargs): """Reduce the items in this group by applying `func` along some dimension(s). Parameters ---------- func : function Function which can be called in the form `func(x, axis=axis, **kwargs)` to return the result of collapsing an np.ndarray over an integer valued axis. dim : str or sequence of str, optional Dimension(s) over which to apply `func`. axis : int or sequence of int, optional Axis(es) over which to apply `func`. Only one of the 'dimension' and 'axis' arguments can be supplied. If neither are supplied, then `func` is calculated over all dimension for each group item. keep_attrs : bool, optional If True, the datasets's attributes (`attrs`) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes. **kwargs : dict Additional keyword arguments passed on to `func`. Returns ------- reduced : Array Array with summarized data and the indicated dimension(s) removed. """ def reduce_dataset(ds): return ds.reduce(func, dim, keep_attrs, **kwargs) return self.apply(reduce_dataset) def assign(self, **kwargs): """Assign data variables by group. See also -------- Dataset.assign """ return self.apply(lambda ds: ds.assign(**kwargs)) ops.inject_reduce_methods(DatasetGroupBy) ops.inject_binary_ops(DatasetGroupBy) ����������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������xarray-0.9.1/xarray/core/indexing.py����������������������������������������������������������������0000644�1311115�0011610�00000047763�13043511243�017561� 0����������������������������������������������������������������������������������������������������ustar �shoyer��������������������������eng�����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������from __future__ import absolute_import from __future__ import division from __future__ import print_function from datetime import timedelta from collections import defaultdict import numpy as np import pandas as pd from . import utils from .pycompat import iteritems, range, dask_array_type, suppress from .utils import is_full_slice, is_dict_like def expanded_indexer(key, ndim): """Given a key for indexing an ndarray, return an equivalent key which is a tuple with length equal to the number of dimensions. The expansion is done by replacing all `Ellipsis` items with the right number of full slices and then padding the key with full slices so that it reaches the appropriate dimensionality. """ if not isinstance(key, tuple): # numpy treats non-tuple keys equivalent to tuples of length 1 key = (key,) new_key = [] # handling Ellipsis right is a little tricky, see: # http://docs.scipy.org/doc/numpy/reference/arrays.indexing.html#advanced-indexing found_ellipsis = False for k in key: if k is Ellipsis: if not found_ellipsis: new_key.extend((ndim + 1 - len(key)) * [slice(None)]) found_ellipsis = True else: new_key.append(slice(None)) else: new_key.append(k) if len(new_key) > ndim: raise IndexError('too many indices') new_key.extend((ndim - len(new_key)) * [slice(None)]) return tuple(new_key) def canonicalize_indexer(key, ndim): """Given an indexer for orthogonal array indexing, return an indexer that is a tuple composed entirely of slices, integer ndarrays and native python ints. """ def canonicalize(indexer): if not isinstance(indexer, slice): indexer = np.asarray(indexer) if indexer.ndim == 0: indexer = int(np.asscalar(indexer)) else: if indexer.ndim != 1: raise ValueError('orthogonal array indexing only supports ' '1d arrays') if indexer.dtype.kind == 'b': indexer, = np.nonzero(indexer) elif indexer.dtype.kind != 'i': raise ValueError('invalid subkey %r for integer based ' 'array indexing; all subkeys must be ' 'slices, integers or sequences of ' 'integers or Booleans' % indexer) return indexer return tuple(canonicalize(k) for k in expanded_indexer(key, ndim)) def _expand_slice(slice_, size): return np.arange(*slice_.indices(size)) def orthogonal_indexer(key, shape): """Given a key for orthogonal array indexing, returns an equivalent key suitable for indexing a numpy.ndarray with fancy indexing. """ # replace Ellipsis objects with slices key = list(canonicalize_indexer(key, len(shape))) # replace 1d arrays and slices with broadcast compatible arrays # note: we treat integers separately (instead of turning them into 1d # arrays) because integers (and only integers) collapse axes when used with # __getitem__ non_int_keys = [n for n, k in enumerate(key) if not isinstance(k, (int, np.integer))] def full_slices_unselected(n_list): def all_full_slices(key_index): return all(is_full_slice(key[n]) for n in key_index) if not n_list: return n_list elif all_full_slices(range(n_list[0] + 1)): return full_slices_unselected(n_list[1:]) elif all_full_slices(range(n_list[-1], len(key))): return full_slices_unselected(n_list[:-1]) else: return n_list # However, testing suggests it is OK to keep contiguous sequences of full # slices at the start or the end of the key. Keeping slices around (when # possible) instead of converting slices to arrays significantly speeds up # indexing. # (Honestly, I don't understand when it's not OK to keep slices even in # between integer indices if as array is somewhere in the key, but such are # the admittedly mind-boggling ways of numpy's advanced indexing.) array_keys = full_slices_unselected(non_int_keys) def maybe_expand_slice(k, length): return _expand_slice(k, length) if isinstance(k, slice) else k array_indexers = np.ix_(*(maybe_expand_slice(key[n], shape[n]) for n in array_keys)) for i, n in enumerate(array_keys): key[n] = array_indexers[i] return tuple(key) def _try_get_item(x): try: return x.item() except AttributeError: return x def _asarray_tuplesafe(values): """ Convert values into a numpy array of at most 1-dimension, while preserving tuples. Adapted from pandas.core.common._asarray_tuplesafe """ if isinstance(values, tuple): result = utils.to_0d_object_array(values) else: result = np.asarray(values) if result.ndim == 2: result = np.empty(len(values), dtype=object) result[:] = values return result def _is_nested_tuple(possible_tuple): return (isinstance(possible_tuple, tuple) and any(isinstance(value, (tuple, list, slice)) for value in possible_tuple)) def _index_method_kwargs(method, tolerance): # backwards compatibility for pandas<0.16 (method) or pandas<0.17 # (tolerance) kwargs = {} if method is not None: kwargs['method'] = method if tolerance is not None: kwargs['tolerance'] = tolerance return kwargs def get_loc(index, label, method=None, tolerance=None): kwargs = _index_method_kwargs(method, tolerance) return index.get_loc(label, **kwargs) def get_indexer(index, labels, method=None, tolerance=None): kwargs = _index_method_kwargs(method, tolerance) return index.get_indexer(labels, **kwargs) def convert_label_indexer(index, label, index_name='', method=None, tolerance=None): """Given a pandas.Index and labels (e.g., from __getitem__) for one dimension, return an indexer suitable for indexing an ndarray along that dimension. If `index` is a pandas.MultiIndex and depending on `label`, return a new pandas.Index or pandas.MultiIndex (otherwise return None). """ new_index = None if isinstance(label, slice): if method is not None or tolerance is not None: raise NotImplementedError( 'cannot use ``method`` argument if any indexers are ' 'slice objects') indexer = index.slice_indexer(_try_get_item(label.start), _try_get_item(label.stop), _try_get_item(label.step)) if not isinstance(indexer, slice): # unlike pandas, in xarray we never want to silently convert a slice # indexer into an array indexer raise KeyError('cannot represent labeled-based slice indexer for ' 'dimension %r with a slice over integer positions; ' 'the index is unsorted or non-unique') elif is_dict_like(label): is_nested_vals = _is_nested_tuple(tuple(label.values())) if not isinstance(index, pd.MultiIndex): raise ValueError('cannot use a dict-like object for selection on a ' 'dimension that does not have a MultiIndex') elif len(label) == index.nlevels and not is_nested_vals: indexer = index.get_loc(tuple((label[k] for k in index.names))) else: indexer, new_index = index.get_loc_level(tuple(label.values()), level=tuple(label.keys())) elif isinstance(label, tuple) and isinstance(index, pd.MultiIndex): if _is_nested_tuple(label): indexer = index.get_locs(label) elif len(label) == index.nlevels: indexer = index.get_loc(label) else: indexer, new_index = index.get_loc_level( label, level=list(range(len(label))) ) else: label = _asarray_tuplesafe(label) if label.ndim == 0: if isinstance(index, pd.MultiIndex): indexer, new_index = index.get_loc_level(label.item(), level=0) else: indexer = get_loc(index, label.item(), method, tolerance) elif label.dtype.kind == 'b': indexer, = np.nonzero(label) else: indexer = get_indexer(index, label, method, tolerance) if np.any(indexer < 0): raise KeyError('not all values found in index %r' % index_name) return indexer, new_index def get_dim_indexers(data_obj, indexers): """Given a xarray data object and label based indexers, return a mapping of label indexers with only dimension names as keys. It groups multiple level indexers given on a multi-index dimension into a single, dictionary indexer for that dimension (Raise a ValueError if it is not possible). """ invalid = [k for k in indexers if k not in data_obj.dims and k not in data_obj._level_coords] if invalid: raise ValueError("dimensions or multi-index levels %r do not exist" % invalid) level_indexers = defaultdict(dict) dim_indexers = {} for key, label in iteritems(indexers): dim, = data_obj[key].dims if key != dim: # assume here multi-index level indexer level_indexers[dim][key] = label else: dim_indexers[key] = label for dim, level_labels in iteritems(level_indexers): if dim_indexers.get(dim, False): raise ValueError("cannot combine multi-index level indexers " "with an indexer for dimension %s" % dim) dim_indexers[dim] = level_labels return dim_indexers def remap_label_indexers(data_obj, indexers, method=None, tolerance=None): """Given an xarray data object and label based indexers, return a mapping of equivalent location based indexers. Also return a mapping of updated pandas index objects (in case of multi-index level drop). """ if method is not None and not isinstance(method, str): raise TypeError('``method`` must be a string') pos_indexers = {} new_indexes = {} dim_indexers = get_dim_indexers(data_obj, indexers) for dim, label in iteritems(dim_indexers): try: index = data_obj.indexes[dim] except KeyError: # no index for this dimension: reuse the provided labels if method is not None or tolerance is not None: raise ValueError('cannot supply ``method`` or ``tolerance`` ' 'when the indexed dimension does not have ' 'an associated coordinate.') pos_indexers[dim] = label else: idxr, new_idx = convert_label_indexer(index, label, dim, method, tolerance) pos_indexers[dim] = idxr if new_idx is not None: new_indexes[dim] = new_idx return pos_indexers, new_indexes def slice_slice(old_slice, applied_slice, size): """Given a slice and the size of the dimension to which it will be applied, index it with another slice to return a new slice equivalent to applying the slices sequentially """ step = (old_slice.step or 1) * (applied_slice.step or 1) # For now, use the hack of turning old_slice into an ndarray to reconstruct # the slice start and stop. This is not entirely ideal, but it is still # definitely better than leaving the indexer as an array. items = _expand_slice(old_slice, size)[applied_slice] if len(items) > 0: start = items[0] stop = items[-1] + step if stop < 0: stop = None else: start = 0 stop = 0 return slice(start, stop, step) def _index_indexer_1d(old_indexer, applied_indexer, size): assert isinstance(applied_indexer, (int, np.integer, slice, np.ndarray)) if isinstance(applied_indexer, slice) and applied_indexer == slice(None): # shortcut for the usual case return old_indexer if isinstance(old_indexer, slice): if isinstance(applied_indexer, slice): indexer = slice_slice(old_indexer, applied_indexer, size) else: indexer = _expand_slice(old_indexer, size)[applied_indexer] else: indexer = old_indexer[applied_indexer] return indexer class LazilyIndexedArray(utils.NDArrayMixin): """Wrap an array that handles orthogonal indexing to make indexing lazy """ def __init__(self, array, key=None): """ Parameters ---------- array : array_like Array like object to index. key : tuple, optional Array indexer. If provided, it is assumed to already be in canonical expanded form. """ if key is None: key = (slice(None),) * array.ndim self.array = array self.key = key def _updated_key(self, new_key): new_key = iter(canonicalize_indexer(new_key, self.ndim)) key = [] for size, k in zip(self.array.shape, self.key): if isinstance(k, (int, np.integer)): key.append(k) else: key.append(_index_indexer_1d(k, next(new_key), size)) return tuple(key) @property def shape(self): shape = [] for size, k in zip(self.array.shape, self.key): if isinstance(k, slice): shape.append(len(range(*k.indices(size)))) elif isinstance(k, np.ndarray): shape.append(k.size) return tuple(shape) def __array__(self, dtype=None): array = orthogonally_indexable(self.array) return np.asarray(array[self.key], dtype=None) def __getitem__(self, key): return type(self)(self.array, self._updated_key(key)) def __setitem__(self, key, value): key = self._updated_key(key) self.array[key] = value def __repr__(self): return ('%s(array=%r, key=%r)' % (type(self).__name__, self.array, self.key)) class CopyOnWriteArray(utils.NDArrayMixin): def __init__(self, array): self.array = array self._copied = False def _ensure_copied(self): if not self._copied: self.array = np.array(self.array) self._copied = True def __array__(self, dtype=None): return np.asarray(self.array, dtype=dtype) def __getitem__(self, key): return type(self)(self.array[key]) def __setitem__(self, key, value): self._ensure_copied() self.array[key] = value class MemoryCachedArray(utils.NDArrayMixin): def __init__(self, array): self.array = array def _ensure_cached(self): if not isinstance(self.array, np.ndarray): self.array = np.asarray(self.array) def __array__(self, dtype=None): self._ensure_cached() return np.asarray(self.array, dtype=dtype) def __getitem__(self, key): return type(self)(self.array[key]) def __setitem__(self, key, value): self.array[key] = value def orthogonally_indexable(array): if isinstance(array, np.ndarray): return NumpyIndexingAdapter(array) if isinstance(array, pd.Index): return PandasIndexAdapter(array) if isinstance(array, dask_array_type): return DaskIndexingAdapter(array) return array class NumpyIndexingAdapter(utils.NDArrayMixin): """Wrap a NumPy array to use orthogonal indexing (array indexing accesses different dimensions independently, like netCDF4-python variables) """ # note: this object is somewhat similar to biggus.NumpyArrayAdapter in that # it implements orthogonal indexing, except it casts to a numpy array, # isn't lazy and supports writing values. def __init__(self, array): self.array = np.asarray(array) def __array__(self, dtype=None): return np.asarray(self.array, dtype=dtype) def _convert_key(self, key): key = expanded_indexer(key, self.ndim) if any(not isinstance(k, (int, np.integer, slice)) for k in key): # key would trigger fancy indexing key = orthogonal_indexer(key, self.shape) return key def _ensure_ndarray(self, value): # We always want the result of indexing to be a NumPy array. If it's # not, then it really should be a 0d array. Doing the coercion here # instead of inside variable.as_compatible_data makes it less error # prone. if not isinstance(value, np.ndarray): value = utils.to_0d_array(value) return value def __getitem__(self, key): key = self._convert_key(key) return self._ensure_ndarray(self.array[key]) def __setitem__(self, key, value): key = self._convert_key(key) self.array[key] = value class DaskIndexingAdapter(utils.NDArrayMixin): """Wrap a dask array to support orthogonal indexing """ def __init__(self, array): self.array = array def __getitem__(self, key): key = expanded_indexer(key, self.ndim) if any(not isinstance(k, (int, np.integer, slice)) for k in key): value = self.array for axis, subkey in reversed(list(enumerate(key))): value = value[(slice(None),) * axis + (subkey,)] else: value = self.array[key] return value class PandasIndexAdapter(utils.NDArrayMixin): """Wrap a pandas.Index to be better about preserving dtypes and to handle indexing by length 1 tuples like numpy """ def __init__(self, array, dtype=None): self.array = utils.safe_cast_to_index(array) if dtype is None: if isinstance(array, pd.PeriodIndex): dtype = np.dtype('O') elif hasattr(array, 'categories'): # category isn't a real numpy dtype dtype = array.categories.dtype elif not utils.is_valid_numpy_dtype(array.dtype): dtype = np.dtype('O') else: dtype = array.dtype self._dtype = dtype @property def dtype(self): return self._dtype def __array__(self, dtype=None): if dtype is None: dtype = self.dtype array = self.array if isinstance(array, pd.PeriodIndex): with suppress(AttributeError): # this might not be public API array = array.asobject return np.asarray(array.values, dtype=dtype) @property def shape(self): # .shape is broken on pandas prior to v0.15.2 return (len(self.array),) def __getitem__(self, key): if isinstance(key, tuple) and len(key) == 1: # unpack key so it can index a pandas.Index object (pandas.Index # objects don't like tuples) key, = key result = self.array[key] if isinstance(result, pd.Index): result = PandasIndexAdapter(result, dtype=self.dtype) else: # result is a scalar if result is pd.NaT: # work around the impossibility of casting NaT with asarray # note: it probably would be better in general to return # pd.Timestamp rather np.than datetime64 but this is easier # (for now) result = np.datetime64('NaT', 'ns') elif isinstance(result, timedelta): result = np.timedelta64(getattr(result, 'value', result), 'ns') elif self.dtype != object: result = np.asarray(result, dtype=self.dtype) # as for numpy.ndarray indexing, we always want the result to be # a NumPy array. result = utils.to_0d_array(result) return result def __repr__(self): return ('%s(array=%r, dtype=%r)' % (type(self).__name__, self.array, self.dtype)) �������������xarray-0.9.1/xarray/core/merge.py�������������������������������������������������������������������0000644�1311115�0011610�00000044134�13043511243�017040� 0����������������������������������������������������������������������������������������������������ustar �shoyer��������������������������eng�����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������from __future__ import absolute_import from __future__ import division from __future__ import print_function import pandas as pd from .alignment import deep_align from .pycompat import OrderedDict, basestring from .utils import Frozen from .variable import as_variable, assert_unique_multiindex_level_names PANDAS_TYPES = (pd.Series, pd.DataFrame, pd.Panel) _VALID_COMPAT = Frozen({'identical': 0, 'equals': 1, 'broadcast_equals': 2, 'minimal': 3, 'no_conflicts': 4}) def broadcast_dimension_size(variables): # type: (List[Variable],) -> Variable """Extract dimension sizes from a dictionary of variables. Raises ValueError if any dimensions have different sizes. """ dims = OrderedDict() for var in variables: for dim, size in zip(var.dims, var.shape): if dim in dims and size != dims[dim]: raise ValueError('index %r not aligned' % dim) dims[dim] = size return dims class MergeError(ValueError): """Error class for merge failures due to incompatible arguments. """ # inherits from ValueError for backward compatibility # TODO: move this to an xarray.exceptions module? def unique_variable(name, variables, compat='broadcast_equals'): # type: (Any, List[Variable], str) -> Variable """Return the unique variable from a list of variables or raise MergeError. Parameters ---------- name : hashable Name for this variable. variables : list of xarray.Variable List of Variable objects, all of which go by the same name in different inputs. compat : {'identical', 'equals', 'broadcast_equals', 'no_conflicts'}, optional Type of equality check to use. Returns ------- Variable to use in the result. Raises ------ MergeError: if any of the variables are not equal. """ out = variables[0] if len(variables) > 1: combine_method = None if compat == 'minimal': compat = 'broadcast_equals' if compat == 'broadcast_equals': dim_lengths = broadcast_dimension_size(variables) out = out.expand_dims(dim_lengths) if compat == 'no_conflicts': combine_method = 'fillna' for var in variables[1:]: if not getattr(out, compat)(var): raise MergeError('conflicting values for variable %r on ' 'objects to be combined:\n' 'first value: %r\nsecond value: %r' % (name, out, var)) if combine_method: # TODO: add preservation of attrs into fillna out = getattr(out, combine_method)(var) out.attrs = var.attrs return out def _assert_compat_valid(compat): if compat not in _VALID_COMPAT: raise ValueError("compat=%r invalid: must be %s" % (compat, set(_VALID_COMPAT))) class OrderedDefaultDict(OrderedDict): # minimal version of an ordered defaultdict # beware: does not pickle or copy properly def __init__(self, default_factory): self.default_factory = default_factory super(OrderedDefaultDict, self).__init__() def __missing__(self, key): self[key] = default = self.default_factory() return default def merge_variables( list_of_variables_dicts, # type: List[Mapping[Any, Variable]] priority_vars=None, # type: Optional[Mapping[Any, Variable]] compat='minimal', # type: str ): # type: (...) -> OrderedDict[Any, Variable] """Merge dicts of variables, while resolving conflicts appropriately. Parameters ---------- lists_of_variables_dicts : list of mappings with Variable values List of mappings for which each value is a xarray.Variable object. priority_vars : mapping with Variable or None values, optional If provided, variables are always taken from this dict in preference to the input variable dictionaries, without checking for conflicts. compat : {'identical', 'equals', 'broadcast_equals', 'minimal', 'no_conflicts'}, optional Type of equality check to use when checking for conflicts. Returns ------- OrderedDict with keys taken by the union of keys on list_of_variable_dicts, and Variable values corresponding to those that should be found on the merged result. """ if priority_vars is None: priority_vars = {} _assert_compat_valid(compat) dim_compat = min(compat, 'equals', key=_VALID_COMPAT.get) lookup = OrderedDefaultDict(list) for variables in list_of_variables_dicts: for name, var in variables.items(): lookup[name].append(var) # n.b. it's important to fill up merged in the original order in which # variables appear merged = OrderedDict() for name, variables in lookup.items(): if name in priority_vars: # one of these arguments (e.g., the first for in-place arithmetic # or the second for Dataset.update) takes priority merged[name] = priority_vars[name] else: dim_variables = [var for var in variables if (name,) == var.dims] if dim_variables: # if there are dimension coordinates, these must be equal (or # identical), and they take priority over non-dimension # coordinates merged[name] = unique_variable(name, dim_variables, dim_compat) else: try: merged[name] = unique_variable(name, variables, compat) except MergeError: if compat != 'minimal': # we need more than "minimal" compatibility (for which # we drop conflicting coordinates) raise return merged def expand_variable_dicts(list_of_variable_dicts): # type: (List[Union[Dataset, Dict]]) -> List[Dict[Any, Variable]] """Given a list of dicts with xarray object values, expand the values. Parameters ---------- list_of_variable_dicts : list of dict or Dataset objects The each value for the mappings must be of the following types: - an xarray.Variable - a tuple `(dims, data[, attrs[, encoding]])` that can be converted in an xarray.Variable - or an xarray.DataArray Returns ------- A list of ordered dictionaries corresponding to inputs, or coordinates from an input's values. The values of each ordered dictionary are all xarray.Variable objects. """ var_dicts = [] for variables in list_of_variable_dicts: if hasattr(variables, 'variables'): # duck-type Dataset sanitized_vars = variables.variables else: # append coords to var_dicts before appending sanitized_vars, # because we want coords to appear first sanitized_vars = OrderedDict() for name, var in variables.items(): if hasattr(var, '_coords'): # duck-type DataArray # use private API for speed coords = var._coords.copy() # explicitly overwritten variables should take precedence coords.pop(name, None) var_dicts.append(coords) var = as_variable(var, name=name) sanitized_vars[name] = var var_dicts.append(sanitized_vars) return var_dicts def determine_coords(list_of_variable_dicts): # type: (List[Dict]) -> Tuple[Set, Set] """Given a list of dicts with xarray object values, identify coordinates. Parameters ---------- list_of_variable_dicts : list of dict or Dataset objects Of the same form as the arguments to expand_variable_dicts. Returns ------- coord_names : set of variable names noncoord_names : set of variable names All variable found in the input should appear in either the set of coordinate or non-coordinate names. """ coord_names = set() noncoord_names = set() for variables in list_of_variable_dicts: if hasattr(variables, 'coords') and hasattr(variables, 'data_vars'): # duck-type Dataset coord_names.update(variables.coords) noncoord_names.update(variables.data_vars) else: for name, var in variables.items(): if hasattr(var, '_coords'): # duck-type DataArray coords = set(var._coords) # use private API for speed # explicitly overwritten variables should take precedence coords.discard(name) coord_names.update(coords) return coord_names, noncoord_names def coerce_pandas_values(objects): """Convert pandas values found in a list of labeled objects. Parameters ---------- objects : list of Dataset or mappings The mappings may contain any sort of objects coercible to xarray.Variables as keys, including pandas objects. Returns ------- List of Dataset or OrderedDict objects. Any inputs or values in the inputs that were pandas objects have been converted into native xarray objects. """ from .dataset import Dataset from .dataarray import DataArray out = [] for obj in objects: if isinstance(obj, Dataset): variables = obj else: variables = OrderedDict() if isinstance(obj, PANDAS_TYPES): obj = OrderedDict(obj.iteritems()) for k, v in obj.items(): if isinstance(v, PANDAS_TYPES): v = DataArray(v) variables[k] = v out.append(variables) return out def merge_coords_for_inplace_math(objs, priority_vars=None): """Merge coordinate variables without worrying about alignment. This function is used for merging variables in coordinates.py. """ expanded = expand_variable_dicts(objs) variables = merge_variables(expanded, priority_vars) assert_unique_multiindex_level_names(variables) return variables def _get_priority_vars(objects, priority_arg, compat='equals'): """Extract the priority variable from a list of mappings. We need this method because in some cases the priority argument itself might have conflicting values (e.g., if it is a dict with two DataArray values with conflicting coordinate values). Parameters ---------- objects : list of dictionaries of variables Dictionaries in which to find the priority variables. priority_arg : int or None Integer object whose variable should take priority. compat : {'identical', 'equals', 'broadcast_equals', 'no_conflicts'}, optional Compatibility checks to use when merging variables. Returns ------- None, if priority_arg is None, or an OrderedDict with Variable objects as values indicating priority variables. """ if priority_arg is None: priority_vars = {} else: expanded = expand_variable_dicts([objects[priority_arg]]) priority_vars = merge_variables(expanded, compat=compat) return priority_vars def expand_and_merge_variables(objs, priority_arg=None): """Merge coordinate variables without worrying about alignment. This function is used for merging variables in computation.py. """ expanded = expand_variable_dicts(objs) priority_vars = _get_priority_vars(objs, priority_arg) variables = merge_variables(expanded, priority_vars) return variables def merge_coords(objs, compat='minimal', join='outer', priority_arg=None, indexes=None): """Merge coordinate variables. See merge_core below for argument descriptions. This works similarly to merge_core, except everything we don't worry about whether variables are coordinates or not. """ _assert_compat_valid(compat) coerced = coerce_pandas_values(objs) aligned = deep_align(coerced, join=join, copy=False, indexes=indexes) expanded = expand_variable_dicts(aligned) priority_vars = _get_priority_vars(aligned, priority_arg, compat=compat) variables = merge_variables(expanded, priority_vars, compat=compat) assert_unique_multiindex_level_names(variables) return variables def merge_data_and_coords(data, coords, compat='broadcast_equals', join='outer'): """Used in Dataset.__init__.""" objs = [data, coords] explicit_coords = coords.keys() return merge_core(objs, compat, join, explicit_coords=explicit_coords) def merge_core(objs, compat='broadcast_equals', join='outer', priority_arg=None, explicit_coords=None, indexes=None): """Core logic for merging labeled objects. This is not public API. Parameters ---------- objs : list of mappings All values must be convertable to labeled arrays. compat : {'identical', 'equals', 'broadcast_equals', 'no_conflicts'}, optional Compatibility checks to use when merging variables. join : {'outer', 'inner', 'left', 'right'}, optional How to combine objects with different indexes. priority_arg : integer, optional Optional argument in `objs` that takes precedence over the others. explicit_coords : set, optional An explicit list of variables from `objs` that are coordinates. indexes : dict, optional Dictionary with values given by pandas.Index objects. Returns ------- variables : OrderedDict Ordered dictionary of Variable objects. coord_names : set Set of coordinate names. dims : dict Dictionary mapping from dimension names to sizes. Raises ------ MergeError if the merge cannot be done successfully. """ from .dataset import calculate_dimensions _assert_compat_valid(compat) coerced = coerce_pandas_values(objs) aligned = deep_align(coerced, join=join, copy=False, indexes=indexes) expanded = expand_variable_dicts(aligned) coord_names, noncoord_names = determine_coords(coerced) if explicit_coords is not None: coord_names.update(explicit_coords) priority_vars = _get_priority_vars(aligned, priority_arg, compat=compat) variables = merge_variables(expanded, priority_vars, compat=compat) assert_unique_multiindex_level_names(variables) dims = calculate_dimensions(variables) for dim, size in dims.items(): if dim in variables: coord_names.add(dim) ambiguous_coords = coord_names.intersection(noncoord_names) if ambiguous_coords: raise MergeError('unable to determine if these variables should be ' 'coordinates or not in the merged result: %s' % ambiguous_coords) return variables, coord_names, dict(dims) def merge(objects, compat='no_conflicts', join='outer'): """Merge any number of xarray objects into a single Dataset as variables. Parameters ---------- objects : Iterable[Union[xarray.Dataset, xarray.DataArray, dict]] Merge together all variables from these objects. If any of them are DataArray objects, they must have a name. compat : {'identical', 'equals', 'broadcast_equals', 'no_conflicts'}, optional String indicating how to compare variables of the same name for potential conflicts: - 'broadcast_equals': all values must be equal when variables are broadcast against each other to ensure common dimensions. - 'equals': all values and dimensions must be the same. - 'identical': all values, dimensions and attributes must be the same. - 'no_conflicts': only values which are not null in both datasets must be equal. The returned dataset then contains the combination of all non-null values. join : {'outer', 'inner', 'left', 'right'}, optional How to combine objects with different indexes. Returns ------- Dataset Dataset with combined variables from each object. Examples -------- >>> arrays = [xr.DataArray(n, name='var%d' % n) for n in range(5)] >>> xr.merge(arrays) Dimensions: () Coordinates: *empty* Data variables: var0 int64 0 var1 int64 1 var2 int64 2 var3 int64 3 var4 int64 4 Raises ------ xarray.MergeError If any variables with the same name have conflicting values. See also -------- concat """ from .dataarray import DataArray from .dataset import Dataset dict_like_objects = [obj.to_dataset() if isinstance(obj, DataArray) else obj for obj in objects] variables, coord_names, dims = merge_core(dict_like_objects, compat, join) merged = Dataset._construct_direct(variables, coord_names, dims) return merged def dataset_merge_method(dataset, other, overwrite_vars, compat, join): """Guts of the Dataset.merge method.""" # we are locked into supporting overwrite_vars for the Dataset.merge # method due for backwards compatibility # TODO: consider deprecating it? if isinstance(overwrite_vars, basestring): overwrite_vars = set([overwrite_vars]) overwrite_vars = set(overwrite_vars) if not overwrite_vars: objs = [dataset, other] priority_arg = None elif overwrite_vars == set(other): objs = [dataset, other] priority_arg = 1 else: other_overwrite = OrderedDict() other_no_overwrite = OrderedDict() for k, v in other.items(): if k in overwrite_vars: other_overwrite[k] = v else: other_no_overwrite[k] = v objs = [dataset, other_no_overwrite, other_overwrite] priority_arg = 2 return merge_core(objs, compat, join, priority_arg=priority_arg) def dataset_update_method(dataset, other): """Guts of the Dataset.update method""" return merge_core([dataset, other], priority_arg=1, indexes=dataset.indexes) ������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������xarray-0.9.1/xarray/core/npcompat.py����������������������������������������������������������������0000644�1311115�0011610�00000032531�13043511243�017560� 0����������������������������������������������������������������������������������������������������ustar �shoyer��������������������������eng�����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������"""Functions copied from unreleased versions of numpy. See the NumPy license in the licenses directory. """ from __future__ import absolute_import from __future__ import division from __future__ import print_function import numpy as np try: from numpy import broadcast_to, stack, nanprod, nancumsum, nancumprod except ImportError: # pragma: no cover # these functions should arrive in numpy v1.10 to v1.12 def _maybe_view_as_subclass(original_array, new_array): if type(original_array) is not type(new_array): # if input was an ndarray subclass and subclasses were OK, # then view the result as that subclass. new_array = new_array.view(type=type(original_array)) # Since we have done something akin to a view from original_array, we # should let the subclass finalize (if it has it implemented, i.e., is # not None). if new_array.__array_finalize__: new_array.__array_finalize__(original_array) return new_array def _broadcast_to(array, shape, subok, readonly): shape = tuple(shape) if np.iterable(shape) else (shape,) array = np.array(array, copy=False, subok=subok) if not shape and array.shape: raise ValueError('cannot broadcast a non-scalar to a scalar array') if any(size < 0 for size in shape): raise ValueError('all elements of broadcast shape must be non-' 'negative') broadcast = np.nditer( (array,), flags=['multi_index', 'zerosize_ok', 'refs_ok'], op_flags=['readonly'], itershape=shape, order='C').itviews[0] result = _maybe_view_as_subclass(array, broadcast) if not readonly and array.flags.writeable: result.flags.writeable = True return result def broadcast_to(array, shape, subok=False): """Broadcast an array to a new shape. Parameters ---------- array : array_like The array to broadcast. shape : tuple The shape of the desired array. subok : bool, optional If True, then sub-classes will be passed-through, otherwise the returned array will be forced to be a base-class array (default). Returns ------- broadcast : array A readonly view on the original array with the given shape. It is typically not contiguous. Furthermore, more than one element of a broadcasted array may refer to a single memory location. Raises ------ ValueError If the array is not compatible with the new shape according to NumPy's broadcasting rules. Examples -------- >>> x = np.array([1, 2, 3]) >>> np.broadcast_to(x, (3, 3)) array([[1, 2, 3], [1, 2, 3], [1, 2, 3]]) """ return _broadcast_to(array, shape, subok=subok, readonly=True) def stack(arrays, axis=0): """ Join a sequence of arrays along a new axis. .. versionadded:: 1.10.0 Parameters ---------- arrays : sequence of ndarrays Each array must have the same shape. axis : int, optional The axis along which the arrays will be stacked. Returns ------- stacked : ndarray The stacked array has one more dimension than the input arrays. See Also -------- concatenate : Join a sequence of arrays along an existing axis. split : Split array into a list of multiple sub-arrays of equal size. Examples -------- >>> arrays = [np.random.randn(3, 4) for _ in range(10)] >>> np.stack(arrays, axis=0).shape (10, 3, 4) >>> np.stack(arrays, axis=1).shape (3, 10, 4) >>> np.stack(arrays, axis=2).shape (3, 4, 10) >>> a = np.array([1, 2, 3]) >>> b = np.array([2, 3, 4]) >>> np.stack((a, b)) array([[1, 2, 3], [2, 3, 4]]) >>> np.stack((a, b), axis=-1) array([[1, 2], [2, 3], [3, 4]]) """ arrays = [np.asanyarray(arr) for arr in arrays] if not arrays: raise ValueError('need at least one array to stack') shapes = set(arr.shape for arr in arrays) if len(shapes) != 1: raise ValueError('all input arrays must have the same shape') result_ndim = arrays[0].ndim + 1 if not -result_ndim <= axis < result_ndim: msg = 'axis {0} out of bounds [-{1}, {1})'.format(axis, result_ndim) raise IndexError(msg) if axis < 0: axis += result_ndim sl = (slice(None),) * axis + (np.newaxis,) expanded_arrays = [arr[sl] for arr in arrays] return np.concatenate(expanded_arrays, axis=axis) def _replace_nan(a, val): """ If `a` is of inexact type, make a copy of `a`, replace NaNs with the `val` value, and return the copy together with a boolean mask marking the locations where NaNs were present. If `a` is not of inexact type, do nothing and return `a` together with a mask of None. Note that scalars will end up as array scalars, which is important for using the result as the value of the out argument in some operations. Parameters ---------- a : array-like Input array. val : float NaN values are set to val before doing the operation. Returns ------- y : ndarray If `a` is of inexact type, return a copy of `a` with the NaNs replaced by the fill value, otherwise return `a`. mask: {bool, None} If `a` is of inexact type, return a boolean mask marking locations of NaNs, otherwise return None. """ is_new = not isinstance(a, np.ndarray) if is_new: a = np.array(a) if not issubclass(a.dtype.type, np.inexact): return a, None if not is_new: # need copy a = np.array(a, subok=True) mask = np.isnan(a) np.copyto(a, val, where=mask) return a, mask def nanprod(a, axis=None, dtype=None, out=None, keepdims=0): """ Return the product of array elements over a given axis treating Not a Numbers (NaNs) as zero. One is returned for slices that are all-NaN or empty. .. versionadded:: 1.10.0 Parameters ---------- a : array_like Array containing numbers whose sum is desired. If `a` is not an array, a conversion is attempted. axis : int, optional Axis along which the product is computed. The default is to compute the product of the flattened array. dtype : data-type, optional The type of the returned array and of the accumulator in which the elements are summed. By default, the dtype of `a` is used. An exception is when `a` has an integer type with less precision than the platform (u)intp. In that case, the default will be either (u)int32 or (u)int64 depending on whether the platform is 32 or 64 bits. For inexact inputs, dtype must be inexact. out : ndarray, optional Alternate output array in which to place the result. The default is ``None``. If provided, it must have the same shape as the expected output, but the type will be cast if necessary. See `doc.ufuncs` for details. The casting of NaN to integer can yield unexpected results. keepdims : bool, optional If True, the axes which are reduced are left in the result as dimensions with size one. With this option, the result will broadcast correctly against the original `arr`. Returns ------- y : ndarray or numpy scalar See Also -------- numpy.prod : Product across array propagating NaNs. isnan : Show which elements are NaN. Notes ----- Numpy integer arithmetic is modular. If the size of a product exceeds the size of an integer accumulator, its value will wrap around and the result will be incorrect. Specifying ``dtype=double`` can alleviate that problem. Examples -------- >>> np.nanprod(1) 1 >>> np.nanprod([1]) 1 >>> np.nanprod([1, np.nan]) 1.0 >>> a = np.array([[1, 2], [3, np.nan]]) >>> np.nanprod(a) 6.0 >>> np.nanprod(a, axis=0) array([ 3., 2.]) """ a, mask = _replace_nan(a, 1) return np.prod(a, axis=axis, dtype=dtype, out=out, keepdims=keepdims) def nancumsum(a, axis=None, dtype=None, out=None): """ Return the cumulative sum of array elements over a given axis treating Not a Numbers (NaNs) as zero. The cumulative sum does not change when NaNs are encountered and leading NaNs are replaced by zeros. Zeros are returned for slices that are all-NaN or empty. .. versionadded:: 1.12.0 Parameters ---------- a : array_like Input array. axis : int, optional Axis along which the cumulative sum is computed. The default (None) is to compute the cumsum over the flattened array. dtype : dtype, optional Type of the returned array and of the accumulator in which the elements are summed. If `dtype` is not specified, it defaults to the dtype of `a`, unless `a` has an integer dtype with a precision less than that of the default platform integer. In that case, the default platform integer is used. out : ndarray, optional Alternative output array in which to place the result. It must have the same shape and buffer length as the expected output but the type will be cast if necessary. See `doc.ufuncs` (Section "Output arguments") for more details. Returns ------- nancumsum : ndarray. A new array holding the result is returned unless `out` is specified, in which it is returned. The result has the same size as `a`, and the same shape as `a` if `axis` is not None or `a` is a 1-d array. See Also -------- numpy.cumsum : Cumulative sum across array propagating NaNs. isnan : Show which elements are NaN. Examples -------- >>> np.nancumsum(1) array([1]) >>> np.nancumsum([1]) array([1]) >>> np.nancumsum([1, np.nan]) array([ 1., 1.]) >>> a = np.array([[1, 2], [3, np.nan]]) >>> np.nancumsum(a) array([ 1., 3., 6., 6.]) >>> np.nancumsum(a, axis=0) array([[ 1., 2.], [ 4., 2.]]) >>> np.nancumsum(a, axis=1) array([[ 1., 3.], [ 3., 3.]]) """ a, mask = _replace_nan(a, 0) return np.cumsum(a, axis=axis, dtype=dtype, out=out) def nancumprod(a, axis=None, dtype=None, out=None): """ Return the cumulative product of array elements over a given axis treating Not a Numbers (NaNs) as one. The cumulative product does not change when NaNs are encountered and leading NaNs are replaced by ones. Ones are returned for slices that are all-NaN or empty. .. versionadded:: 1.12.0 Parameters ---------- a : array_like Input array. axis : int, optional Axis along which the cumulative product is computed. By default the input is flattened. dtype : dtype, optional Type of the returned array, as well as of the accumulator in which the elements are multiplied. If *dtype* is not specified, it defaults to the dtype of `a`, unless `a` has an integer dtype with a precision less than that of the default platform integer. In that case, the default platform integer is used instead. out : ndarray, optional Alternative output array in which to place the result. It must have the same shape and buffer length as the expected output but the type of the resulting values will be cast if necessary. Returns ------- nancumprod : ndarray A new array holding the result is returned unless `out` is specified, in which case it is returned. See Also -------- numpy.cumprod : Cumulative product across array propagating NaNs. isnan : Show which elements are NaN. Examples -------- >>> np.nancumprod(1) array([1]) >>> np.nancumprod([1]) array([1]) >>> np.nancumprod([1, np.nan]) array([ 1., 1.]) >>> a = np.array([[1, 2], [3, np.nan]]) >>> np.nancumprod(a) array([ 1., 2., 6., 6.]) >>> np.nancumprod(a, axis=0) array([[ 1., 2.], [ 3., 2.]]) >>> np.nancumprod(a, axis=1) array([[ 1., 2.], [ 3., 3.]]) """ a, mask = _replace_nan(a, 1) return np.cumprod(a, axis=axis, dtype=dtype, out=out) �����������������������������������������������������������������������������������������������������������������������������������������������������������������������xarray-0.9.1/xarray/core/nputils.py�����������������������������������������������������������������0000644�1311115�0011610�00000005032�13043511243�017431� 0����������������������������������������������������������������������������������������������������ustar �shoyer��������������������������eng�����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������from __future__ import absolute_import from __future__ import division from __future__ import print_function import numpy as np import pandas as pd import warnings def _validate_axis(data, axis): ndim = data.ndim if not -ndim <= axis < ndim: raise IndexError('axis %r out of bounds [-%r, %r)' % (axis, ndim, ndim)) if axis < 0: axis += ndim return axis def _select_along_axis(values, idx, axis): other_ind = np.ix_(*[np.arange(s) for s in idx.shape]) sl = other_ind[:axis] + (idx,) + other_ind[axis:] return values[sl] def nanfirst(values, axis): axis = _validate_axis(values, axis) idx_first = np.argmax(~pd.isnull(values), axis=axis) return _select_along_axis(values, idx_first, axis) def nanlast(values, axis): axis = _validate_axis(values, axis) rev = (slice(None),) * axis + (slice(None, None, -1),) idx_last = -1 - np.argmax(~pd.isnull(values)[rev], axis=axis) return _select_along_axis(values, idx_last, axis) def inverse_permutation(indices): """Return indices for an inverse permutation. Parameters ---------- indices : 1D np.ndarray with dtype=int Integer positions to assign elements to. Returns ------- inverse_permutation : 1D np.ndarray with dtype=int Integer indices to take from the original array to create the permutation. """ # use intp instead of int64 because of windows :( inverse_permutation = np.empty(len(indices), dtype=np.intp) inverse_permutation[indices] = np.arange(len(indices), dtype=np.intp) return inverse_permutation def _ensure_bool_is_ndarray(result, *args): # numpy will sometimes return a scalar value from binary comparisons if it # can't handle the comparison instead of broadcasting, e.g., # In [10]: 1 == np.array(['a', 'b']) # Out[10]: False # This function ensures that the result is the appropriate shape in these # cases if isinstance(result, bool): shape = np.broadcast(*args).shape constructor = np.ones if result else np.zeros result = constructor(shape, dtype=bool) return result def array_eq(self, other): with warnings.catch_warnings(): warnings.filterwarnings('ignore', r'elementwise comparison failed') return _ensure_bool_is_ndarray(self == other, self, other) def array_ne(self, other): with warnings.catch_warnings(): warnings.filterwarnings('ignore', r'elementwise comparison failed') return _ensure_bool_is_ndarray(self != other, self, other) ������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������xarray-0.9.1/xarray/core/ops.py���������������������������������������������������������������������0000644�1311115�0011610�00000045315�13043675344�016561� 0����������������������������������������������������������������������������������������������������ustar �shoyer��������������������������eng�����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������from __future__ import absolute_import from __future__ import division from __future__ import print_function from functools import partial import contextlib import inspect import operator import warnings from distutils.version import LooseVersion import numpy as np import pandas as pd from . import npcompat from .pycompat import PY3, dask_array_type from .nputils import nanfirst, nanlast, array_eq, array_ne try: import bottleneck as bn has_bottleneck = True except ImportError: # use numpy methods instead bn = np has_bottleneck = False try: import dask.array as da has_dask = True except ImportError: has_dask = False UNARY_OPS = ['neg', 'pos', 'abs', 'invert'] CMP_BINARY_OPS = ['lt', 'le', 'ge', 'gt'] NUM_BINARY_OPS = ['add', 'sub', 'mul', 'truediv', 'floordiv', 'mod', 'pow', 'and', 'xor', 'or'] if not PY3: NUM_BINARY_OPS.append('div') # methods which pass on the numpy return value unchanged # be careful not to list methods that we would want to wrap later NUMPY_SAME_METHODS = ['item', 'searchsorted'] # methods which don't modify the data shape, so the result should still be # wrapped in an Variable/DataArray NUMPY_UNARY_METHODS = ['astype', 'argsort', 'clip', 'conj', 'conjugate'] PANDAS_UNARY_FUNCTIONS = ['isnull', 'notnull'] # methods which remove an axis REDUCE_METHODS = ['all', 'any'] NAN_REDUCE_METHODS = ['argmax', 'argmin', 'max', 'min', 'mean', 'prod', 'sum', 'std', 'var', 'median'] NAN_CUM_METHODS = ['cumsum', 'cumprod'] BOTTLENECK_ROLLING_METHODS = {'move_sum': 'sum', 'move_mean': 'mean', 'move_std': 'std', 'move_min': 'min', 'move_max': 'max'} # TODO: wrap take, dot, sort def _dask_or_eager_func(name, eager_module=np, list_of_args=False, n_array_args=1): if has_dask: def f(*args, **kwargs): dispatch_args = args[0] if list_of_args else args if any(isinstance(a, da.Array) for a in dispatch_args[:n_array_args]): module = da else: module = eager_module return getattr(module, name)(*args, **kwargs) else: def f(data, *args, **kwargs): return getattr(eager_module, name)(data, *args, **kwargs) return f def _fail_on_dask_array_input(values, msg=None, func_name=None): if isinstance(values, dask_array_type): if msg is None: msg = '%r is not a valid method on dask arrays' if func_name is None: func_name = inspect.stack()[1][3] raise NotImplementedError(msg % func_name) around = _dask_or_eager_func('around') isclose = _dask_or_eager_func('isclose') notnull = _dask_or_eager_func('notnull', pd) _isnull = _dask_or_eager_func('isnull', pd) def isnull(data): # GH837, GH861 # isnull fcn from pandas will throw TypeError when run on numpy structured # array therefore for dims that are np structured arrays we assume all # data is present try: return _isnull(data) except TypeError: return np.zeros(data.shape, dtype=bool) transpose = _dask_or_eager_func('transpose') where = _dask_or_eager_func('where', n_array_args=3) insert = _dask_or_eager_func('insert') take = _dask_or_eager_func('take') broadcast_to = _dask_or_eager_func('broadcast_to', npcompat) concatenate = _dask_or_eager_func('concatenate', list_of_args=True) stack = _dask_or_eager_func('stack', npcompat, list_of_args=True) array_all = _dask_or_eager_func('all') array_any = _dask_or_eager_func('any') tensordot = _dask_or_eager_func('tensordot', n_array_args=2) def asarray(data): return data if isinstance(data, dask_array_type) else np.asarray(data) def as_like_arrays(*data): if all(isinstance(d, dask_array_type) for d in data): return data else: return tuple(np.asarray(d) for d in data) def allclose_or_equiv(arr1, arr2, rtol=1e-5, atol=1e-8): """Like np.allclose, but also allows values to be NaN in both arrays """ arr1, arr2 = as_like_arrays(arr1, arr2) if arr1.shape != arr2.shape: return False return bool(isclose(arr1, arr2, rtol=rtol, atol=atol, equal_nan=True).all()) def array_equiv(arr1, arr2): """Like np.array_equal, but also allows values to be NaN in both arrays """ arr1, arr2 = as_like_arrays(arr1, arr2) if arr1.shape != arr2.shape: return False flag_array = (arr1 == arr2) flag_array |= (isnull(arr1) & isnull(arr2)) return bool(flag_array.all()) def array_notnull_equiv(arr1, arr2): """Like np.array_equal, but also allows values to be NaN in either or both arrays """ arr1, arr2 = as_like_arrays(arr1, arr2) if arr1.shape != arr2.shape: return False flag_array = (arr1 == arr2) flag_array |= isnull(arr1) flag_array |= isnull(arr2) return bool(flag_array.all()) def _call_possibly_missing_method(arg, name, args, kwargs): try: method = getattr(arg, name) except AttributeError: _fail_on_dask_array_input(arg, func_name=name) if hasattr(arg, 'data'): _fail_on_dask_array_input(arg.data, func_name=name) raise else: return method(*args, **kwargs) def _values_method_wrapper(name): def func(self, *args, **kwargs): return _call_possibly_missing_method(self.data, name, args, kwargs) func.__name__ = name func.__doc__ = getattr(np.ndarray, name).__doc__ return func def _method_wrapper(name): def func(self, *args, **kwargs): return _call_possibly_missing_method(self, name, args, kwargs) func.__name__ = name func.__doc__ = getattr(np.ndarray, name).__doc__ return func def _func_slash_method_wrapper(f, name=None): # try to wrap a method, but if not found use the function # this is useful when patching in a function as both a DataArray and # Dataset method if name is None: name = f.__name__ def func(self, *args, **kwargs): try: return getattr(self, name)(*args, **kwargs) except AttributeError: return f(self, *args, **kwargs) func.__name__ = name func.__doc__ = f.__doc__ return func _CUM_DOCSTRING_TEMPLATE = \ """Apply `{name}` along some dimension of {cls}. Parameters ---------- {extra_args} skipna : bool, optional If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64). keep_attrs : bool, optional If True, the attributes (`attrs`) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes. **kwargs : dict Additional keyword arguments passed on to `{name}`. Returns ------- cumvalue : {cls} New {cls} object with `{name}` applied to its data along the indicated dimension. """ _REDUCE_DOCSTRING_TEMPLATE = \ """Reduce this {cls}'s data by applying `{name}` along some dimension(s). Parameters ---------- {extra_args} skipna : bool, optional If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64). keep_attrs : bool, optional If True, the attributes (`attrs`) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes. **kwargs : dict Additional keyword arguments passed on to `{name}`. Returns ------- reduced : {cls} New {cls} object with `{name}` applied to its data and the indicated dimension(s) removed. """ _ROLLING_REDUCE_DOCSTRING_TEMPLATE = \ """Reduce this DataArrayRolling's data windows by applying `{name}` along its dimension. Parameters ---------- **kwargs : dict Additional keyword arguments passed on to `{name}`. Returns ------- reduced : DataArray New DataArray object with `{name}` applied along its rolling dimnension. """ def count(data, axis=None): """Count the number of non-NA in this array along the given axis or axes """ return sum(~isnull(data), axis=axis) def fillna(data, other, join="left", dataset_join="left"): """Fill missing values in this object with data from the other object. Follows normal broadcasting and alignment rules. Parameters ---------- join : {'outer', 'inner', 'left', 'right'}, optional Method for joining the indexes of the passed objects along each dimension - 'outer': use the union of object indexes - 'inner': use the intersection of object indexes - 'left': use indexes from the first object with each dimension - 'right': use indexes from the last object with each dimension dataset_join : {'outer', 'inner', 'left', 'right'}, optional Method for joining variables of Dataset objects with mismatched data variables. - 'outer': take variables from both Dataset objects - 'inner': take only overlapped variables - 'left': take only variables from the first object - 'right': take only variables from the last object """ from .computation import apply_ufunc def _fillna(data, other): return where(isnull(data), other, data) return apply_ufunc(_fillna, data, other, join=join, dask_array="allowed", dataset_join=dataset_join, dataset_fill_value=np.nan, keep_attrs=True) def where_method(data, cond, other=np.nan): """Select values from this object that are True in cond. Everything else gets masked with other. Follows normal broadcasting and alignment rules. """ return where(cond, data, other) @contextlib.contextmanager def _ignore_warnings_if(condition): if condition: with warnings.catch_warnings(): warnings.simplefilter('ignore') yield else: yield def _create_nan_agg_method(name, numeric_only=False, np_compat=False, no_bottleneck=False, coerce_strings=False, keep_dims=False): def f(values, axis=None, skipna=None, **kwargs): # ignore keyword args inserted by np.mean and other numpy aggregators # automatically: kwargs.pop('dtype', None) kwargs.pop('out', None) values = asarray(values) if coerce_strings and values.dtype.kind in 'SU': values = values.astype(object) if skipna or (skipna is None and values.dtype.kind in 'cf'): if values.dtype.kind not in ['i', 'f', 'c']: raise NotImplementedError( 'skipna=True not yet implemented for %s with dtype %s' % (name, values.dtype)) nanname = 'nan' + name if isinstance(axis, tuple) or not values.dtype.isnative or no_bottleneck: # bottleneck can't handle multiple axis arguments or non-native # endianness if np_compat: eager_module = npcompat else: eager_module = np else: eager_module = bn func = _dask_or_eager_func(nanname, eager_module) using_numpy_nan_func = eager_module is np or eager_module is npcompat else: func = _dask_or_eager_func(name) using_numpy_nan_func = False with _ignore_warnings_if(using_numpy_nan_func): try: return func(values, axis=axis, **kwargs) except AttributeError: if isinstance(values, dask_array_type): msg = '%s is not yet implemented on dask arrays' % name else: assert using_numpy_nan_func msg = ('%s is not available with skipna=False with the ' 'installed version of numpy; upgrade to numpy 1.12 ' 'or newer to use skipna=True or skipna=None' % name) raise NotImplementedError(msg) f.numeric_only = numeric_only f.keep_dims = keep_dims f.__name__ = name return f argmax = _create_nan_agg_method('argmax', coerce_strings=True) argmin = _create_nan_agg_method('argmin', coerce_strings=True) max = _create_nan_agg_method('max', coerce_strings=True) min = _create_nan_agg_method('min', coerce_strings=True) sum = _create_nan_agg_method('sum', numeric_only=True) mean = _create_nan_agg_method('mean', numeric_only=True) std = _create_nan_agg_method('std', numeric_only=True) var = _create_nan_agg_method('var', numeric_only=True) median = _create_nan_agg_method('median', numeric_only=True) prod = _create_nan_agg_method('prod', numeric_only=True, np_compat=True, no_bottleneck=True) cumprod = _create_nan_agg_method('cumprod', numeric_only=True, np_compat=True, no_bottleneck=True, keep_dims=True) cumsum = _create_nan_agg_method('cumsum', numeric_only=True, np_compat=True, no_bottleneck=True, keep_dims=True) _fail_on_dask_array_input_skipna = partial( _fail_on_dask_array_input, msg='%r with skipna=True is not yet implemented on dask arrays') def first(values, axis, skipna=None): """Return the first non-NA elements in this array along the given axis """ if (skipna or skipna is None) and values.dtype.kind not in 'iSU': # only bother for dtypes that can hold NaN _fail_on_dask_array_input_skipna(values) return nanfirst(values, axis) return take(values, 0, axis=axis) def last(values, axis, skipna=None): """Return the last non-NA elements in this array along the given axis """ if (skipna or skipna is None) and values.dtype.kind not in 'iSU': # only bother for dtypes that can hold NaN _fail_on_dask_array_input_skipna(values) return nanlast(values, axis) return take(values, -1, axis=axis) def inject_reduce_methods(cls): methods = ([(name, globals()['array_%s' % name], False) for name in REDUCE_METHODS] + [(name, globals()[name], True) for name in NAN_REDUCE_METHODS] + [('count', count, False)]) for name, f, include_skipna in methods: numeric_only = getattr(f, 'numeric_only', False) func = cls._reduce_method(f, include_skipna, numeric_only) func.__name__ = name func.__doc__ = _REDUCE_DOCSTRING_TEMPLATE.format( name=name, cls=cls.__name__, extra_args=cls._reduce_extra_args_docstring.format(name=name)) setattr(cls, name, func) def inject_cum_methods(cls): methods = ([(name, globals()[name], True) for name in NAN_CUM_METHODS]) for name, f, include_skipna in methods: numeric_only = getattr(f, 'numeric_only', False) func = cls._reduce_method(f, include_skipna, numeric_only) func.__name__ = name func.__doc__ = _CUM_DOCSTRING_TEMPLATE.format( name=name, cls=cls.__name__, extra_args=cls._cum_extra_args_docstring.format(name=name)) setattr(cls, name, func) def op_str(name): return '__%s__' % name def get_op(name): return getattr(operator, op_str(name)) NON_INPLACE_OP = dict((get_op('i' + name), get_op(name)) for name in NUM_BINARY_OPS) def inplace_to_noninplace_op(f): return NON_INPLACE_OP[f] def inject_binary_ops(cls, inplace=False): for name in CMP_BINARY_OPS + NUM_BINARY_OPS: setattr(cls, op_str(name), cls._binary_op(get_op(name))) for name, f in [('eq', array_eq), ('ne', array_ne)]: setattr(cls, op_str(name), cls._binary_op(f)) # patch in where f = _func_slash_method_wrapper(where_method, 'where') setattr(cls, '_where', cls._binary_op(f)) for name in NUM_BINARY_OPS: # only numeric operations have in-place and reflexive variants setattr(cls, op_str('r' + name), cls._binary_op(get_op(name), reflexive=True)) if inplace: setattr(cls, op_str('i' + name), cls._inplace_binary_op(get_op('i' + name))) def inject_all_ops_and_reduce_methods(cls, priority=50, array_only=True): # prioritize our operations over those of numpy.ndarray (priority=1) # and numpy.matrix (priority=10) cls.__array_priority__ = priority # patch in standard special operations for name in UNARY_OPS: setattr(cls, op_str(name), cls._unary_op(get_op(name))) inject_binary_ops(cls, inplace=True) # patch in numpy/pandas methods for name in NUMPY_UNARY_METHODS: setattr(cls, name, cls._unary_op(_method_wrapper(name))) for name in PANDAS_UNARY_FUNCTIONS: f = _func_slash_method_wrapper(getattr(pd, name)) setattr(cls, name, cls._unary_op(f)) f = _func_slash_method_wrapper(around, name='round') setattr(cls, 'round', cls._unary_op(f)) if array_only: # these methods don't return arrays of the same shape as the input, so # don't try to patch these in for Dataset objects for name in NUMPY_SAME_METHODS: setattr(cls, name, _values_method_wrapper(name)) inject_reduce_methods(cls) inject_cum_methods(cls) def inject_bottleneck_rolling_methods(cls): # standard numpy reduce methods methods = [(name, globals()[name]) for name in NAN_REDUCE_METHODS] for name, f in methods: func = cls._reduce_method(f) func.__name__ = name func.__doc__ = _ROLLING_REDUCE_DOCSTRING_TEMPLATE.format(name=func.__name__) setattr(cls, name, func) # bottleneck rolling methods if has_bottleneck: if LooseVersion(bn.__version__) < LooseVersion('1.0'): return for bn_name, method_name in BOTTLENECK_ROLLING_METHODS.items(): f = getattr(bn, bn_name) func = cls._bottleneck_reduce(f) func.__name__ = method_name func.__doc__ = _ROLLING_REDUCE_DOCSTRING_TEMPLATE.format(name=func.__name__) setattr(cls, method_name, func) # bottleneck rolling methods without min_count f = getattr(bn, 'move_median') func = cls._bottleneck_reduce_without_min_count(f) func.__name__ = 'median' func.__doc__ = _ROLLING_REDUCE_DOCSTRING_TEMPLATE.format(name=func.__name__) setattr(cls, 'median', func) �������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������xarray-0.9.1/xarray/core/options.py�����������������������������������������������������������������0000644�1311115�0011610�00000002602�13043511243�017426� 0����������������������������������������������������������������������������������������������������ustar �shoyer��������������������������eng�����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������from __future__ import absolute_import from __future__ import division from __future__ import print_function OPTIONS = { 'display_width': 80, 'arithmetic_join': 'inner', } class set_options(object): """Set options for xarray in a controlled context. Currently supported options: - ``display_width``: maximum display width for ``repr`` on xarray objects. Default: ``80``. - ``arithmetic_join``: DataArray/Dataset alignment in binary operations. Default: ``'inner'``. You can use ``set_options`` either as a context manager: >>> ds = xr.Dataset({'x': np.arange(1000)}) >>> with xr.set_options(display_width=40): ... print(ds) Dimensions: (x: 1000) Coordinates: * x (x) int64 0 1 2 3 4 5 6 ... Data variables: *empty* Or to set global options: >>> xr.set_options(display_width=80) """ def __init__(self, **kwargs): invalid_options = {k for k in kwargs if k not in OPTIONS} if invalid_options: raise ValueError('argument names %r are not in the set of valid ' 'options %r' % (invalid_options, set(OPTIONS))) self.old = OPTIONS.copy() OPTIONS.update(kwargs) def __enter__(self): return def __exit__(self, type, value, traceback): OPTIONS.clear() OPTIONS.update(self.old) ������������������������������������������������������������������������������������������������������������������������������xarray-0.9.1/xarray/core/pycompat.py����������������������������������������������������������������0000644�1311115�0011610�00000005121�13043511243�017566� 0����������������������������������������������������������������������������������������������������ustar �shoyer��������������������������eng�����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������from __future__ import absolute_import from __future__ import division from __future__ import print_function import sys PY2 = sys.version_info[0] < 3 PY3 = sys.version_info[0] >= 3 if PY3: # pragma: no cover basestring = str unicode_type = str bytes_type = bytes def iteritems(d): return iter(d.items()) def itervalues(d): return iter(d.values()) range = range zip = zip from functools import reduce import builtins from urllib.request import urlretrieve else: # pragma: no cover # Python 2 basestring = basestring unicode_type = unicode bytes_type = str def iteritems(d): return d.iteritems() def itervalues(d): return d.itervalues() range = xrange from itertools import izip as zip, imap as map reduce = reduce import __builtin__ as builtins from urllib import urlretrieve try: from cyordereddict import OrderedDict except ImportError: # pragma: no cover try: from collections import OrderedDict except ImportError: from ordereddict import OrderedDict try: # solely for isinstance checks import dask.array dask_array_type = (dask.array.Array,) except ImportError: # pragma: no cover dask_array_type = () try: from contextlib import suppress except ImportError: # backport from Python 3.5: class suppress: """Context manager to suppress specified exceptions After the exception is suppressed, execution proceeds with the next statement following the with statement. with suppress(FileNotFoundError): os.remove(somefile) # Execution still resumes here if the file was already removed """ def __init__(self, *exceptions): self._exceptions = exceptions def __enter__(self): pass def __exit__(self, exctype, excinst, exctb): # Unlike isinstance and issubclass, CPython exception handling # currently only looks at the concrete type hierarchy (ignoring # the instance and subclass checking hooks). While Guido considers # that a bug rather than a feature, it's a fairly hard one to fix # due to various internal implementation details. suppress provides # the simpler issubclass based semantics, rather than trying to # exactly reproduce the limitations of the CPython interpreter. # # See http://bugs.python.org/issue12029 for more details return exctype is not None and issubclass(exctype, self._exceptions) �����������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������xarray-0.9.1/xarray/core/rolling.py�����������������������������������������������������������������0000644�1311115�0011610�00000013737�13043675344�017431� 0����������������������������������������������������������������������������������������������������ustar �shoyer��������������������������eng�����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������from __future__ import absolute_import from __future__ import division from __future__ import print_function import numpy as np import warnings from distutils.version import LooseVersion from .pycompat import OrderedDict, zip from .common import ImplementsRollingArrayReduce, full_like from .combine import concat from .ops import inject_bottleneck_rolling_methods, has_bottleneck, bn class Rolling(object): """A object that implements the moving window pattern. See Also -------- Dataset.groupby DataArray.groupby Dataset.rolling DataArray.rolling """ _attributes = ['window', 'min_periods', 'center', 'dim'] def __init__(self, obj, min_periods=None, center=False, **windows): """ Moving window object. Parameters ---------- obj : Dataset or DataArray Object to window. min_periods : int, default None Minimum number of observations in window required to have a value (otherwise result is NA). The default, None, is equivalent to setting min_periods equal to the size of the window. center : boolean, default False Set the labels at the center of the window. **windows : dim=window dim : str Name of the dimension to create the rolling iterator along (e.g., `time`). window : int Size of the moving window. Returns ------- rolling : type of input argument """ if (has_bottleneck and (LooseVersion(bn.__version__) < LooseVersion('1.0'))): warnings.warn('xarray requires bottleneck version of 1.0 or ' 'greater for rolling operations. Rolling ' 'aggregation methods will use numpy instead' 'of bottleneck.') if len(windows) != 1: raise ValueError('exactly one dim/window should be provided') dim, window = next(iter(windows.items())) if window <= 0: raise ValueError('window must be > 0') self.obj = obj # attributes self.window = window self.min_periods = min_periods if min_periods is None: self._min_periods = window else: if min_periods <= 0: raise ValueError('min_periods must be greater than zero or None') self._min_periods = min_periods self.center = center self.dim = dim self._axis_num = self.obj.get_axis_num(self.dim) self._windows = None self._valid_windows = None self.window_indices = None self.window_labels = None self._setup_windows() def __repr__(self): """provide a nice str repr of our rolling object""" attrs = ["{k}->{v}".format(k=k, v=getattr(self, k)) for k in self._attributes if getattr(self, k, None) is not None] return "{klass} [{attrs}]".format(klass=self.__class__.__name__, attrs=','.join(attrs)) @property def windows(self): if self._windows is None: self._windows = OrderedDict(zip(self.window_labels, self.window_indices)) return self._windows def __len__(self): return len(self.obj[self.dim]) def __iter__(self): for (label, indices, valid) in zip(self.window_labels, self.window_indices, self._valid_windows): window = self.obj.isel(**{self.dim: indices}) if not valid: window = full_like(window, fill_value=True, dtype=bool) yield (label, window) def _setup_windows(self): """ Find the indices and labels for each window """ from .dataarray import DataArray self.window_labels = self.obj[self.dim] window = int(self.window) dim_size = self.obj[self.dim].size stops = np.arange(dim_size) + 1 starts = np.maximum(stops - window, 0) if self._min_periods > 1: valid_windows = (stops - starts) >= self._min_periods else: # No invalid windows valid_windows = np.ones(dim_size, dtype=bool) self._valid_windows = DataArray(valid_windows, dims=(self.dim, ), coords=self.obj[self.dim].coords) self.window_indices = [slice(start, stop) for start, stop in zip(starts, stops)] def _center_result(self, result): """center result""" shift = (-self.window // 2) + 1 return result.shift(**{self.dim: shift}) def reduce(self, func, **kwargs): """Reduce the items in this group by applying `func` along some dimension(s). Parameters ---------- func : function Function which can be called in the form `func(x, **kwargs)` to return the result of collapsing an np.ndarray over an the rolling dimension. **kwargs : dict Additional keyword arguments passed on to `func`. Returns ------- reduced : DataArray Array with summarized data. """ windows = [window.reduce(func, dim=self.dim, **kwargs) for _, window in self] # Find valid windows based on count concat_dim = self.window_labels if self.dim in self.obj else self.dim counts = concat([window.count(dim=self.dim) for _, window in self], dim=concat_dim) result = concat(windows, dim=concat_dim) result = result.where(counts >= self._min_periods) if self.center: result = self._center_result(result) return result class DataArrayRolling(Rolling, ImplementsRollingArrayReduce): """Rolling object for DataArrays""" inject_bottleneck_rolling_methods(DataArrayRolling) ���������������������������������xarray-0.9.1/xarray/core/utils.py�������������������������������������������������������������������0000644�1311115�0011610�00000033334�13043511243�017101� 0����������������������������������������������������������������������������������������������������ustar �shoyer��������������������������eng�����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������"""Internal utilties; not for external use """ from __future__ import absolute_import from __future__ import division from __future__ import print_function import contextlib import functools import itertools import re import warnings from collections import Mapping, MutableMapping, Iterable import numpy as np import pandas as pd from . import ops from .pycompat import iteritems, OrderedDict, basestring, bytes_type def alias_message(old_name, new_name): return '%s has been deprecated. Use %s instead.' % (old_name, new_name) def alias_warning(old_name, new_name, stacklevel=3): warnings.warn(alias_message(old_name, new_name), FutureWarning, stacklevel=stacklevel) def alias(obj, old_name): assert isinstance(old_name, basestring) @functools.wraps(obj) def wrapper(*args, **kwargs): alias_warning(old_name, obj.__name__) return obj(*args, **kwargs) wrapper.__doc__ = alias_message(old_name, obj.__name__) return wrapper def safe_cast_to_index(array): """Given an array, safely cast it to a pandas.Index. If it is already a pandas.Index, return it unchanged. Unlike pandas.Index, if the array has dtype=object or dtype=timedelta64, this function will not attempt to do automatic type conversion but will always return an index with dtype=object. """ if isinstance(array, pd.Index): index = array elif hasattr(array, 'to_index'): index = array.to_index() else: kwargs = {} if hasattr(array, 'dtype') and array.dtype.kind == 'O': kwargs['dtype'] = object index = pd.Index(np.asarray(array), **kwargs) return index def multiindex_from_product_levels(levels, names=None): """Creating a MultiIndex from a product without refactorizing levels. Keeping levels the same is faster, and also gives back the original labels when we unstack. Parameters ---------- levels : sequence of arrays Unique labels for each level. names : optional sequence of objects Names for each level. Returns ------- pandas.MultiIndex """ labels_mesh = np.meshgrid(*[np.arange(len(lev)) for lev in levels], indexing='ij') labels = [x.ravel() for x in labels_mesh] return pd.MultiIndex(levels, labels, sortorder=0, names=names) def maybe_wrap_array(original, new_array): """Wrap a transformed array with __array_wrap__ is it can be done safely. This lets us treat arbitrary functions that take and return ndarray objects like ufuncs, as long as they return an array with the same shape. """ # in case func lost array's metadata if isinstance(new_array, np.ndarray) and new_array.shape == original.shape: return original.__array_wrap__(new_array) else: return new_array def equivalent(first, second): """Compare two objects for equivalence (identity or equality), using array_equiv if either object is an ndarray """ if isinstance(first, np.ndarray) or isinstance(second, np.ndarray): return ops.array_equiv(first, second) else: return (first is second or first == second or (pd.isnull(first) and pd.isnull(second))) def peek_at(iterable): """Returns the first value from iterable, as well as a new iterable with the same content as the original iterable """ gen = iter(iterable) peek = next(gen) return peek, itertools.chain([peek], gen) def update_safety_check(first_dict, second_dict, compat=equivalent): """Check the safety of updating one dictionary with another. Raises ValueError if dictionaries have non-compatible values for any key, where compatibility is determined by identity (they are the same item) or the `compat` function. Parameters ---------- first_dict, second_dict : dict-like All items in the second dictionary are checked against for conflicts against items in the first dictionary. compat : function, optional Binary operator to determine if two values are compatible. By default, checks for equivalence. """ for k, v in iteritems(second_dict): if k in first_dict and not compat(v, first_dict[k]): raise ValueError('unsafe to merge dictionaries without ' 'overriding values; conflicting key %r' % k) def remove_incompatible_items(first_dict, second_dict, compat=equivalent): """Remove incompatible items from the first dictionary in-place. Items are retained if their keys are found in both dictionaries and the values are compatible. Parameters ---------- first_dict, second_dict : dict-like Mappings to merge. compat : function, optional Binary operator to determine if two values are compatible. By default, checks for equivalence. """ for k in list(first_dict): if (k not in second_dict or (k in second_dict and not compat(first_dict[k], second_dict[k]))): del first_dict[k] def is_dict_like(value): return hasattr(value, 'keys') and hasattr(value, '__getitem__') def is_full_slice(value): return isinstance(value, slice) and value == slice(None) def combine_pos_and_kw_args(pos_kwargs, kw_kwargs, func_name): if pos_kwargs is not None: if not is_dict_like(pos_kwargs): raise ValueError('the first argument to .%s must be a dictionary' % func_name) if kw_kwargs: raise ValueError('cannot specify both keyword and positional ' 'arguments to .%s' % func_name) return pos_kwargs else: return kw_kwargs def is_scalar(value): """Whether to treat a value as a scalar. Any non-iterable, string, or 0-D array """ return ( getattr(value, 'ndim', None) == 0 or isinstance(value, (basestring, bytes_type)) or not isinstance(value, Iterable)) def is_valid_numpy_dtype(dtype): try: np.dtype(dtype) except (TypeError, ValueError): return False else: return True def to_0d_object_array(value): """Given a value, wrap it in a 0-D numpy.ndarray with dtype=object.""" result = np.empty((), dtype=object) result[()] = value return result def to_0d_array(value): """Given a value, wrap it in a 0-D numpy.ndarray.""" if np.isscalar(value) or (isinstance(value, np.ndarray) and value.ndim == 0): return np.array(value) else: return to_0d_object_array(value) def dict_equiv(first, second, compat=equivalent): """Test equivalence of two dict-like objects. If any of the values are numpy arrays, compare them correctly. Parameters ---------- first, second : dict-like Dictionaries to compare for equality compat : function, optional Binary operator to determine if two values are compatible. By default, checks for equivalence. Returns ------- equals : bool True if the dictionaries are equal """ for k in first: if k not in second or not compat(first[k], second[k]): return False for k in second: if k not in first: return False return True def ordered_dict_intersection(first_dict, second_dict, compat=equivalent): """Return the intersection of two dictionaries as a new OrderedDict. Items are retained if their keys are found in both dictionaries and the values are compatible. Parameters ---------- first_dict, second_dict : dict-like Mappings to merge. compat : function, optional Binary operator to determine if two values are compatible. By default, checks for equivalence. Returns ------- intersection : OrderedDict Intersection of the contents. """ new_dict = OrderedDict(first_dict) remove_incompatible_items(new_dict, second_dict, compat) return new_dict class SingleSlotPickleMixin(object): """Mixin class to add the ability to pickle objects whose state is defined by a single __slots__ attribute. Only necessary under Python 2. """ def __getstate__(self): return getattr(self, self.__slots__[0]) def __setstate__(self, state): setattr(self, self.__slots__[0], state) class Frozen(Mapping, SingleSlotPickleMixin): """Wrapper around an object implementing the mapping interface to make it immutable. If you really want to modify the mapping, the mutable version is saved under the `mapping` attribute. """ __slots__ = ['mapping'] def __init__(self, mapping): self.mapping = mapping def __getitem__(self, key): return self.mapping[key] def __iter__(self): return iter(self.mapping) def __len__(self): return len(self.mapping) def __contains__(self, key): return key in self.mapping def __repr__(self): return '%s(%r)' % (type(self).__name__, self.mapping) def FrozenOrderedDict(*args, **kwargs): return Frozen(OrderedDict(*args, **kwargs)) class SortedKeysDict(MutableMapping, SingleSlotPickleMixin): """An wrapper for dictionary-like objects that always iterates over its items in sorted order by key but is otherwise equivalent to the underlying mapping. """ __slots__ = ['mapping'] def __init__(self, mapping=None): self.mapping = {} if mapping is None else mapping def __getitem__(self, key): return self.mapping[key] def __setitem__(self, key, value): self.mapping[key] = value def __delitem__(self, key): del self.mapping[key] def __iter__(self): return iter(sorted(self.mapping)) def __len__(self): return len(self.mapping) def __contains__(self, key): return key in self.mapping def __repr__(self): return '%s(%r)' % (type(self).__name__, self.mapping) def copy(self): return type(self)(self.mapping.copy()) class ChainMap(MutableMapping, SingleSlotPickleMixin): """Partial backport of collections.ChainMap from Python>=3.3 Don't return this from any public APIs, since some of the public methods for a MutableMapping are missing (they will raise a NotImplementedError) """ __slots__ = ['maps'] def __init__(self, *maps): self.maps = maps def __getitem__(self, key): for mapping in self.maps: try: return mapping[key] except KeyError: pass raise KeyError(key) def __setitem__(self, key, value): self.maps[0][key] = value def __delitem__(self, value): # pragma: no cover raise NotImplementedError def __iter__(self): seen = set() for mapping in self.maps: for item in mapping: if item not in seen: yield item seen.add(item) def __len__(self): raise len(iter(self)) class NdimSizeLenMixin(object): """Mixin class that extends a class that defines a ``shape`` property to one that also defines ``ndim``, ``size`` and ``__len__``. """ @property def ndim(self): return len(self.shape) @property def size(self): # cast to int so that shape = () gives size = 1 return int(np.prod(self.shape)) def __len__(self): try: return self.shape[0] except IndexError: raise TypeError('len() of unsized object') class NDArrayMixin(NdimSizeLenMixin): """Mixin class for making wrappers of N-dimensional arrays that conform to the ndarray interface required for the data argument to Variable objects. A subclass should set the `array` property and override one or more of `dtype`, `shape` and `__getitem__`. """ @property def dtype(self): return self.array.dtype @property def shape(self): return self.array.shape def __array__(self, dtype=None): return np.asarray(self[...], dtype=dtype) def __getitem__(self, key): return self.array[key] def __repr__(self): return '%s(array=%r)' % (type(self).__name__, self.array) @contextlib.contextmanager def close_on_error(f): """Context manager to ensure that a file opened by xarray is closed if an exception is raised before the user sees the file object. """ try: yield except Exception: f.close() raise def is_remote_uri(path): return bool(re.search('^https?\://', path)) def is_uniform_spaced(arr, **kwargs): """Return True if values of an array are uniformly spaced and sorted. >>> is_uniform_spaced(range(5)) True >>> is_uniform_spaced([-4, 0, 100]) False kwargs are additional arguments to ``np.isclose`` """ arr = np.array(arr, dtype=float) diffs = np.diff(arr) return np.isclose(diffs.min(), diffs.max(), **kwargs) def hashable(v): """Determine whether `v` can be hashed.""" try: hash(v) except TypeError: return False return True def not_implemented(*args, **kwargs): return NotImplemented def decode_numpy_dict_values(attrs): """Convert attribute values from numpy objects to native Python objects, for use in to_dict""" attrs = dict(attrs) for k, v in attrs.items(): if isinstance(v, np.ndarray): attrs[k] = v.tolist() elif isinstance(v, np.generic): attrs[k] = np.asscalar(v) return attrs def ensure_us_time_resolution(val): """Convert val out of numpy time, for use in to_dict. Needed because of numpy bug GH#7619""" if np.issubdtype(val.dtype, np.datetime64): val = val.astype('datetime64[us]') elif np.issubdtype(val.dtype, np.timedelta64): val = val.astype('timedelta64[us]') return val ����������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������xarray-0.9.1/xarray/core/variable.py����������������������������������������������������������������0000644�1311115�0011610�00000146110�13043511243�017523� 0����������������������������������������������������������������������������������������������������ustar �shoyer��������������������������eng�����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������from __future__ import absolute_import from __future__ import division from __future__ import print_function from datetime import timedelta from collections import defaultdict import functools import itertools from distutils.version import LooseVersion import numpy as np import pandas as pd from . import common from . import indexing from . import ops from . import utils from . import nputils from .pycompat import basestring, OrderedDict, zip, dask_array_type from .indexing import (PandasIndexAdapter, orthogonally_indexable) import xarray as xr # only for Dataset and DataArray try: import dask.array as da except ImportError: pass def as_variable(obj, name=None, copy=False): """Convert an object into an Variable. - If the object is already an `Variable`, return a shallow copy. - Otherwise, if the object has 'dims' and 'data' attributes, convert it into a new `Variable`. - If all else fails, attempt to convert the object into an `Variable` by unpacking it into the arguments for `Variable.__init__`. """ # TODO: consider extending this method to automatically handle Iris and # pandas objects. if hasattr(obj, 'variable'): # extract the primary Variable from DataArrays obj = obj.variable if isinstance(obj, Variable): obj = obj.copy(deep=False) elif hasattr(obj, 'dims') and (hasattr(obj, 'data') or hasattr(obj, 'values')): obj = Variable(obj.dims, getattr(obj, 'data', obj.values), getattr(obj, 'attrs', None), getattr(obj, 'encoding', None)) elif isinstance(obj, tuple): try: obj = Variable(*obj) except TypeError: # use .format() instead of % because it handles tuples consistently raise TypeError('tuples to convert into variables must be of the ' 'form (dims, data[, attrs, encoding]): ' '{}'.format(obj)) elif utils.is_scalar(obj): obj = Variable([], obj) elif getattr(obj, 'name', None) is not None: obj = Variable(obj.name, obj) elif name is not None: data = as_compatible_data(obj) if data.ndim != 1: raise ValueError( 'cannot set variable %r with %r-dimensional data ' 'without explicit dimension names. Pass a tuple of ' '(dims, data) instead.' % (name, data.ndim)) obj = Variable(name, obj, fastpath=True) else: raise TypeError('unable to convert object into a variable without an ' 'explicit list of dimensions: %r' % obj) if name is not None and name in obj.dims: # convert the into an Index if obj.ndim != 1: raise ValueError( '%r has more than 1-dimension and the same name as one of its ' 'dimensions %r. xarray disallows such variables because they ' 'conflict with the coordinates used to label dimensions.' % (name, obj.dims)) obj = obj.to_index_variable() return obj def _maybe_wrap_data(data): """ Put pandas.Index and numpy.ndarray arguments in adapter objects to ensure they can be indexed properly. NumpyArrayAdapter, PandasIndexAdapter and LazilyIndexedArray should all pass through unmodified. """ if isinstance(data, pd.Index): return PandasIndexAdapter(data) return data def as_compatible_data(data, fastpath=False): """Prepare and wrap data to put in a Variable. - If data does not have the necessary attributes, convert it to ndarray. - If data has dtype=datetime64, ensure that it has ns precision. If it's a pandas.Timestamp, convert it to datetime64. - If data is already a pandas or xarray object (other than an Index), just use the values. Finally, wrap it up with an adapter if necessary. """ if fastpath and getattr(data, 'ndim', 0) > 0: # can't use fastpath (yet) for scalars return _maybe_wrap_data(data) if isinstance(data, Variable): return data.data # add a custom fast-path for dask.array to avoid expensive checks for the # dtype attribute if isinstance(data, dask_array_type): return data if isinstance(data, pd.Index): return _maybe_wrap_data(data) if isinstance(data, tuple): data = utils.to_0d_object_array(data) if isinstance(data, pd.Timestamp): # TODO: convert, handle datetime objects, too data = np.datetime64(data.value, 'ns') if isinstance(data, timedelta): data = np.timedelta64(getattr(data, 'value', data), 'ns') if (not hasattr(data, 'dtype') or not isinstance(data.dtype, np.dtype) or not hasattr(data, 'shape') or isinstance(data, (np.string_, np.unicode_, np.datetime64, np.timedelta64))): # data must be ndarray-like # don't allow non-numpy dtypes (e.g., categories) data = np.asarray(data) # we don't want nested self-described arrays data = getattr(data, 'values', data) if isinstance(data, np.ma.MaskedArray): mask = np.ma.getmaskarray(data) if mask.any(): dtype, fill_value = common._maybe_promote(data.dtype) data = np.asarray(data, dtype=dtype) data[mask] = fill_value else: data = np.asarray(data) if isinstance(data, np.ndarray): if data.dtype.kind == 'O': data = common._possibly_convert_objects(data) elif data.dtype.kind == 'M': data = np.asarray(data, 'datetime64[ns]') elif data.dtype.kind == 'm': data = np.asarray(data, 'timedelta64[ns]') return _maybe_wrap_data(data) def _as_array_or_item(data): """Return the given values as a numpy array, or as an individual item if it's a 0d datetime64 or timedelta64 array. Importantly, this function does not copy data if it is already an ndarray - otherwise, it will not be possible to update Variable values in place. This function mostly exists because 0-dimensional ndarrays with dtype=datetime64 are broken :( https://github.com/numpy/numpy/issues/4337 https://github.com/numpy/numpy/issues/7619 TODO: remove this (replace with np.asarray) once these issues are fixed """ data = np.asarray(data) if data.ndim == 0: if data.dtype.kind == 'M': data = np.datetime64(data, 'ns') elif data.dtype.kind == 'm': data = np.timedelta64(data, 'ns') return data class Variable(common.AbstractArray, utils.NdimSizeLenMixin): """A netcdf-like variable consisting of dimensions, data and attributes which describe a single Array. A single Variable object is not fully described outside the context of its parent Dataset (if you want such a fully described object, use a DataArray instead). The main functional difference between Variables and numpy arrays is that numerical operations on Variables implement array broadcasting by dimension name. For example, adding an Variable with dimensions `('time',)` to another Variable with dimensions `('space',)` results in a new Variable with dimensions `('time', 'space')`. Furthermore, numpy reduce operations like ``mean`` or ``sum`` are overwritten to take a "dimension" argument instead of an "axis". Variables are light-weight objects used as the building block for datasets. They are more primitive objects, so operations with them provide marginally higher performance than using DataArrays. However, manipulating data in the form of a Dataset or DataArray should almost always be preferred, because they can use more complete metadata in context of coordinate labels. """ def __init__(self, dims, data, attrs=None, encoding=None, fastpath=False): """ Parameters ---------- dims : str or sequence of str Name(s) of the the data dimension(s). Must be either a string (only for 1D data) or a sequence of strings with length equal to the number of dimensions. data : array_like Data array which supports numpy-like data access. attrs : dict_like or None, optional Attributes to assign to the new variable. If None (default), an empty attribute dictionary is initialized. encoding : dict_like or None, optional Dictionary specifying how to encode this array's data into a serialized format like netCDF4. Currently used keys (for netCDF) include '_FillValue', 'scale_factor', 'add_offset' and 'dtype'. Well-behaved code to serialize a Variable should ignore unrecognized encoding items. """ self._data = as_compatible_data(data, fastpath=fastpath) self._dims = self._parse_dimensions(dims) self._attrs = None self._encoding = None if attrs is not None: self.attrs = attrs if encoding is not None: self.encoding = encoding @property def dtype(self): return self._data.dtype @property def shape(self): return self._data.shape @property def nbytes(self): return self.size * self.dtype.itemsize @property def _in_memory(self): return (isinstance(self._data, (np.ndarray, PandasIndexAdapter)) or (isinstance(self._data, indexing.MemoryCachedArray) and isinstance(self._data.array, np.ndarray))) @property def data(self): if isinstance(self._data, dask_array_type): return self._data else: return self.values @data.setter def data(self, data): data = as_compatible_data(data) if data.shape != self.shape: raise ValueError( "replacement data must match the Variable's shape") self._data = data @property def _indexable_data(self): return orthogonally_indexable(self._data) def load(self): """Manually trigger loading of this variable's data from disk or a remote source into memory and return this variable. Normally, it should not be necessary to call this method in user code, because all xarray functions should either work on deferred data or load data automatically. """ if not isinstance(self._data, np.ndarray): self._data = np.asarray(self._data) return self def compute(self): """Manually trigger loading of this variable's data from disk or a remote source into memory and return a new variable. The original is left unaltered. Normally, it should not be necessary to call this method in user code, because all xarray functions should either work on deferred data or load data automatically. """ new = self.copy(deep=False) return new.load() @property def values(self): """The variable's data as a numpy.ndarray""" return _as_array_or_item(self._data) @values.setter def values(self, values): self.data = values def to_base_variable(self): """Return this variable as a base xarray.Variable""" return Variable(self.dims, self._data, self._attrs, encoding=self._encoding, fastpath=True) to_variable = utils.alias(to_base_variable, 'to_variable') def to_index_variable(self): """Return this variable as an xarray.IndexVariable""" return IndexVariable(self.dims, self._data, self._attrs, encoding=self._encoding, fastpath=True) to_coord = utils.alias(to_index_variable, 'to_coord') def to_index(self): """Convert this variable to a pandas.Index""" return self.to_index_variable().to_index() @property def dims(self): """Tuple of dimension names with which this variable is associated. """ return self._dims def _parse_dimensions(self, dims): if isinstance(dims, basestring): dims = (dims,) dims = tuple(dims) if len(dims) != self.ndim: raise ValueError('dimensions %s must have the same length as the ' 'number of data dimensions, ndim=%s' % (dims, self.ndim)) return dims @dims.setter def dims(self, value): self._dims = self._parse_dimensions(value) def _item_key_to_tuple(self, key): if utils.is_dict_like(key): return tuple(key.get(dim, slice(None)) for dim in self.dims) else: return key def __getitem__(self, key): """Return a new Array object whose contents are consistent with getting the provided key from the underlying data. NB. __getitem__ and __setitem__ implement "orthogonal indexing" like netCDF4-python, where the key can only include integers, slices (including `Ellipsis`) and 1d arrays, each of which are applied orthogonally along their respective dimensions. The difference does not matter in most cases unless you are using numpy's "fancy indexing," which can otherwise result in data arrays whose shapes is inconsistent (or just uninterpretable with) with the variable's dimensions. If you really want to do indexing like `x[x > 0]`, manipulate the numpy array `x.values` directly. """ key = self._item_key_to_tuple(key) key = indexing.expanded_indexer(key, self.ndim) dims = tuple(dim for k, dim in zip(key, self.dims) if not isinstance(k, (int, np.integer))) values = self._indexable_data[key] # orthogonal indexing should ensure the dimensionality is consistent if hasattr(values, 'ndim'): assert values.ndim == len(dims), (values.ndim, len(dims)) else: assert len(dims) == 0, len(dims) return type(self)(dims, values, self._attrs, self._encoding, fastpath=True) def __setitem__(self, key, value): """__setitem__ is overloaded to access the underlying numpy values with orthogonal indexing. See __getitem__ for more details. """ key = self._item_key_to_tuple(key) if isinstance(self._data, dask_array_type): raise TypeError("this variable's data is stored in a dask array, " 'which does not support item assignment. To ' 'assign to this variable, you must first load it ' 'into memory explicitly using the .load_data() ' 'method or accessing its .values attribute.') data = orthogonally_indexable(self._data) data[key] = value @property def attrs(self): """Dictionary of local attributes on this variable. """ if self._attrs is None: self._attrs = OrderedDict() return self._attrs @attrs.setter def attrs(self, value): self._attrs = OrderedDict(value) @property def encoding(self): """Dictionary of encodings on this variable. """ if self._encoding is None: self._encoding = {} return self._encoding @encoding.setter def encoding(self, value): try: self._encoding = dict(value) except ValueError: raise ValueError('encoding must be castable to a dictionary') def copy(self, deep=True): """Returns a copy of this object. If `deep=True`, the data array is loaded into memory and copied onto the new object. Dimensions, attributes and encodings are always copied. """ data = self._data if isinstance(data, indexing.MemoryCachedArray): # don't share caching between copies data = indexing.MemoryCachedArray(data.array) if deep: if isinstance(data, dask_array_type): data = data.copy() elif not isinstance(data, PandasIndexAdapter): # pandas.Index is immutable data = np.array(data) # note: # dims is already an immutable tuple # attributes and encoding will be copied when the new Array is created return type(self)(self.dims, data, self._attrs, self._encoding, fastpath=True) def __copy__(self): return self.copy(deep=False) def __deepcopy__(self, memo=None): # memo does nothing but is required for compatibility with # copy.deepcopy return self.copy(deep=True) # mutable objects should not be hashable __hash__ = None @property def chunks(self): """Block dimensions for this array's data or None if it's not a dask array. """ return getattr(self._data, 'chunks', None) _array_counter = itertools.count() def chunk(self, chunks=None, name=None, lock=False): """Coerce this array's data into a dask arrays with the given chunks. If this variable is a non-dask array, it will be converted to dask array. If it's a dask array, it will be rechunked to the given chunk sizes. If neither chunks is not provided for one or more dimensions, chunk sizes along that dimension will not be updated; non-dask arrays will be converted into dask arrays with a single block. Parameters ---------- chunks : int, tuple or dict, optional Chunk sizes along each dimension, e.g., ``5``, ``(5, 5)`` or ``{'x': 5, 'y': 5}``. name : str, optional Used to generate the name for this array in the internal dask graph. Does not need not be unique. lock : optional Passed on to :py:func:`dask.array.from_array`, if the array is not already as dask array. Returns ------- chunked : xarray.Variable """ import dask.array as da if utils.is_dict_like(chunks): chunks = dict((self.get_axis_num(dim), chunk) for dim, chunk in chunks.items()) if chunks is None: chunks = self.chunks or self.shape data = self._data if isinstance(data, da.Array): data = data.rechunk(chunks) else: if utils.is_dict_like(chunks): chunks = tuple(chunks.get(n, s) for n, s in enumerate(self.shape)) data = da.from_array(data, chunks, name=name, lock=lock) return type(self)(self.dims, data, self._attrs, self._encoding, fastpath=True) def isel(self, **indexers): """Return a new array indexed along the specified dimension(s). Parameters ---------- **indexers : {dim: indexer, ...} Keyword arguments with names matching dimensions and values given by integers, slice objects or arrays. Returns ------- obj : Array object A new Array with the selected data and dimensions. In general, the new variable's data will be a view of this variable's data, unless numpy fancy indexing was triggered by using an array indexer, in which case the data will be a copy. """ invalid = [k for k in indexers if k not in self.dims] if invalid: raise ValueError("dimensions %r do not exist" % invalid) key = [slice(None)] * self.ndim for i, dim in enumerate(self.dims): if dim in indexers: key[i] = indexers[dim] return self[tuple(key)] def squeeze(self, dim=None): """Return a new object with squeezed data. Parameters ---------- dim : None or str or tuple of str, optional Selects a subset of the length one dimensions. If a dimension is selected with length greater than one, an error is raised. If None, all length one dimensions are squeezed. Returns ------- squeezed : same type as caller This object, but with with all or a subset of the dimensions of length 1 removed. See Also -------- numpy.squeeze """ dims = common.get_squeeze_dims(self, dim) return self.isel(**{d: 0 for d in dims}) def _shift_one_dim(self, dim, count): axis = self.get_axis_num(dim) if count > 0: keep = slice(None, -count) elif count < 0: keep = slice(-count, None) else: keep = slice(None) trimmed_data = self[(slice(None),) * axis + (keep,)].data dtype, fill_value = common._maybe_promote(self.dtype) shape = list(self.shape) shape[axis] = min(abs(count), shape[axis]) if isinstance(trimmed_data, dask_array_type): chunks = list(trimmed_data.chunks) chunks[axis] = (shape[axis],) full = functools.partial(da.full, chunks=chunks) else: full = np.full nans = full(shape, fill_value, dtype=dtype) if count > 0: arrays = [nans, trimmed_data] else: arrays = [trimmed_data, nans] data = ops.concatenate(arrays, axis) if isinstance(data, dask_array_type): # chunked data should come out with the same chunks; this makes # it feasible to combine shifted and unshifted data # TODO: remove this once dask.array automatically aligns chunks data = data.rechunk(self.data.chunks) return type(self)(self.dims, data, self._attrs, fastpath=True) def shift(self, **shifts): """ Return a new Variable with shifted data. Parameters ---------- **shifts : keyword arguments of the form {dim: offset} Integer offset to shift along each of the given dimensions. Positive offsets shift to the right; negative offsets shift to the left. Returns ------- shifted : Variable Variable with the same dimensions and attributes but shifted data. """ result = self for dim, count in shifts.items(): result = result._shift_one_dim(dim, count) return result def _roll_one_dim(self, dim, count): axis = self.get_axis_num(dim) count %= self.shape[axis] if count != 0: indices = [slice(-count, None), slice(None, -count)] else: indices = [slice(None)] arrays = [self[(slice(None),) * axis + (idx,)].data for idx in indices] data = ops.concatenate(arrays, axis) if isinstance(data, dask_array_type): # chunked data should come out with the same chunks; this makes # it feasible to combine shifted and unshifted data # TODO: remove this once dask.array automatically aligns chunks data = data.rechunk(self.data.chunks) return type(self)(self.dims, data, self._attrs, fastpath=True) def roll(self, **shifts): """ Return a new Variable with rolld data. Parameters ---------- **shifts : keyword arguments of the form {dim: offset} Integer offset to roll along each of the given dimensions. Positive offsets roll to the right; negative offsets roll to the left. Returns ------- shifted : Variable Variable with the same dimensions and attributes but rolled data. """ result = self for dim, count in shifts.items(): result = result._roll_one_dim(dim, count) return result def transpose(self, *dims): """Return a new Variable object with transposed dimensions. Parameters ---------- *dims : str, optional By default, reverse the dimensions. Otherwise, reorder the dimensions to this order. Returns ------- transposed : Variable The returned object has transposed data and dimensions with the same attributes as the original. Notes ----- Although this operation returns a view of this variable's data, it is not lazy -- the data will be fully loaded. See Also -------- numpy.transpose """ if len(dims) == 0: dims = self.dims[::-1] axes = self.get_axis_num(dims) if len(dims) < 2: # no need to transpose if only one dimension return self.copy(deep=False) data = ops.transpose(self.data, axes) return type(self)(dims, data, self._attrs, self._encoding, fastpath=True) def expand_dims(self, dims, shape=None): """Return a new variable with expanded dimensions. When possible, this operation does not copy this variable's data. Parameters ---------- dims : str or sequence of str or dict Dimensions to include on the new variable. If a dict, values are used to provide the sizes of new dimensions; otherwise, new dimensions are inserted with length 1. Returns ------- Variable """ if isinstance(dims, basestring): dims = [dims] if shape is None and utils.is_dict_like(dims): shape = dims.values() missing_dims = set(self.dims) - set(dims) if missing_dims: raise ValueError('new dimensions must be a superset of existing ' 'dimensions') self_dims = set(self.dims) expanded_dims = tuple( d for d in dims if d not in self_dims) + self.dims if self.dims == expanded_dims: # don't use broadcast_to unless necessary so the result remains # writeable if possible expanded_data = self.data elif shape is not None: dims_map = dict(zip(dims, shape)) tmp_shape = tuple(dims_map[d] for d in expanded_dims) expanded_data = ops.broadcast_to(self.data, tmp_shape) else: expanded_data = self.data[ (None,) * (len(expanded_dims) - self.ndim)] expanded_var = Variable(expanded_dims, expanded_data, self._attrs, self._encoding, fastpath=True) return expanded_var.transpose(*dims) def _stack_once(self, dims, new_dim): if not set(dims) <= set(self.dims): raise ValueError('invalid existing dimensions: %s' % dims) if new_dim in self.dims: raise ValueError('cannot create a new dimension with the same ' 'name as an existing dimension') if len(dims) == 0: # don't stack return self.copy(deep=False) other_dims = [d for d in self.dims if d not in dims] dim_order = other_dims + list(dims) reordered = self.transpose(*dim_order) new_shape = reordered.shape[:len(other_dims)] + (-1,) new_data = reordered.data.reshape(new_shape) new_dims = reordered.dims[:len(other_dims)] + (new_dim,) return Variable(new_dims, new_data, self._attrs, self._encoding, fastpath=True) def stack(self, **dimensions): """ Stack any number of existing dimensions into a single new dimension. New dimensions will be added at the end, and the order of the data along each new dimension will be in contiguous (C) order. Parameters ---------- **dimensions : keyword arguments of the form new_name=(dim1, dim2, ...) Names of new dimensions, and the existing dimensions that they replace. Returns ------- stacked : Variable Variable with the same attributes but stacked data. See also -------- Variable.unstack """ result = self for new_dim, dims in dimensions.items(): result = result._stack_once(dims, new_dim) return result def _unstack_once(self, dims, old_dim): new_dim_names = tuple(dims.keys()) new_dim_sizes = tuple(dims.values()) if old_dim not in self.dims: raise ValueError('invalid existing dimension: %s' % old_dim) if set(new_dim_names).intersection(self.dims): raise ValueError('cannot create a new dimension with the same ' 'name as an existing dimension') if np.prod(new_dim_sizes) != self.sizes[old_dim]: raise ValueError('the product of the new dimension sizes must ' 'equal the size of the old dimension') other_dims = [d for d in self.dims if d != old_dim] dim_order = other_dims + [old_dim] reordered = self.transpose(*dim_order) new_shape = reordered.shape[:len(other_dims)] + new_dim_sizes new_data = reordered.data.reshape(new_shape) new_dims = reordered.dims[:len(other_dims)] + new_dim_names return Variable(new_dims, new_data, self._attrs, self._encoding, fastpath=True) def unstack(self, **dimensions): """ Unstack an existing dimension into multiple new dimensions. New dimensions will be added at the end, and the order of the data along each new dimension will be in contiguous (C) order. Parameters ---------- **dimensions : keyword arguments of the form old_dim={dim1: size1, ...} Names of existing dimensions, and the new dimensions and sizes that they map to. Returns ------- unstacked : Variable Variable with the same attributes but unstacked data. See also -------- Variable.stack """ result = self for old_dim, dims in dimensions.items(): result = result._unstack_once(dims, old_dim) return result def fillna(self, value): return ops.fillna(self, value) def where(self, cond): return self._where(cond) def reduce(self, func, dim=None, axis=None, keep_attrs=False, allow_lazy=False, **kwargs): """Reduce this array by applying `func` along some dimension(s). Parameters ---------- func : function Function which can be called in the form `func(x, axis=axis, **kwargs)` to return the result of reducing an np.ndarray over an integer valued axis. dim : str or sequence of str, optional Dimension(s) over which to apply `func`. axis : int or sequence of int, optional Axis(es) over which to apply `func`. Only one of the 'dim' and 'axis' arguments can be supplied. If neither are supplied, then the reduction is calculated over the flattened array (by calling `func(x)` without an axis argument). keep_attrs : bool, optional If True, the variable's attributes (`attrs`) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes. **kwargs : dict Additional keyword arguments passed on to `func`. Returns ------- reduced : Array Array with summarized data and the indicated dimension(s) removed. """ if dim is not None and axis is not None: raise ValueError("cannot supply both 'axis' and 'dim' arguments") if getattr(func, 'keep_dims', False): if dim is None and axis is None: raise ValueError("must supply either single 'dim' or 'axis' argument to %s" % (func.__name__)) if dim is not None: axis = self.get_axis_num(dim) data = func(self.data if allow_lazy else self.values, axis=axis, **kwargs) if getattr(data, 'shape', ()) == self.shape: dims = self.dims else: removed_axes = (range(self.ndim) if axis is None else np.atleast_1d(axis) % self.ndim) dims = [adim for n, adim in enumerate(self.dims) if n not in removed_axes] attrs = self._attrs if keep_attrs else None return Variable(dims, data, attrs=attrs) @classmethod def concat(cls, variables, dim='concat_dim', positions=None, shortcut=False): """Concatenate variables along a new or existing dimension. Parameters ---------- variables : iterable of Array Arrays to stack together. Each variable is expected to have matching dimensions and shape except for along the stacked dimension. dim : str or DataArray, optional Name of the dimension to stack along. This can either be a new dimension name, in which case it is added along axis=0, or an existing dimension name, in which case the location of the dimension is unchanged. Where to insert the new dimension is determined by the first variable. positions : None or list of integer arrays, optional List of integer arrays which specifies the integer positions to which to assign each dataset along the concatenated dimension. If not supplied, objects are concatenated in the provided order. shortcut : bool, optional This option is used internally to speed-up groupby operations. If `shortcut` is True, some checks of internal consistency between arrays to concatenate are skipped. Returns ------- stacked : Variable Concatenated Variable formed by stacking all the supplied variables along the given dimension. """ if not isinstance(dim, basestring): dim, = dim.dims # can't do this lazily: we need to loop through variables at least # twice variables = list(variables) first_var = variables[0] arrays = [v.data for v in variables] # TODO: use our own type promotion rules to ensure that # [str, float] -> object, not str like numpy if dim in first_var.dims: axis = first_var.get_axis_num(dim) dims = first_var.dims data = ops.concatenate(arrays, axis=axis) if positions is not None: # TODO: deprecate this option -- we don't need it for groupby # any more. indices = nputils.inverse_permutation(np.concatenate(positions)) data = ops.take(data, indices, axis=axis) else: axis = 0 dims = (dim,) + first_var.dims data = ops.stack(arrays, axis=axis) attrs = OrderedDict(first_var.attrs) if not shortcut: for var in variables: if var.dims != first_var.dims: raise ValueError('inconsistent dimensions') utils.remove_incompatible_items(attrs, var.attrs) return cls(dims, data, attrs) def equals(self, other, equiv=ops.array_equiv): """True if two Variables have the same dimensions and values; otherwise False. Variables can still be equal (like pandas objects) if they have NaN values in the same locations. This method is necessary because `v1 == v2` for Variables does element-wise comparisons (like numpy.ndarrays). """ other = getattr(other, 'variable', other) try: return (self.dims == other.dims and (self._data is other._data or equiv(self.data, other.data))) except (TypeError, AttributeError): return False def broadcast_equals(self, other, equiv=ops.array_equiv): """True if two Variables have the values after being broadcast against each other; otherwise False. Variables can still be equal (like pandas objects) if they have NaN values in the same locations. """ try: self, other = broadcast_variables(self, other) except (ValueError, AttributeError): return False return self.equals(other, equiv=equiv) def identical(self, other): """Like equals, but also checks attributes. """ try: return (utils.dict_equiv(self.attrs, other.attrs) and self.equals(other)) except (TypeError, AttributeError): return False def no_conflicts(self, other): """True if the intersection of two Variable's non-null data is equal; otherwise false. Variables can thus still be equal if there are locations where either, or both, contain NaN values. """ return self.broadcast_equals(other, equiv=ops.array_notnull_equiv) def quantile(self, q, dim=None, interpolation='linear'): """Compute the qth quantile of the data along the specified dimension. Returns the qth quantiles(s) of the array elements. Parameters ---------- q : float in range of [0,1] (or sequence of floats) Quantile to compute, which must be between 0 and 1 inclusive. dim : str or sequence of str, optional Dimension(s) over which to apply quantile. interpolation : {'linear', 'lower', 'higher', 'midpoint', 'nearest'} This optional parameter specifies the interpolation method to use when the desired quantile lies between two data points ``i < j``: * linear: ``i + (j - i) * fraction``, where ``fraction`` is the fractional part of the index surrounded by ``i`` and ``j``. * lower: ``i``. * higher: ``j``. * nearest: ``i`` or ``j``, whichever is nearest. * midpoint: ``(i + j) / 2``. Returns ------- quantiles : Variable If `q` is a single quantile, then the result is a scalar. If multiple percentiles are given, first axis of the result corresponds to the quantile and a quantile dimension is added to the return array. The other dimensions are the dimensions that remain after the reduction of the array. See Also -------- numpy.nanpercentile, pandas.Series.quantile, Dataset.quantile, DataArray.quantile """ if isinstance(self.data, dask_array_type): TypeError("quantile does not work for arrays stored as dask " "arrays. Load the data via .compute() or .load() prior " "to calling this method.") if LooseVersion(np.__version__) < LooseVersion('1.10.0'): raise NotImplementedError( 'quantile requres numpy version 1.10.0 or later') q = np.asarray(q, dtype=np.float64) new_dims = list(self.dims) if dim is not None: axis = self.get_axis_num(dim) if utils.is_scalar(dim): new_dims.remove(dim) else: for d in dim: new_dims.remove(d) else: axis = None new_dims = [] # only add the quantile dimension if q is array like if q.ndim != 0: new_dims = ['quantile'] + new_dims qs = np.nanpercentile(self.data, q * 100., axis=axis, interpolation=interpolation) return Variable(new_dims, qs) @property def real(self): return type(self)(self.dims, self.data.real, self._attrs) @property def imag(self): return type(self)(self.dims, self.data.imag, self._attrs) def __array_wrap__(self, obj, context=None): return Variable(self.dims, obj) @staticmethod def _unary_op(f): @functools.wraps(f) def func(self, *args, **kwargs): return self.__array_wrap__(f(self.data, *args, **kwargs)) return func @staticmethod def _binary_op(f, reflexive=False, **ignored_kwargs): @functools.wraps(f) def func(self, other): if isinstance(other, (xr.DataArray, xr.Dataset)): return NotImplemented self_data, other_data, dims = _broadcast_compat_data(self, other) new_data = (f(self_data, other_data) if not reflexive else f(other_data, self_data)) result = Variable(dims, new_data) return result return func @staticmethod def _inplace_binary_op(f): @functools.wraps(f) def func(self, other): if isinstance(other, xr.Dataset): raise TypeError('cannot add a Dataset to a Variable in-place') self_data, other_data, dims = _broadcast_compat_data(self, other) if dims != self.dims: raise ValueError('dimensions cannot change for in-place ' 'operations') self.values = f(self_data, other_data) return self return func ops.inject_all_ops_and_reduce_methods(Variable) class IndexVariable(Variable): """Wrapper for accommodating a pandas.Index in an xarray.Variable. IndexVariable preserve loaded values in the form of a pandas.Index instead of a NumPy array. Hence, their values are immutable and must always be one- dimensional. They also have a name property, which is the name of their sole dimension unless another name is given. """ def __init__(self, dims, data, attrs=None, encoding=None, fastpath=False): super(IndexVariable, self).__init__(dims, data, attrs, encoding, fastpath) if self.ndim != 1: raise ValueError('%s objects must be 1-dimensional' % type(self).__name__) # Unlike in Variable, always eagerly load values into memory if not isinstance(self._data, PandasIndexAdapter): self._data = PandasIndexAdapter(self._data) def load(self): # data is already loaded into memory for IndexVariable return self @Variable.data.setter def data(self, data): Variable.data.fset(self, data) if not isinstance(self._data, PandasIndexAdapter): self._data = PandasIndexAdapter(self._data) def chunk(self, chunks=None, name=None, lock=False): # Dummy - do not chunk. This method is invoked e.g. by Dataset.chunk() return self.copy(deep=False) def __getitem__(self, key): key = self._item_key_to_tuple(key) values = self._indexable_data[key] if not hasattr(values, 'ndim') or values.ndim == 0: return Variable((), values, self._attrs, self._encoding) else: return type(self)(self.dims, values, self._attrs, self._encoding, fastpath=True) def __setitem__(self, key, value): raise TypeError('%s values cannot be modified' % type(self).__name__) @classmethod def concat(cls, variables, dim='concat_dim', positions=None, shortcut=False): """Specialized version of Variable.concat for IndexVariable objects. This exists because we want to avoid converting Index objects to NumPy arrays, if possible. """ if not isinstance(dim, basestring): dim, = dim.dims variables = list(variables) first_var = variables[0] if any(not isinstance(v, cls) for v in variables): raise TypeError('IndexVariable.concat requires that all input ' 'variables be IndexVariable objects') indexes = [v._data.array for v in variables] if not indexes: data = [] else: data = indexes[0].append(indexes[1:]) if positions is not None: indices = nputils.inverse_permutation( np.concatenate(positions)) data = data.take(indices) attrs = OrderedDict(first_var.attrs) if not shortcut: for var in variables: if var.dims != first_var.dims: raise ValueError('inconsistent dimensions') utils.remove_incompatible_items(attrs, var.attrs) return cls(first_var.dims, data, attrs) def copy(self, deep=True): """Returns a copy of this object. `deep` is ignored since data is stored in the form of pandas.Index, which is already immutable. Dimensions, attributes and encodings are always copied. """ return type(self)(self.dims, self._data, self._attrs, self._encoding, fastpath=True) def _data_equals(self, other): return self.to_index().equals(other.to_index()) def to_index_variable(self): """Return this variable as an xarray.IndexVariable""" return self to_coord = utils.alias(to_index_variable, 'to_coord') def to_index(self): """Convert this variable to a pandas.Index""" # n.b. creating a new pandas.Index from an old pandas.Index is # basically free as pandas.Index objects are immutable assert self.ndim == 1 index = self._data.array if isinstance(index, pd.MultiIndex): # set default names for multi-index unnamed levels so that # we can safely rename dimension / coordinate later valid_level_names = [name or '{}_level_{}'.format(self.dims[0], i) for i, name in enumerate(index.names)] index = index.set_names(valid_level_names) else: index = index.set_names(self.name) return index @property def level_names(self): """Return MultiIndex level names or None if this IndexVariable has no MultiIndex. """ index = self.to_index() if isinstance(index, pd.MultiIndex): return index.names else: return None def get_level_variable(self, level): """Return a new IndexVariable from a given MultiIndex level.""" if self.level_names is None: raise ValueError("IndexVariable %r has no MultiIndex" % self.name) index = self.to_index() return type(self)(self.dims, index.get_level_values(level)) @property def name(self): return self.dims[0] @name.setter def name(self, value): raise AttributeError('cannot modify name of IndexVariable in-place') # for backwards compatibility Coordinate = utils.alias(IndexVariable, 'Coordinate') def _unified_dims(variables): # validate dimensions all_dims = OrderedDict() for var in variables: var_dims = var.dims if len(set(var_dims)) < len(var_dims): raise ValueError('broadcasting cannot handle duplicate ' 'dimensions: %r' % list(var_dims)) for d, s in zip(var_dims, var.shape): if d not in all_dims: all_dims[d] = s elif all_dims[d] != s: raise ValueError('operands cannot be broadcast together ' 'with mismatched lengths for dimension %r: %s' % (d, (all_dims[d], s))) return all_dims def _broadcast_compat_variables(*variables): dims = tuple(_unified_dims(variables)) return tuple(var.expand_dims(dims) if var.dims != dims else var for var in variables) def broadcast_variables(*variables): """Given any number of variables, return variables with matching dimensions and broadcast data. The data on the returned variables will be a view of the data on the corresponding original arrays, but dimensions will be reordered and inserted so that both broadcast arrays have the same dimensions. The new dimensions are sorted in order of appearance in the first variable's dimensions followed by the second variable's dimensions. """ dims_map = _unified_dims(variables) dims_tuple = tuple(dims_map) return tuple(var.expand_dims(dims_map) if var.dims != dims_tuple else var for var in variables) def _broadcast_compat_data(self, other): if all(hasattr(other, attr) for attr in ['dims', 'data', 'shape', 'encoding']): # `other` satisfies the necessary Variable API for broadcast_variables new_self, new_other = _broadcast_compat_variables(self, other) self_data = new_self.data other_data = new_other.data dims = new_self.dims else: # rely on numpy broadcasting rules self_data = self.data other_data = other dims = self.dims return self_data, other_data, dims def concat(variables, dim='concat_dim', positions=None, shortcut=False): """Concatenate variables along a new or existing dimension. Parameters ---------- variables : iterable of Array Arrays to stack together. Each variable is expected to have matching dimensions and shape except for along the stacked dimension. dim : str or DataArray, optional Name of the dimension to stack along. This can either be a new dimension name, in which case it is added along axis=0, or an existing dimension name, in which case the location of the dimension is unchanged. Where to insert the new dimension is determined by the first variable. positions : None or list of integer arrays, optional List of integer arrays which specifies the integer positions to which to assign each dataset along the concatenated dimension. If not supplied, objects are concatenated in the provided order. shortcut : bool, optional This option is used internally to speed-up groupby operations. If `shortcut` is True, some checks of internal consistency between arrays to concatenate are skipped. Returns ------- stacked : Variable Concatenated Variable formed by stacking all the supplied variables along the given dimension. """ variables = list(variables) if all(isinstance(v, IndexVariable) for v in variables): return IndexVariable.concat(variables, dim, positions, shortcut) else: return Variable.concat(variables, dim, positions, shortcut) def assert_unique_multiindex_level_names(variables): """Check for uniqueness of MultiIndex level names in all given variables. Not public API. Used for checking consistency of DataArray and Dataset objects. """ level_names = defaultdict(list) for var_name, var in variables.items(): if isinstance(var._data, PandasIndexAdapter): idx_level_names = var.to_index_variable().level_names if idx_level_names is not None: for n in idx_level_names: level_names[n].append('%r (%s)' % (n, var_name)) for k, v in level_names.items(): if k in variables: v.append('(%s)' % k) duplicate_names = [v for v in level_names.values() if len(v) > 1] if duplicate_names: conflict_str = '\n'.join([', '.join(v) for v in duplicate_names]) raise ValueError('conflicting MultiIndex level name(s):\n%s' % conflict_str) 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����������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������xarray-0.9.1/xarray/plot/default_colormap.csv�������������������������������������������������������0000644�1311115�0011610�00000020255�12652753364�021471� 0����������������������������������������������������������������������������������������������������ustar �shoyer��������������������������eng�����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������0.26700401,0.00487433,0.32941519 0.26851048,0.00960483,0.33542652 0.26994384,0.01462494,0.34137895 0.27130489,0.01994186,0.34726862 0.27259384,0.02556309,0.35309303 0.27380934,0.03149748,0.35885256 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0.59583934,0.84871722,0.24332878 0.60604528,0.8507331,0.23671214 0.61629283,0.85270912,0.23005179 0.62657923,0.85464543,0.22335258 0.63690157,0.85654226,0.21662012 0.64725685,0.85839991,0.20986086 0.65764197,0.86021878,0.20308229 0.66805369,0.86199932,0.19629307 0.67848868,0.86374211,0.18950326 0.68894351,0.86544779,0.18272455 0.69941463,0.86711711,0.17597055 0.70989842,0.86875092,0.16925712 0.72039115,0.87035015,0.16260273 0.73088902,0.87191584,0.15602894 0.74138803,0.87344918,0.14956101 0.75188414,0.87495143,0.14322828 0.76237342,0.87642392,0.13706449 0.77285183,0.87786808,0.13110864 0.78331535,0.87928545,0.12540538 0.79375994,0.88067763,0.12000532 0.80418159,0.88204632,0.11496505 0.81457634,0.88339329,0.11034678 0.82494028,0.88472036,0.10621724 0.83526959,0.88602943,0.1026459 0.84556056,0.88732243,0.09970219 0.8558096,0.88860134,0.09745186 0.86601325,0.88986815,0.09595277 0.87616824,0.89112487,0.09525046 0.88627146,0.89237353,0.09537439 0.89632002,0.89361614,0.09633538 0.90631121,0.89485467,0.09812496 0.91624212,0.89609127,0.1007168 0.92610579,0.89732977,0.10407067 0.93590444,0.8985704,0.10813094 0.94563626,0.899815,0.11283773 0.95529972,0.90106534,0.11812832 0.96489353,0.90232311,0.12394051 0.97441665,0.90358991,0.13021494 0.98386829,0.90486726,0.13689671 0.99324789,0.90615657,0.1439362 ���������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������xarray-0.9.1/xarray/plot/facetgrid.py���������������������������������������������������������������0000644�1311115�0011610�00000035127�13043511243�017721� 0����������������������������������������������������������������������������������������������������ustar �shoyer��������������������������eng�����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������from __future__ import absolute_import from __future__ import division from __future__ import print_function import inspect import warnings import itertools import functools import numpy as np from ..core.formatting import format_item from .utils import _determine_cmap_params, _infer_xy_labels # Overrides axes.labelsize, xtick.major.size, ytick.major.size # from mpl.rcParams _FONTSIZE = 'small' # For major ticks on x, y axes _NTICKS = 5 def _nicetitle(coord, value, maxchar, template): """ Put coord, value in template and truncate at maxchar """ prettyvalue = format_item(value, quote_strings=False) title = template.format(coord=coord, value=prettyvalue) if len(title) > maxchar: title = title[:(maxchar - 3)] + '...' return title class FacetGrid(object): """ Initialize the matplotlib figure and FacetGrid object. The :class:`FacetGrid` is an object that links a xarray DataArray to a matplotlib figure with a particular structure. In particular, :class:`FacetGrid` is used to draw plots with multiple Axes where each Axes shows the same relationship conditioned on different levels of some dimension. It's possible to condition on up to two variables by assigning variables to the rows and columns of the grid. The general approach to plotting here is called "small multiples", where the same kind of plot is repeated multiple times, and the specific use of small multiples to display the same relationship conditioned on one ore more other variables is often called a "trellis plot". The basic workflow is to initialize the :class:`FacetGrid` object with the DataArray and the variable names that are used to structure the grid. Then plotting functions can be applied to each subset by calling :meth:`FacetGrid.map_dataarray` or :meth:`FacetGrid.map`. Attributes ---------- axes : numpy object array Contains axes in corresponding position, as returned from plt.subplots fig : matplotlib.Figure The figure containing all the axes name_dicts : numpy object array Contains dictionaries mapping coordinate names to values. None is used as a sentinel value for axes which should remain empty, ie. sometimes the bottom right grid """ def __init__(self, data, col=None, row=None, col_wrap=None, sharex=True, sharey=True, figsize=None, aspect=1, size=3, subplot_kws=None): """ Parameters ---------- data : DataArray xarray DataArray to be plotted row, col : strings Dimesion names that define subsets of the data, which will be drawn on separate facets in the grid. col_wrap : int, optional "Wrap" the column variable at this width, so that the column facets sharex : bool, optional If true, the facets will share x axes sharey : bool, optional If true, the facets will share y axes figsize : tuple, optional A tuple (width, height) of the figure in inches. If set, overrides ``size`` and ``aspect``. aspect : scalar, optional Aspect ratio of each facet, so that ``aspect * size`` gives the width of each facet in inches size : scalar, optional Height (in inches) of each facet. See also: ``aspect`` subplot_kws : dict, optional Dictionary of keyword arguments for matplotlib subplots """ import matplotlib.pyplot as plt # Handle corner case of nonunique coordinates rep_col = col is not None and not data[col].to_index().is_unique rep_row = row is not None and not data[row].to_index().is_unique if rep_col or rep_row: raise ValueError('Coordinates used for faceting cannot ' 'contain repeated (nonunique) values.') # single_group is the grouping variable, if there is exactly one if col and row: single_group = False nrow = len(data[row]) ncol = len(data[col]) nfacet = nrow * ncol if col_wrap is not None: warnings.warn('Ignoring col_wrap since both col and row ' 'were passed') elif row and not col: single_group = row elif not row and col: single_group = col else: raise ValueError( 'Pass a coordinate name as an argument for row or col') # Compute grid shape if single_group: nfacet = len(data[single_group]) if col: # idea - could add heuristic for nice shapes like 3x4 ncol = nfacet if row: ncol = 1 if col_wrap is not None: # Overrides previous settings ncol = col_wrap nrow = int(np.ceil(nfacet / ncol)) # Set the subplot kwargs subplot_kws = {} if subplot_kws is None else subplot_kws if figsize is None: # Calculate the base figure size with extra horizontal space for a # colorbar cbar_space = 1 figsize = (ncol * size * aspect + cbar_space, nrow * size) fig, axes = plt.subplots(nrow, ncol, sharex=sharex, sharey=sharey, squeeze=False, figsize=figsize, subplot_kw=subplot_kws) # Set up the lists of names for the row and column facet variables col_names = list(data[col].values) if col else [] row_names = list(data[row].values) if row else [] if single_group: full = [{single_group: x} for x in data[single_group].values] empty = [None for x in range(nrow * ncol - len(full))] name_dicts = full + empty else: rowcols = itertools.product(row_names, col_names) name_dicts = [{row: r, col: c} for r, c in rowcols] name_dicts = np.array(name_dicts).reshape(nrow, ncol) # Set up the class attributes # --------------------------- # First the public API self.data = data self.name_dicts = name_dicts self.fig = fig self.axes = axes self.row_names = row_names self.col_names = col_names # Next the private variables self._single_group = single_group self._nrow = nrow self._row_var = row self._ncol = ncol self._col_var = col self._col_wrap = col_wrap self._x_var = None self._y_var = None self._cmap_extend = None self._mappables = [] @property def _left_axes(self): return self.axes[:, 0] @property def _bottom_axes(self): return self.axes[-1, :] def map_dataarray(self, func, x, y, **kwargs): """ Apply a plotting function to a 2d facet's subset of the data. This is more convenient and less general than ``FacetGrid.map`` Parameters ---------- func : callable A plotting function with the same signature as a 2d xarray plotting method such as `xarray.plot.imshow` x, y : string Names of the coordinates to plot on x, y axes kwargs : additional keyword arguments to func Returns ------- self : FacetGrid object """ # These should be consistent with xarray.plot._plot2d cmap_kwargs = {'plot_data': self.data.values, # MPL default 'levels': 7 if 'contour' in func.__name__ else None, 'filled': func.__name__ != 'contour', } cmap_args = inspect.getargspec(_determine_cmap_params).args cmap_kwargs.update((a, kwargs[a]) for a in cmap_args if a in kwargs) cmap_params = _determine_cmap_params(**cmap_kwargs) # Order is important func_kwargs = kwargs.copy() func_kwargs.update(cmap_params) func_kwargs.update({'add_colorbar': False, 'add_labels': False}) # Get x, y labels for the first subplot x, y = _infer_xy_labels(darray=self.data.loc[self.name_dicts.flat[0]], x=x, y=y) for d, ax in zip(self.name_dicts.flat, self.axes.flat): # None is the sentinel value if d is not None: subset = self.data.loc[d] mappable = func(subset, x, y, ax=ax, **func_kwargs) self._mappables.append(mappable) self._cmap_extend = cmap_params.get('extend') self._finalize_grid(x, y) if kwargs.get('add_colorbar', True): self.add_colorbar() return self def _finalize_grid(self, *axlabels): """Finalize the annotations and layout.""" self.set_axis_labels(*axlabels) self.set_titles() self.fig.tight_layout() for ax, namedict in zip(self.axes.flat, self.name_dicts.flat): if namedict is None: ax.set_visible(False) def add_colorbar(self, **kwargs): """Draw a colorbar """ kwargs = kwargs.copy() if self._cmap_extend is not None: kwargs.setdefault('extend', self._cmap_extend) if getattr(self.data, 'name', None) is not None: kwargs.setdefault('label', self.data.name) self.cbar = self.fig.colorbar(self._mappables[-1], ax=list(self.axes.flat), **kwargs) return self def set_axis_labels(self, x_var=None, y_var=None): """Set axis labels on the left column and bottom row of the grid.""" if x_var is not None: self._x_var = x_var self.set_xlabels(x_var) if y_var is not None: self._y_var = y_var self.set_ylabels(y_var) return self def set_xlabels(self, label=None, **kwargs): """Label the x axis on the bottom row of the grid.""" if label is None: label = self._x_var for ax in self._bottom_axes: ax.set_xlabel(label, **kwargs) return self def set_ylabels(self, label=None, **kwargs): """Label the y axis on the left column of the grid.""" if label is None: label = self._y_var for ax in self._left_axes: ax.set_ylabel(label, **kwargs) return self def set_titles(self, template="{coord} = {value}", maxchar=30, **kwargs): """ Draw titles either above each facet or on the grid margins. Parameters ---------- template : string Template for plot titles containing {coord} and {value} maxchar : int Truncate titles at maxchar kwargs : keyword args additional arguments to matplotlib.text Returns ------- self: FacetGrid object """ import matplotlib as mpl kwargs["size"] = kwargs.pop("size", mpl.rcParams["axes.labelsize"]) nicetitle = functools.partial(_nicetitle, maxchar=maxchar, template=template) if self._single_group: for d, ax in zip(self.name_dicts.flat, self.axes.flat): # Only label the ones with data if d is not None: coord, value = list(d.items()).pop() title = nicetitle(coord, value, maxchar=maxchar) ax.set_title(title, **kwargs) else: # The row titles on the right edge of the grid for ax, row_name in zip(self.axes[:, -1], self.row_names): title = nicetitle(coord=self._row_var, value=row_name, maxchar=maxchar) ax.annotate(title, xy=(1.02, .5), xycoords="axes fraction", rotation=270, ha="left", va="center", **kwargs) # The column titles on the top row for ax, col_name in zip(self.axes[0, :], self.col_names): title = nicetitle(coord=self._col_var, value=col_name, maxchar=maxchar) ax.set_title(title, **kwargs) return self def set_ticks(self, max_xticks=_NTICKS, max_yticks=_NTICKS, fontsize=_FONTSIZE): """ Set and control tick behavior Parameters ---------- max_xticks, max_yticks : int, optional Maximum number of labeled ticks to plot on x, y axes fontsize : string or int Font size as used by matplotlib text Returns ------- self : FacetGrid object """ from matplotlib.ticker import MaxNLocator # Both are necessary x_major_locator = MaxNLocator(nbins=max_xticks) y_major_locator = MaxNLocator(nbins=max_yticks) for ax in self.axes.flat: ax.xaxis.set_major_locator(x_major_locator) ax.yaxis.set_major_locator(y_major_locator) for tick in itertools.chain(ax.xaxis.get_major_ticks(), ax.yaxis.get_major_ticks()): tick.label.set_fontsize(fontsize) return self def map(self, func, *args, **kwargs): """ Apply a plotting function to each facet's subset of the data. Parameters ---------- func : callable A plotting function that takes data and keyword arguments. It must plot to the currently active matplotlib Axes and take a `color` keyword argument. If faceting on the `hue` dimension, it must also take a `label` keyword argument. args : strings Column names in self.data that identify variables with data to plot. The data for each variable is passed to `func` in the order the variables are specified in the call. kwargs : keyword arguments All keyword arguments are passed to the plotting function. Returns ------- self : FacetGrid object """ import matplotlib.pyplot as plt for ax, namedict in zip(self.axes.flat, self.name_dicts.flat): if namedict is not None: data = self.data.loc[namedict] plt.sca(ax) innerargs = [data[a].values for a in args] # TODO: is it possible to verify that an artist is mappable? mappable = func(*innerargs, **kwargs) self._mappables.append(mappable) self._finalize_grid(*args[:2]) return self �����������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������xarray-0.9.1/xarray/plot/plot.py��������������������������������������������������������������������0000644�1311115�0011610�00000056204�13043511243�016746� 0����������������������������������������������������������������������������������������������������ustar �shoyer��������������������������eng�����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������""" Use this module directly: import xarray.plot as xplt Or use the methods on a DataArray: DataArray.plot._____ """ from __future__ import absolute_import from __future__ import division from __future__ import print_function import functools import warnings import numpy as np import pandas as pd from .utils import _determine_cmap_params, _infer_xy_labels, get_axis from .facetgrid import FacetGrid from xarray.core.pycompat import basestring # Maybe more appropriate to keep this in .utils def _right_dtype(arr, types): """ Is the numpy array a sub dtype of anything in types? """ return any(np.issubdtype(arr.dtype, t) for t in types) def _ensure_plottable(*args): """ Raise exception if there is anything in args that can't be plotted on an axis. """ plottypes = [np.floating, np.integer, np.timedelta64, np.datetime64] # Lists need to be converted to np.arrays here. if not any(_right_dtype(np.array(x), plottypes) for x in args): raise TypeError('Plotting requires coordinates to be numeric ' 'or dates.') def _easy_facetgrid(darray, plotfunc, x, y, row=None, col=None, col_wrap=None, sharex=True, sharey=True, aspect=None, size=None, subplot_kws=None, **kwargs): """ Convenience method to call xarray.plot.FacetGrid from 2d plotting methods kwargs are the arguments to 2d plotting method """ ax = kwargs.pop('ax', None) figsize = kwargs.pop('figsize', None) if ax is not None: raise ValueError("Can't use axes when making faceted plots.") if aspect is None: aspect = 1 if size is None: size = 3 elif figsize is not None: raise ValueError('cannot provide both `figsize` and `size` arguments') g = FacetGrid(data=darray, col=col, row=row, col_wrap=col_wrap, sharex=sharex, sharey=sharey, figsize=figsize, aspect=aspect, size=size, subplot_kws=subplot_kws) return g.map_dataarray(plotfunc, x, y, **kwargs) def plot(darray, row=None, col=None, col_wrap=None, ax=None, rtol=0.01, subplot_kws=None, **kwargs): """ Default plot of DataArray using matplotlib.pyplot. Calls xarray plotting function based on the dimensions of darray.squeeze() =============== =========================== Dimensions Plotting function --------------- --------------------------- 1 :py:func:`xarray.plot.line` 2 :py:func:`xarray.plot.pcolormesh` Anything else :py:func:`xarray.plot.hist` =============== =========================== Parameters ---------- darray : DataArray row : string, optional If passed, make row faceted plots on this dimension name col : string, optional If passed, make column faceted plots on this dimension name col_wrap : integer, optional Use together with ``col`` to wrap faceted plots ax : matplotlib axes, optional If None, uses the current axis. Not applicable when using facets. rtol : number, optional Relative tolerance used to determine if the indexes are uniformly spaced. Usually a small positive number. subplot_kws : dict, optional Dictionary of keyword arguments for matplotlib subplots. Only applies to FacetGrid plotting. **kwargs : optional Additional keyword arguments to matplotlib """ darray = darray.squeeze() plot_dims = set(darray.dims) plot_dims.discard(row) plot_dims.discard(col) ndims = len(plot_dims) error_msg = ('Only 2d plots are supported for facets in xarray. ' 'See the package `Seaborn` for more options.') if ndims == 1: if row or col: raise ValueError(error_msg) plotfunc = line elif ndims == 2: # Only 2d can FacetGrid kwargs['row'] = row kwargs['col'] = col kwargs['col_wrap'] = col_wrap kwargs['subplot_kws'] = subplot_kws plotfunc = pcolormesh else: if row or col: raise ValueError(error_msg) plotfunc = hist kwargs['ax'] = ax return plotfunc(darray, **kwargs) # This function signature should not change so that it can use # matplotlib format strings def line(darray, *args, **kwargs): """ Line plot of 1 dimensional DataArray index against values Wraps matplotlib.pyplot.plot Parameters ---------- darray : DataArray Must be 1 dimensional figsize : tuple, optional A tuple (width, height) of the figure in inches. Mutually exclusive with ``size`` and ``ax``. aspect : scalar, optional Aspect ratio of plot, so that ``aspect * size`` gives the width in inches. Only used if a ``size`` is provided. size : scalar, optional If provided, create a new figure for the plot with the given size. Height (in inches) of each plot. See also: ``aspect``. ax : matplotlib axes object, optional Axis on which to plot this figure. By default, use the current axis. Mutually exclusive with ``size`` and ``figsize``. *args, **kwargs : optional Additional arguments to matplotlib.pyplot.plot """ import matplotlib.pyplot as plt ndims = len(darray.dims) if ndims != 1: raise ValueError('Line plots are for 1 dimensional DataArrays. ' 'Passed DataArray has {ndims} ' 'dimensions'.format(ndims=ndims)) # Ensures consistency with .plot method figsize = kwargs.pop('figsize', None) aspect = kwargs.pop('aspect', None) size = kwargs.pop('size', None) ax = kwargs.pop('ax', None) ax = get_axis(figsize, size, aspect, ax) xlabel, = darray.dims x = darray.coords[xlabel] _ensure_plottable(x) primitive = ax.plot(x, darray, *args, **kwargs) ax.set_xlabel(xlabel) ax.set_title(darray._title_for_slice()) if darray.name is not None: ax.set_ylabel(darray.name) # Rotate dates on xlabels if np.issubdtype(x.dtype, np.datetime64): plt.gcf().autofmt_xdate() return primitive def hist(darray, figsize=None, size=None, aspect=None, ax=None, **kwargs): """ Histogram of DataArray Wraps matplotlib.pyplot.hist Plots N dimensional arrays by first flattening the array. Parameters ---------- darray : DataArray Can be any dimension figsize : tuple, optional A tuple (width, height) of the figure in inches. Mutually exclusive with ``size`` and ``ax``. aspect : scalar, optional Aspect ratio of plot, so that ``aspect * size`` gives the width in inches. Only used if a ``size`` is provided. size : scalar, optional If provided, create a new figure for the plot with the given size. Height (in inches) of each plot. See also: ``aspect``. ax : matplotlib axes object, optional Axis on which to plot this figure. By default, use the current axis. Mutually exclusive with ``size`` and ``figsize``. **kwargs : optional Additional keyword arguments to matplotlib.pyplot.hist """ import matplotlib.pyplot as plt ax = get_axis(figsize, size, aspect, ax) no_nan = np.ravel(darray.values) no_nan = no_nan[pd.notnull(no_nan)] primitive = ax.hist(no_nan, **kwargs) ax.set_ylabel('Count') if darray.name is not None: ax.set_title('Histogram of {0}'.format(darray.name)) return primitive def _update_axes_limits(ax, xincrease, yincrease): """ Update axes in place to increase or decrease For use in _plot2d """ if xincrease is None: pass elif xincrease: ax.set_xlim(sorted(ax.get_xlim())) elif not xincrease: ax.set_xlim(sorted(ax.get_xlim(), reverse=True)) if yincrease is None: pass elif yincrease: ax.set_ylim(sorted(ax.get_ylim())) elif not yincrease: ax.set_ylim(sorted(ax.get_ylim(), reverse=True)) # MUST run before any 2d plotting functions are defined since # _plot2d decorator adds them as methods here. class _PlotMethods(object): """ Enables use of xarray.plot functions as attributes on a DataArray. For example, DataArray.plot.imshow """ def __init__(self, darray): self._da = darray def __call__(self, **kwargs): return plot(self._da, **kwargs) @functools.wraps(hist) def hist(self, ax=None, **kwargs): return hist(self._da, ax=ax, **kwargs) @functools.wraps(line) def line(self, *args, **kwargs): return line(self._da, *args, **kwargs) def _plot2d(plotfunc): """ Decorator for common 2d plotting logic Also adds the 2d plot method to class _PlotMethods """ commondoc = """ Parameters ---------- darray : DataArray Must be 2 dimensional, unless creating faceted plots x : string, optional Coordinate for x axis. If None use darray.dims[1] y : string, optional Coordinate for y axis. If None use darray.dims[0] figsize : tuple, optional A tuple (width, height) of the figure in inches. Mutually exclusive with ``size`` and ``ax``. aspect : scalar, optional Aspect ratio of plot, so that ``aspect * size`` gives the width in inches. Only used if a ``size`` is provided. size : scalar, optional If provided, create a new figure for the plot with the given size. Height (in inches) of each plot. See also: ``aspect``. ax : matplotlib axes object, optional Axis on which to plot this figure. By default, use the current axis. Mutually exclusive with ``size`` and ``figsize``. row : string, optional If passed, make row faceted plots on this dimension name col : string, optional If passed, make column faceted plots on this dimension name col_wrap : integer, optional Use together with ``col`` to wrap faceted plots xincrease : None, True, or False, optional Should the values on the x axes be increasing from left to right? if None, use the default for the matplotlib function yincrease : None, True, or False, optional Should the values on the y axes be increasing from top to bottom? if None, use the default for the matplotlib function add_colorbar : Boolean, optional Adds colorbar to axis add_labels : Boolean, optional Use xarray metadata to label axes vmin, vmax : floats, optional Values to anchor the colormap, otherwise they are inferred from the data and other keyword arguments. When a diverging dataset is inferred, setting one of these values will fix the other by symmetry around ``center``. Setting both values prevents use of a diverging colormap. If discrete levels are provided as an explicit list, both of these values are ignored. cmap : matplotlib colormap name or object, optional The mapping from data values to color space. If not provided, this will be either be ``viridis`` (if the function infers a sequential dataset) or ``RdBu_r`` (if the function infers a diverging dataset). When `Seaborn` is installed, ``cmap`` may also be a `seaborn` color palette. If ``cmap`` is seaborn color palette and the plot type is not ``contour`` or ``contourf``, ``levels`` must also be specified. colors : discrete colors to plot, optional A single color or a list of colors. If the plot type is not ``contour`` or ``contourf``, the ``levels`` argument is required. center : float, optional The value at which to center the colormap. Passing this value implies use of a diverging colormap. Setting it to ``False`` prevents use of a diverging colormap. robust : bool, optional If True and ``vmin`` or ``vmax`` are absent, the colormap range is computed with 2nd and 98th percentiles instead of the extreme values. extend : {'neither', 'both', 'min', 'max'}, optional How to draw arrows extending the colorbar beyond its limits. If not provided, extend is inferred from vmin, vmax and the data limits. levels : int or list-like object, optional Split the colormap (cmap) into discrete color intervals. If an integer is provided, "nice" levels are chosen based on the data range: this can imply that the final number of levels is not exactly the expected one. Setting ``vmin`` and/or ``vmax`` with ``levels=N`` is equivalent to setting ``levels=np.linspace(vmin, vmax, N)``. infer_intervals : bool, optional Only applies to pcolormesh. If True, the coordinate intervals are passed to pcolormesh. If False, the original coordinates are used (this can be useful for certain map projections). The default is to always infer intervals, unless the mesh is irregular and plotted on a map projection. subplot_kws : dict, optional Dictionary of keyword arguments for matplotlib subplots. Only applies to FacetGrid plotting. cbar_ax : matplotlib Axes, optional Axes in which to draw the colorbar. cbar_kwargs : dict, optional Dictionary of keyword arguments to pass to the colorbar. **kwargs : optional Additional arguments to wrapped matplotlib function Returns ------- artist : The same type of primitive artist that the wrapped matplotlib function returns """ # Build on the original docstring plotfunc.__doc__ = '\n'.join((plotfunc.__doc__, commondoc)) @functools.wraps(plotfunc) def newplotfunc(darray, x=None, y=None, figsize=None, size=None, aspect=None, ax=None, row=None, col=None, col_wrap=None, xincrease=True, yincrease=True, add_colorbar=None, add_labels=True, vmin=None, vmax=None, cmap=None, center=None, robust=False, extend=None, levels=None, infer_intervals=None, colors=None, subplot_kws=None, cbar_ax=None, cbar_kwargs=None, **kwargs): # All 2d plots in xarray share this function signature. # Method signature below should be consistent. # Decide on a default for the colorbar before facetgrids if add_colorbar is None: add_colorbar = plotfunc.__name__ != 'contour' # Handle facetgrids first if row or col: allargs = locals().copy() allargs.update(allargs.pop('kwargs')) # Need the decorated plotting function allargs['plotfunc'] = globals()[plotfunc.__name__] return _easy_facetgrid(**allargs) import matplotlib.pyplot as plt # colors is mutually exclusive with cmap if cmap and colors: raise ValueError("Can't specify both cmap and colors.") # colors is only valid when levels is supplied or the plot is of type # contour or contourf if colors and (('contour' not in plotfunc.__name__) and (not levels)): raise ValueError("Can only specify colors with contour or levels") # we should not be getting a list of colors in cmap anymore # is there a better way to do this test? if isinstance(cmap, (list, tuple)): warnings.warn("Specifying a list of colors in cmap is deprecated. " "Use colors keyword instead.", DeprecationWarning, stacklevel=3) xlab, ylab = _infer_xy_labels(darray=darray, x=x, y=y) # better to pass the ndarrays directly to plotting functions xval = darray[xlab].values yval = darray[ylab].values zval = darray.to_masked_array(copy=False) # May need to transpose for correct x, y labels # xlab may be the name of a coord, we have to check for dim names if darray[xlab].dims[-1] == darray.dims[0]: zval = zval.T _ensure_plottable(xval, yval) if 'contour' in plotfunc.__name__ and levels is None: levels = 7 # this is the matplotlib default cmap_kwargs = {'plot_data': zval.data, 'vmin': vmin, 'vmax': vmax, 'cmap': colors if colors else cmap, 'center': center, 'robust': robust, 'extend': extend, 'levels': levels, 'filled': plotfunc.__name__ != 'contour', } cmap_params = _determine_cmap_params(**cmap_kwargs) if 'contour' in plotfunc.__name__: # extend is a keyword argument only for contour and contourf, but # passing it to the colorbar is sufficient for imshow and # pcolormesh kwargs['extend'] = cmap_params['extend'] kwargs['levels'] = cmap_params['levels'] if 'pcolormesh' == plotfunc.__name__: kwargs['infer_intervals'] = infer_intervals # This allows the user to pass in a custom norm coming via kwargs kwargs.setdefault('norm', cmap_params['norm']) if 'imshow' == plotfunc.__name__ and isinstance(aspect, basestring): # forbid usage of mpl strings raise ValueError("plt.imshow's `aspect` kwarg is not available " "in xarray") ax = get_axis(figsize, size, aspect, ax) primitive = plotfunc(xval, yval, zval, ax=ax, cmap=cmap_params['cmap'], vmin=cmap_params['vmin'], vmax=cmap_params['vmax'], **kwargs) # Label the plot with metadata if add_labels: ax.set_xlabel(xlab) ax.set_ylabel(ylab) ax.set_title(darray._title_for_slice()) if add_colorbar: cbar_kwargs = {} if cbar_kwargs is None else dict(cbar_kwargs) cbar_kwargs.setdefault('extend', cmap_params['extend']) if cbar_ax is None: cbar_kwargs.setdefault('ax', ax) else: cbar_kwargs.setdefault('cax', cbar_ax) cbar = plt.colorbar(primitive, **cbar_kwargs) if darray.name and add_labels and 'label' not in cbar_kwargs: cbar.set_label(darray.name, rotation=90) elif cbar_ax is not None or cbar_kwargs is not None: # inform the user about keywords which aren't used raise ValueError("cbar_ax and cbar_kwargs can't be used with " "add_colorbar=False.") _update_axes_limits(ax, xincrease, yincrease) return primitive # For use as DataArray.plot.plotmethod @functools.wraps(newplotfunc) def plotmethod(_PlotMethods_obj, x=None, y=None, figsize=None, size=None, aspect=None, ax=None, row=None, col=None, col_wrap=None, xincrease=True, yincrease=True, add_colorbar=None, add_labels=True, vmin=None, vmax=None, cmap=None, colors=None, center=None, robust=False, extend=None, levels=None, infer_intervals=None, subplot_kws=None, cbar_ax=None, cbar_kwargs=None, **kwargs): """ The method should have the same signature as the function. This just makes the method work on Plotmethods objects, and passes all the other arguments straight through. """ allargs = locals() allargs['darray'] = _PlotMethods_obj._da allargs.update(kwargs) for arg in ['_PlotMethods_obj', 'newplotfunc', 'kwargs']: del allargs[arg] return newplotfunc(**allargs) # Add to class _PlotMethods setattr(_PlotMethods, plotmethod.__name__, plotmethod) return newplotfunc @_plot2d def imshow(x, y, z, ax, **kwargs): """ Image plot of 2d DataArray using matplotlib.pyplot Wraps matplotlib.pyplot.imshow ..note:: This function needs uniformly spaced coordinates to properly label the axes. Call DataArray.plot() to check. The pixels are centered on the coordinates values. Ie, if the coordinate value is 3.2 then the pixels for those coordinates will be centered on 3.2. """ if x.ndim != 1 or y.ndim != 1: raise ValueError('imshow requires 1D coordinates, try using ' 'pcolormesh or contour(f)') # Centering the pixels- Assumes uniform spacing xstep = (x[1] - x[0]) / 2.0 ystep = (y[1] - y[0]) / 2.0 left, right = x[0] - xstep, x[-1] + xstep bottom, top = y[-1] + ystep, y[0] - ystep defaults = {'extent': [left, right, bottom, top], 'origin': 'upper', 'interpolation': 'nearest', } if not hasattr(ax, 'projection'): # not for cartopy geoaxes defaults['aspect'] = 'auto' # Allow user to override these defaults defaults.update(kwargs) primitive = ax.imshow(z, **defaults) return primitive @_plot2d def contour(x, y, z, ax, **kwargs): """ Contour plot of 2d DataArray Wraps matplotlib.pyplot.contour """ primitive = ax.contour(x, y, z, **kwargs) return primitive @_plot2d def contourf(x, y, z, ax, **kwargs): """ Filled contour plot of 2d DataArray Wraps matplotlib.pyplot.contourf """ primitive = ax.contourf(x, y, z, **kwargs) return primitive def _infer_interval_breaks(coord, axis=0): """ >>> _infer_interval_breaks(np.arange(5)) array([-0.5, 0.5, 1.5, 2.5, 3.5, 4.5]) >>> _infer_interval_breaks([[0, 1], [3, 4]], axis=1) array([[-0.5, 0.5, 1.5], [ 2.5, 3.5, 4.5]]) """ coord = np.asarray(coord) deltas = 0.5 * np.diff(coord, axis=axis) first = np.take(coord, [0], axis=axis) - np.take(deltas, [0], axis=axis) last = np.take(coord, [-1], axis=axis) + np.take(deltas, [-1], axis=axis) trim_last = tuple(slice(None, -1) if n == axis else slice(None) for n in range(coord.ndim)) return np.concatenate([first, coord[trim_last] + deltas, last], axis=axis) @_plot2d def pcolormesh(x, y, z, ax, infer_intervals=None, **kwargs): """ Pseudocolor plot of 2d DataArray Wraps matplotlib.pyplot.pcolormesh """ # decide on a default for infer_intervals (GH781) x = np.asarray(x) if infer_intervals is None: if hasattr(ax, 'projection'): if len(x.shape) == 1: infer_intervals = True else: infer_intervals = False else: infer_intervals = True if infer_intervals: if len(x.shape) == 1: x = _infer_interval_breaks(x) y = _infer_interval_breaks(y) else: # we have to infer the intervals on both axes x = _infer_interval_breaks(x, axis=1) x = _infer_interval_breaks(x, axis=0) y = _infer_interval_breaks(y, axis=1) y = _infer_interval_breaks(y, axis=0) primitive = ax.pcolormesh(x, y, z, **kwargs) # by default, pcolormesh picks "round" values for bounds # this results in ugly looking plots with lots of surrounding whitespace if not hasattr(ax, 'projection') and x.ndim == 1 and y.ndim == 1: # not a cartopy geoaxis ax.set_xlim(x[0], x[-1]) ax.set_ylim(y[0], y[-1]) return primitive ��������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������xarray-0.9.1/xarray/plot/utils.py�������������������������������������������������������������������0000644�1311115�0011610�00000017301�13043511243�017123� 0����������������������������������������������������������������������������������������������������ustar �shoyer��������������������������eng�����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������from __future__ import absolute_import from __future__ import division from __future__ import print_function import pkg_resources import numpy as np import pandas as pd from ..core.pycompat import basestring from ..core.utils import is_scalar def _load_default_cmap(fname='default_colormap.csv'): """ Returns viridis color map """ from matplotlib.colors import LinearSegmentedColormap # Not sure what the first arg here should be f = pkg_resources.resource_stream(__name__, fname) cm_data = pd.read_csv(f, header=None).values return LinearSegmentedColormap.from_list('viridis', cm_data) def _determine_extend(calc_data, vmin, vmax): extend_min = calc_data.min() < vmin extend_max = calc_data.max() > vmax if extend_min and extend_max: extend = 'both' elif extend_min: extend = 'min' elif extend_max: extend = 'max' else: extend = 'neither' return extend def _build_discrete_cmap(cmap, levels, extend, filled): """ Build a discrete colormap and normalization of the data. """ import matplotlib as mpl if not filled: # non-filled contour plots extend = 'max' if extend == 'both': ext_n = 2 elif extend in ['min', 'max']: ext_n = 1 else: ext_n = 0 n_colors = len(levels) + ext_n - 1 pal = _color_palette(cmap, n_colors) new_cmap, cnorm = mpl.colors.from_levels_and_colors( levels, pal, extend=extend) # copy the old cmap name, for easier testing new_cmap.name = getattr(cmap, 'name', cmap) return new_cmap, cnorm def _color_palette(cmap, n_colors): import matplotlib.pyplot as plt from matplotlib.colors import ListedColormap colors_i = np.linspace(0, 1., n_colors) if isinstance(cmap, (list, tuple)): # we have a list of colors try: # first try to turn it into a palette with seaborn from seaborn.apionly import color_palette pal = color_palette(cmap, n_colors=n_colors) except ImportError: # if that fails, use matplotlib # in this case, is there any difference between mpl and seaborn? cmap = ListedColormap(cmap, N=n_colors) pal = cmap(colors_i) elif isinstance(cmap, basestring): # we have some sort of named palette try: # first try to turn it into a palette with seaborn from seaborn.apionly import color_palette pal = color_palette(cmap, n_colors=n_colors) except (ImportError, ValueError): # ValueError is raised when seaborn doesn't like a colormap # (e.g. jet). If that fails, use matplotlib try: # is this a matplotlib cmap? cmap = plt.get_cmap(cmap) except ValueError: # or maybe we just got a single color as a string cmap = ListedColormap([cmap], N=n_colors) pal = cmap(colors_i) else: # cmap better be a LinearSegmentedColormap (e.g. viridis) pal = cmap(colors_i) return pal def _determine_cmap_params(plot_data, vmin=None, vmax=None, cmap=None, center=None, robust=False, extend=None, levels=None, filled=True, norm=None): """ Use some heuristics to set good defaults for colorbar and range. Adapted from Seaborn: https://github.com/mwaskom/seaborn/blob/v0.6/seaborn/matrix.py#L158 Parameters ========== plot_data: Numpy array Doesn't handle xarray objects Returns ======= cmap_params : dict Use depends on the type of the plotting function """ ROBUST_PERCENTILE = 2.0 import matplotlib as mpl calc_data = np.ravel(plot_data[~pd.isnull(plot_data)]) # Setting center=False prevents a divergent cmap possibly_divergent = center is not False # Set center to 0 so math below makes sense but remember its state center_is_none = False if center is None: center = 0 center_is_none = True # Setting both vmin and vmax prevents a divergent cmap if (vmin is not None) and (vmax is not None): possibly_divergent = False # Setting vmin or vmax implies linspaced levels user_minmax = (vmin is not None) or (vmax is not None) # vlim might be computed below vlim = None if vmin is None: if robust: vmin = np.percentile(calc_data, ROBUST_PERCENTILE) else: vmin = calc_data.min() elif possibly_divergent: vlim = abs(vmin - center) if vmax is None: if robust: vmax = np.percentile(calc_data, 100 - ROBUST_PERCENTILE) else: vmax = calc_data.max() elif possibly_divergent: vlim = abs(vmax - center) if possibly_divergent: # kwargs not specific about divergent or not: infer defaults from data divergent = ((vmin < 0) and (vmax > 0)) or not center_is_none else: divergent = False # A divergent map should be symmetric around the center value if divergent: if vlim is None: vlim = max(abs(vmin - center), abs(vmax - center)) vmin, vmax = -vlim, vlim # Now add in the centering value and set the limits vmin += center vmax += center # Choose default colormaps if not provided if cmap is None: if divergent: cmap = "RdBu_r" else: cmap = "viridis" # Allow viridis before matplotlib 1.5 if cmap == "viridis": cmap = _load_default_cmap() # Handle discrete levels if levels is not None: if is_scalar(levels): if user_minmax or levels == 1: levels = np.linspace(vmin, vmax, levels) else: # N in MaxNLocator refers to bins, not ticks ticker = mpl.ticker.MaxNLocator(levels-1) levels = ticker.tick_values(vmin, vmax) vmin, vmax = levels[0], levels[-1] if extend is None: extend = _determine_extend(calc_data, vmin, vmax) if levels is not None: cmap, norm = _build_discrete_cmap(cmap, levels, extend, filled) return dict(vmin=vmin, vmax=vmax, cmap=cmap, extend=extend, levels=levels, norm=norm) def _infer_xy_labels(darray, x, y): """ Determine x and y labels. For use in _plot2d darray must be a 2 dimensional data array. """ if x is None and y is None: if darray.ndim != 2: raise ValueError('DataArray must be 2d') y, x = darray.dims elif x is None or y is None: raise ValueError('cannot supply only one of x and y') elif any(k not in darray.coords and k not in darray.dims for k in (x, y)): raise ValueError('x and y must be coordinate variables') return x, y def get_axis(figsize, size, aspect, ax): import matplotlib as mpl import matplotlib.pyplot as plt if figsize is not None: if ax is not None: raise ValueError('cannot provide both `figsize` and ' '`ax` arguments') if size is not None: raise ValueError('cannot provide both `figsize` and ' '`size` arguments') _, ax = plt.subplots(figsize=figsize) elif size is not None: if ax is not None: raise ValueError('cannot provide both `size` and `ax` arguments') if aspect is None: width, height = mpl.rcParams['figure.figsize'] aspect = width / height figsize = (size * aspect, size) _, ax = plt.subplots(figsize=figsize) elif aspect is not None: raise ValueError('cannot provide `aspect` argument without `size`') if ax is None: ax = plt.gca() return ax �������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������xarray-0.9.1/xarray/testing.py����������������������������������������������������������������������0000644�1311115�0011610�00000012414�13043511243�016462� 0����������������������������������������������������������������������������������������������������ustar �shoyer��������������������������eng�����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������"""Testing functions exposed to the user API""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import numpy as np from xarray.core import ops def _decode_string_data(data): if data.dtype.kind == 'S': return np.core.defchararray.decode(data, 'utf-8', 'replace') return data def _data_allclose_or_equiv(arr1, arr2, rtol=1e-05, atol=1e-08, decode_bytes=True): if any(arr.dtype.kind == 'S' for arr in [arr1, arr2]) and decode_bytes: arr1 = _decode_string_data(arr1) arr2 = _decode_string_data(arr2) exact_dtypes = ['M', 'm', 'O', 'U'] if any(arr.dtype.kind in exact_dtypes for arr in [arr1, arr2]): return ops.array_equiv(arr1, arr2) else: return ops.allclose_or_equiv(arr1, arr2, rtol=rtol, atol=atol) def assert_equal(a, b): """Like :py:func:`numpy.testing.assert_array_equal`, but for xarray objects. Raises an AssertionError if two objects are not equal. This will match data values, dimensions and coordinates, but not names or attributes (except for Dataset objects for which the variable names must match). Arrays with NaN in the same location are considered equal. Parameters ---------- a : xarray.Dataset, xarray.DataArray or xarray.Variable The first object to compare. b : xarray.Dataset, xarray.DataArray or xarray.Variable The second object to compare. See also -------- assert_identical, assert_allclose, Dataset.equals, DataArray.equals, numpy.testing.assert_array_equal """ import xarray as xr ___tracebackhide__ = True # noqa: F841 assert type(a) == type(b) if isinstance(a, (xr.Variable, xr.DataArray, xr.Dataset)): assert a.equals(b), '{}\n{}'.format(a, b) else: raise TypeError('{} not supported by assertion comparison' .format(type(a))) def assert_identical(a, b): """Like :py:func:`xarray.testing.assert_equal`, but also matches the objects' names and attributes. Raises an AssertionError if two objects are not identical. Parameters ---------- a : xarray.Dataset, xarray.DataArray or xarray.Variable The first object to compare. b : xarray.Dataset, xarray.DataArray or xarray.Variable The second object to compare. See also -------- assert_equal, assert_allclose, Dataset.equals, DataArray.equals """ import xarray as xr ___tracebackhide__ = True # noqa: F841 assert type(a) == type(b) if isinstance(a, xr.DataArray): assert a.name == b.name assert_identical(a._to_temp_dataset(), b._to_temp_dataset()) elif isinstance(a, (xr.Dataset, xr.Variable)): assert a.identical(b), '{}\n{}'.format(a, b) else: raise TypeError('{} not supported by assertion comparison' .format(type(a))) def assert_allclose(a, b, rtol=1e-05, atol=1e-08, decode_bytes=True): """Like :py:func:`numpy.testing.assert_allclose`, but for xarray objects. Raises an AssertionError if two objects are not equal up to desired tolerance. Parameters ---------- a : xarray.Dataset, xarray.DataArray or xarray.Variable The first object to compare. b : xarray.Dataset, xarray.DataArray or xarray.Variable The second object to compare. rtol : float, optional Relative tolerance. atol : float, optional Absolute tolerance. decode_bytes : bool, optional Whether byte dtypes should be decoded to strings as UTF-8 or not. This is useful for testing serialization methods on Python 3 that return saved strings as bytes. See also -------- assert_identical, assert_equal, numpy.testing.assert_allclose """ import xarray as xr ___tracebackhide__ = True # noqa: F841 assert type(a) == type(b) if isinstance(a, xr.Variable): assert a.dims == b.dims allclose = _data_allclose_or_equiv(a.values, b.values, rtol=rtol, atol=atol, decode_bytes=decode_bytes) assert allclose, '{}\n{}'.format(a.values, b.values) elif isinstance(a, xr.DataArray): assert_allclose(a.variable, b.variable) assert set(a.coords) == set(b.coords) for v in a.coords.variables: # can't recurse with this function as coord is sometimes a # DataArray, so call into _data_allclose_or_equiv directly allclose = _data_allclose_or_equiv(a.coords[v].values, b.coords[v].values, rtol=rtol, atol=atol, decode_bytes=decode_bytes) assert allclose, '{}\n{}'.format(a.coords[v].values, b.coords[v].values) elif isinstance(a, xr.Dataset): assert set(a) == set(b) assert set(a.coords) == set(b.coords) for k in list(a.variables) + list(a.coords): assert_allclose(a[k], b[k], rtol=rtol, atol=atol, decode_bytes=decode_bytes) else: raise TypeError('{} not supported by assertion comparison' .format(type(a))) ����������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������xarray-0.9.1/xarray/tests/��������������������������������������������������������������������������0000755�1311115�0011610�00000000000�13043675421�015604� 5����������������������������������������������������������������������������������������������������ustar �shoyer��������������������������eng�����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������xarray-0.9.1/xarray/tests/__init__.py���������������������������������������������������������������0000644�1311115�0011610�00000011436�13043675344�017726� 0����������������������������������������������������������������������������������������������������ustar �shoyer��������������������������eng�����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������from __future__ import absolute_import from __future__ import division from __future__ import print_function import warnings from contextlib import contextmanager from distutils.version import LooseVersion import numpy as np from numpy.testing import assert_array_equal import pytest from xarray.core import utils, nputils, ops from xarray.core.variable import as_variable from xarray.core.pycompat import PY3 from xarray.testing import assert_equal, assert_identical, assert_allclose try: import unittest2 as unittest except ImportError: import unittest try: import scipy has_scipy = True except ImportError: has_scipy = False try: import pydap.client has_pydap = True except ImportError: has_pydap = False try: import netCDF4 has_netCDF4 = True except ImportError: has_netCDF4 = False try: import h5netcdf has_h5netcdf = True except ImportError: has_h5netcdf = False try: import Nio has_pynio = True except ImportError: has_pynio = False try: import dask.array import dask dask.set_options(get=dask.get) has_dask = True except ImportError: has_dask = False try: import matplotlib has_matplotlib = True except ImportError: has_matplotlib = False try: import bottleneck if LooseVersion(bottleneck.__version__) < LooseVersion('1.0'): raise ImportError('Fall back to numpy') has_bottleneck = True except ImportError: has_bottleneck = False # slighly simpler construction that the full functions. # Generally `pytest.importorskip('package')` inline is even easier requires_matplotlib = pytest.mark.skipif(not has_matplotlib, reason='requires matplotlib') def requires_scipy(test): return test if has_scipy else pytest.mark.skip('requires scipy')(test) def requires_pydap(test): return test if has_pydap else pytest.mark.skip('requires pydap.client')(test) def requires_netCDF4(test): return test if has_netCDF4 else pytest.mark.skip('requires netCDF4')(test) def requires_h5netcdf(test): return test if has_h5netcdf else pytest.mark.skip('requires h5netcdf')(test) def requires_pynio(test): return test if has_pynio else pytest.mark.skip('requires pynio')(test) def requires_scipy_or_netCDF4(test): return (test if has_scipy or has_netCDF4 else pytest.mark.skip('requires scipy or netCDF4')(test)) def requires_dask(test): return test if has_dask else pytest.mark.skip('requires dask')(test) def requires_bottleneck(test): return test if has_bottleneck else pytest.mark.skip('requires bottleneck')(test) class TestCase(unittest.TestCase): if PY3: # Python 3 assertCountEqual is roughly equivalent to Python 2 # assertItemsEqual def assertItemsEqual(self, first, second, msg=None): return self.assertCountEqual(first, second, msg) @contextmanager def assertWarns(self, message): with warnings.catch_warnings(record=True) as w: warnings.filterwarnings('always', message) yield assert len(w) > 0 assert any(message in str(wi.message) for wi in w) def assertVariableEqual(self, v1, v2): assert_equal(v1, v2) def assertVariableIdentical(self, v1, v2): assert_identical(v1, v2) def assertVariableAllClose(self, v1, v2, rtol=1e-05, atol=1e-08): assert_allclose(v1, v2, rtol=rtol, atol=atol) def assertVariableNotEqual(self, v1, v2): assert not v1.equals(v2) def assertArrayEqual(self, a1, a2): assert_array_equal(a1, a2) def assertEqual(self, a1, a2): assert a1 == a2 or (a1 != a1 and a2 != a2) def assertDatasetEqual(self, d1, d2): assert_equal(d1, d2) def assertDatasetIdentical(self, d1, d2): assert_identical(d1, d2) def assertDatasetAllClose(self, d1, d2, rtol=1e-05, atol=1e-08): assert_allclose(d1, d2, rtol=rtol, atol=atol) def assertCoordinatesEqual(self, d1, d2): assert_equal(d1, d2) def assertDataArrayEqual(self, ar1, ar2): assert_equal(ar1, ar2) def assertDataArrayIdentical(self, ar1, ar2): assert_identical(ar1, ar2) def assertDataArrayAllClose(self, ar1, ar2, rtol=1e-05, atol=1e-08): assert_allclose(ar1, ar2, rtol=rtol, atol=atol) class UnexpectedDataAccess(Exception): pass class InaccessibleArray(utils.NDArrayMixin): def __init__(self, array): self.array = array def __getitem__(self, key): raise UnexpectedDataAccess("Tried accessing data") class ReturnItem(object): def __getitem__(self, key): return key def source_ndarray(array): """Given an ndarray, return the base object which holds its memory, or the object itself. 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R0=c"uI]Z>=X+Z]6%>ۘǁ^+y6=$ky3WLfǗ[rp,)_d&Ou+d^'m"ΣX~Rn2*u6Q?r>̝# ߉Z3 c|&zqߋN9y>].6ng29>>ǵA(D`(6'^ juT.>Dχ7LN\7+|;p!U? ! hl2lNRQLIԌO)MӑԒOWNFW~�;kDž��������������������������������������������xarray-0.9.1/xarray/tests/test_backends.py����������������������������������������������������������0000644�1311115�0011610�00000156315�13043511243�020771� 0����������������������������������������������������������������������������������������������������ustar �shoyer��������������������������eng�����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������from __future__ import absolute_import from __future__ import division from __future__ import print_function from io import BytesIO from threading import Lock import contextlib import itertools import os.path import pickle import shutil import tempfile import unittest import sys import numpy as np import pandas as pd import pytest import xarray as xr from xarray import (Dataset, DataArray, open_dataset, open_dataarray, open_mfdataset, backends, save_mfdataset) from xarray.backends.common import robust_getitem from xarray.backends.netCDF4_ import _extract_nc4_variable_encoding from xarray.core import indexing from xarray.core.pycompat import iteritems, PY2, PY3 from . import (TestCase, requires_scipy, requires_netCDF4, requires_pydap, requires_scipy_or_netCDF4, requires_dask, requires_h5netcdf, requires_pynio, has_netCDF4, has_scipy, assert_allclose) from .test_dataset import create_test_data try: import netCDF4 as nc4 except ImportError: pass try: import dask.array as da except ImportError: pass ON_WINDOWS = sys.platform == 'win32' def open_example_dataset(name, *args, **kwargs): return open_dataset(os.path.join(os.path.dirname(__file__), 'data', name), *args, **kwargs) def create_masked_and_scaled_data(): x = np.array([np.nan, np.nan, 10, 10.1, 10.2]) encoding = {'_FillValue': -1, 'add_offset': 10, 'scale_factor': np.float32(0.1), 'dtype': 'i2'} return Dataset({'x': ('t', x, {}, encoding)}) def create_encoded_masked_and_scaled_data(): attributes = {'_FillValue': -1, 'add_offset': 10, 'scale_factor': np.float32(0.1)} return Dataset({'x': ('t', [-1, -1, 0, 1, 2], attributes)}) def create_boolean_data(): attributes = {'units': '-'} return Dataset({'x': ('t', [True, False, False, True], attributes)}) class TestCommon(TestCase): def test_robust_getitem(self): class UnreliableArrayFailure(Exception): pass class UnreliableArray(object): def __init__(self, array, failures=1): self.array = array self.failures = failures def __getitem__(self, key): if self.failures > 0: self.failures -= 1 raise UnreliableArrayFailure return self.array[key] array = UnreliableArray([0]) with self.assertRaises(UnreliableArrayFailure): array[0] self.assertEqual(array[0], 0) actual = robust_getitem(array, 0, catch=UnreliableArrayFailure, initial_delay=0) self.assertEqual(actual, 0) class Only32BitTypes(object): pass class DatasetIOTestCases(object): def create_store(self): raise NotImplementedError def roundtrip(self, data, **kwargs): raise NotImplementedError def test_zero_dimensional_variable(self): expected = create_test_data() expected['float_var'] = ([], 1.0e9, {'units': 'units of awesome'}) expected['string_var'] = ([], np.array('foobar', dtype='S')) with self.roundtrip(expected) as actual: self.assertDatasetAllClose(expected, actual) def test_write_store(self): expected = create_test_data() with self.create_store() as store: expected.dump_to_store(store) # we need to cf decode the store because it has time and # non-dimension coordinates actual = xr.decode_cf(store) self.assertDatasetAllClose(expected, actual) def test_roundtrip_test_data(self): expected = create_test_data() with self.roundtrip(expected) as actual: self.assertDatasetAllClose(expected, actual) def test_load(self): expected = create_test_data() @contextlib.contextmanager def assert_loads(vars=None): if vars is None: vars = expected with self.roundtrip(expected) as actual: for k, v in actual.variables.items(): # IndexVariables are eagerly loaded into memory self.assertEqual(v._in_memory, k in actual.dims) yield actual for k, v in actual.variables.items(): if k in vars: self.assertTrue(v._in_memory) self.assertDatasetAllClose(expected, actual) with self.assertRaises(AssertionError): # make sure the contextmanager works! with assert_loads() as ds: pass with assert_loads() as ds: ds.load() with assert_loads(['var1', 'dim1', 'dim2']) as ds: ds['var1'].load() # verify we can read data even after closing the file with self.roundtrip(expected) as ds: actual = ds.load() self.assertDatasetAllClose(expected, actual) def test_dataset_compute(self): expected = create_test_data() with self.roundtrip(expected) as actual: # Test Dataset.compute() for k, v in actual.variables.items(): # IndexVariables are eagerly cached self.assertEqual(v._in_memory, k in actual.dims) computed = actual.compute() for k, v in actual.variables.items(): self.assertEqual(v._in_memory, k in actual.dims) for v in computed.variables.values(): self.assertTrue(v._in_memory) self.assertDatasetAllClose(expected, actual) self.assertDatasetAllClose(expected, computed) def test_pickle(self): expected = Dataset({'foo': ('x', [42])}) with self.roundtrip( expected, allow_cleanup_failure=ON_WINDOWS) as roundtripped: raw_pickle = pickle.dumps(roundtripped) # windows doesn't like opening the same file twice roundtripped.close() unpickled_ds = pickle.loads(raw_pickle) self.assertDatasetIdentical(expected, unpickled_ds) def test_pickle_dataarray(self): expected = Dataset({'foo': ('x', [42])}) with self.roundtrip( expected, allow_cleanup_failure=ON_WINDOWS) as roundtripped: unpickled_array = pickle.loads(pickle.dumps(roundtripped['foo'])) self.assertDatasetIdentical(expected['foo'], unpickled_array) def test_dataset_caching(self): expected = Dataset({'foo': ('x', [5, 6, 7])}) with self.roundtrip(expected) as actual: assert isinstance(actual.foo.variable._data, indexing.MemoryCachedArray) assert not actual.foo.variable._in_memory actual.foo.values # cache assert actual.foo.variable._in_memory with self.roundtrip(expected, open_kwargs={'cache': False}) as actual: assert isinstance(actual.foo.variable._data, indexing.CopyOnWriteArray) assert not actual.foo.variable._in_memory actual.foo.values # no caching assert not actual.foo.variable._in_memory def test_roundtrip_None_variable(self): expected = Dataset({None: (('x', 'y'), [[0, 1], [2, 3]])}) with self.roundtrip(expected) as actual: self.assertDatasetAllClose(expected, actual) def test_roundtrip_object_dtype(self): floats = np.array([0.0, 0.0, 1.0, 2.0, 3.0], dtype=object) floats_nans = np.array([np.nan, np.nan, 1.0, 2.0, 3.0], dtype=object) letters = np.array(['ab', 'cdef', 'g'], dtype=object) letters_nans = np.array(['ab', 'cdef', np.nan], dtype=object) all_nans = np.array([np.nan, np.nan], dtype=object) original = Dataset({'floats': ('a', floats), 'floats_nans': ('a', floats_nans), 'letters': ('b', letters), 'letters_nans': ('b', letters_nans), 'all_nans': ('c', all_nans), 'nan': ([], np.nan)}) expected = original.copy(deep=True) if isinstance(self, Only32BitTypes): # for netCDF3 tests, expect the results to come back as characters expected['letters_nans'] = expected['letters_nans'].astype('S') expected['letters'] = expected['letters'].astype('S') with self.roundtrip(original) as actual: try: self.assertDatasetIdentical(expected, actual) except AssertionError: # Most stores use '' for nans in strings, but some don't # first try the ideal case (where the store returns exactly) # the original Dataset), then try a more realistic case. # ScipyDataTest, NetCDF3ViaNetCDF4DataTest and NetCDF4DataTest # all end up using this case. expected['letters_nans'][-1] = '' self.assertDatasetIdentical(expected, actual) def test_roundtrip_string_data(self): expected = Dataset({'x': ('t', ['ab', 'cdef'])}) with self.roundtrip(expected) as actual: if isinstance(self, Only32BitTypes): expected['x'] = expected['x'].astype('S') self.assertDatasetIdentical(expected, actual) def test_roundtrip_datetime_data(self): times = pd.to_datetime(['2000-01-01', '2000-01-02', 'NaT']) expected = Dataset({'t': ('t', times), 't0': times[0]}) kwds = {'encoding': {'t0': {'units': 'days since 1950-01-01'}}} with self.roundtrip(expected, save_kwargs=kwds) as actual: self.assertDatasetIdentical(expected, actual) self.assertEquals(actual.t0.encoding['units'], 'days since 1950-01-01') def test_roundtrip_timedelta_data(self): time_deltas = pd.to_timedelta(['1h', '2h', 'NaT']) expected = Dataset({'td': ('td', time_deltas), 'td0': time_deltas[0]}) with self.roundtrip(expected) as actual: self.assertDatasetIdentical(expected, actual) def test_roundtrip_float64_data(self): expected = Dataset({'x': ('y', np.array([1.0, 2.0, np.pi], dtype='float64'))}) with self.roundtrip(expected) as actual: self.assertDatasetIdentical(expected, actual) def test_roundtrip_example_1_netcdf(self): expected = open_example_dataset('example_1.nc') with self.roundtrip(expected) as actual: # we allow the attributes to differ since that # will depend on the encoding used. For example, # without CF encoding 'actual' will end up with # a dtype attribute. self.assertDatasetEqual(expected, actual) def test_roundtrip_coordinates(self): original = Dataset({'foo': ('x', [0, 1])}, {'x': [2, 3], 'y': ('a', [42]), 'z': ('x', [4, 5])}) with self.roundtrip(original) as actual: self.assertDatasetIdentical(original, actual) expected = original.drop('foo') with self.roundtrip(expected) as actual: self.assertDatasetIdentical(expected, actual) def test_roundtrip_boolean_dtype(self): original = create_boolean_data() self.assertEqual(original['x'].dtype, 'bool') with self.roundtrip(original) as actual: self.assertDatasetIdentical(original, actual) self.assertEqual(actual['x'].dtype, 'bool') def test_orthogonal_indexing(self): in_memory = create_test_data() with self.roundtrip(in_memory) as on_disk: indexers = {'dim1': np.arange(3), 'dim2': np.arange(4), 'dim3': np.arange(5)} expected = in_memory.isel(**indexers) actual = on_disk.isel(**indexers) self.assertDatasetAllClose(expected, actual) # do it twice, to make sure we're switched from orthogonal -> numpy # when we cached the values actual = on_disk.isel(**indexers) self.assertDatasetAllClose(expected, actual) class CFEncodedDataTest(DatasetIOTestCases): def test_roundtrip_strings_with_fill_value(self): values = np.array(['ab', 'cdef', np.nan], dtype=object) encoding = {'_FillValue': np.string_('X'), 'dtype': np.dtype('S1')} original = Dataset({'x': ('t', values, {}, encoding)}) expected = original.copy(deep=True) expected['x'][:2] = values[:2].astype('S') with self.roundtrip(original) as actual: self.assertDatasetIdentical(expected, actual) original = Dataset({'x': ('t', values, {}, {'_FillValue': '\x00'})}) if not isinstance(self, Only32BitTypes): # these stores can save unicode strings expected = original.copy(deep=True) if isinstance(self, BaseNetCDF4Test): # netCDF4 can't keep track of an empty _FillValue for VLEN # variables expected['x'][-1] = '' elif (isinstance(self, (NetCDF3ViaNetCDF4DataTest, NetCDF4ClassicViaNetCDF4DataTest)) or (has_netCDF4 and type(self) is GenericNetCDFDataTest)): # netCDF4 can't keep track of an empty _FillValue for nc3, either: # https://github.com/Unidata/netcdf4-python/issues/273 expected['x'][-1] = np.string_('') with self.roundtrip(original) as actual: self.assertDatasetIdentical(expected, actual) def test_roundtrip_mask_and_scale(self): decoded = create_masked_and_scaled_data() encoded = create_encoded_masked_and_scaled_data() with self.roundtrip(decoded) as actual: self.assertDatasetAllClose(decoded, actual) with self.roundtrip(decoded, open_kwargs=dict(decode_cf=False)) as actual: # TODO: this assumes that all roundtrips will first # encode. Is that something we want to test for? self.assertDatasetAllClose(encoded, actual) with self.roundtrip(encoded, open_kwargs=dict(decode_cf=False)) as actual: self.assertDatasetAllClose(encoded, actual) # make sure roundtrip encoding didn't change the # original dataset. self.assertDatasetIdentical(encoded, create_encoded_masked_and_scaled_data()) with self.roundtrip(encoded) as actual: self.assertDatasetAllClose(decoded, actual) with self.roundtrip(encoded, open_kwargs=dict(decode_cf=False)) as actual: self.assertDatasetAllClose(encoded, actual) def test_coordinates_encoding(self): def equals_latlon(obj): return obj == 'lat lon' or obj == 'lon lat' original = Dataset({'temp': ('x', [0, 1]), 'precip': ('x', [0, -1])}, {'lat': ('x', [2, 3]), 'lon': ('x', [4, 5])}) with self.roundtrip(original) as actual: self.assertDatasetIdentical(actual, original) with create_tmp_file() as tmp_file: original.to_netcdf(tmp_file) with open_dataset(tmp_file, decode_coords=False) as ds: self.assertTrue(equals_latlon(ds['temp'].attrs['coordinates'])) self.assertTrue(equals_latlon(ds['precip'].attrs['coordinates'])) self.assertNotIn('coordinates', ds.attrs) self.assertNotIn('coordinates', ds['lat'].attrs) self.assertNotIn('coordinates', ds['lon'].attrs) modified = original.drop(['temp', 'precip']) with self.roundtrip(modified) as actual: self.assertDatasetIdentical(actual, modified) with create_tmp_file() as tmp_file: modified.to_netcdf(tmp_file) with open_dataset(tmp_file, decode_coords=False) as ds: self.assertTrue(equals_latlon(ds.attrs['coordinates'])) self.assertNotIn('coordinates', ds['lat'].attrs) self.assertNotIn('coordinates', ds['lon'].attrs) def test_roundtrip_endian(self): ds = Dataset({'x': np.arange(3, 10, dtype='>i2'), 'y': np.arange(3, 20, dtype=' 2d objs = [Dataset({'z': ('x', [-1])}, {'x': [0], 'y': [0]}), Dataset({'z': ('y', [1])}, {'x': [1], 'y': [0]})] actual = concat(objs, 'x') expected = Dataset({'z': (('x', 'y'), [[-1], [1]])}, {'x': [0, 1], 'y': [0]}) self.assertDatasetIdentical(actual, expected) def test_concat_do_not_promote(self): # GH438 objs = [Dataset({'y': ('t', [1])}, {'x': 1, 't': [0]}), Dataset({'y': ('t', [2])}, {'x': 1, 't': [0]})] expected = Dataset({'y': ('t', [1, 2])}, {'x': 1, 't': [0, 0]}) actual = concat(objs, 't') self.assertDatasetIdentical(expected, actual) objs = [Dataset({'y': ('t', [1])}, {'x': 1, 't': [0]}), Dataset({'y': ('t', [2])}, {'x': 2, 't': [0]})] with self.assertRaises(ValueError): concat(objs, 't', coords='minimal') def test_concat_dim_is_variable(self): objs = [Dataset({'x': 0}), Dataset({'x': 1})] coord = Variable('y', [3, 4]) expected = Dataset({'x': ('y', [0, 1]), 'y': [3, 4]}) actual = concat(objs, coord) self.assertDatasetIdentical(actual, expected) def test_concat_multiindex(self): x = pd.MultiIndex.from_product([[1, 2, 3], ['a', 'b']]) expected = Dataset({'x': x}) actual = concat([expected.isel(x=slice(2)), expected.isel(x=slice(2, None))], 'x') assert expected.equals(actual) assert isinstance(actual.x.to_index(), pd.MultiIndex) class TestConcatDataArray(TestCase): def test_concat(self): ds = Dataset({'foo': (['x', 'y'], np.random.random((2, 3))), 'bar': (['x', 'y'], np.random.random((2, 3)))}, {'x': [0, 1]}) foo = ds['foo'] bar = ds['bar'] # from dataset array: expected = DataArray(np.array([foo.values, bar.values]), dims=['w', 'x', 'y'], coords={'x': [0, 1]}) actual = concat([foo, bar], 'w') self.assertDataArrayEqual(expected, actual) # from iteration: grouped = [g for _, g in foo.groupby('x')] stacked = concat(grouped, ds['x']) self.assertDataArrayIdentical(foo, stacked) # with an index as the 'dim' argument stacked = concat(grouped, ds.indexes['x']) self.assertDataArrayIdentical(foo, stacked) actual = concat([foo[0], foo[1]], pd.Index([0, 1])).reset_coords(drop=True) expected = foo[:2].rename({'x': 'concat_dim'}) self.assertDataArrayIdentical(expected, actual) actual = concat([foo[0], foo[1]], [0, 1]).reset_coords(drop=True) expected = foo[:2].rename({'x': 'concat_dim'}) self.assertDataArrayIdentical(expected, actual) with self.assertRaisesRegexp(ValueError, 'not identical'): concat([foo, bar], dim='w', compat='identical') with self.assertRaisesRegexp(ValueError, 'not a valid argument'): concat([foo, bar], dim='w', data_vars='minimal') @requires_dask def test_concat_lazy(self): import dask.array as da arrays = [DataArray( da.from_array(InaccessibleArray(np.zeros((3, 3))), 3), dims=['x', 'y']) for _ in range(2)] # should not raise combined = concat(arrays, dim='z') self.assertEqual(combined.shape, (2, 3, 3)) self.assertEqual(combined.dims, ('z', 'x', 'y')) class TestAutoCombine(TestCase): @requires_dask # only for toolz def test_auto_combine(self): objs = [Dataset({'x': [0]}), Dataset({'x': [1]})] actual = auto_combine(objs) expected = Dataset({'x': [0, 1]}) self.assertDatasetIdentical(expected, actual) actual = auto_combine([actual]) self.assertDatasetIdentical(expected, actual) objs = [Dataset({'x': [0, 1]}), Dataset({'x': [2]})] actual = auto_combine(objs) expected = Dataset({'x': [0, 1, 2]}) self.assertDatasetIdentical(expected, actual) # ensure auto_combine handles non-sorted variables objs = [Dataset(OrderedDict([('x', ('a', [0])), ('y', ('a', [0]))])), Dataset(OrderedDict([('y', ('a', [1])), ('x', ('a', [1]))]))] actual = auto_combine(objs) expected = Dataset({'x': ('a', [0, 1]), 'y': ('a', [0, 1])}) self.assertDatasetIdentical(expected, actual) objs = [Dataset({'x': [0], 'y': [0]}), Dataset({'y': [1], 'x': [1]})] with self.assertRaisesRegexp(ValueError, 'too many .* dimensions'): auto_combine(objs) objs = [Dataset({'x': 0}), Dataset({'x': 1})] with self.assertRaisesRegexp(ValueError, 'cannot infer dimension'): auto_combine(objs) objs = [Dataset({'x': [0], 'y': [0]}), Dataset({'x': [0]})] with self.assertRaises(KeyError): auto_combine(objs) @requires_dask # only for toolz def test_auto_combine_previously_failed(self): # In the above scenario, one file is missing, containing the data for # one year's data for one variable. datasets = [Dataset({'a': ('x', [0]), 'x': [0]}), Dataset({'b': ('x', [0]), 'x': [0]}), Dataset({'a': ('x', [1]), 'x': [1]})] expected = Dataset({'a': ('x', [0, 1]), 'b': ('x', [0, np.nan])}, {'x': [0, 1]}) actual = auto_combine(datasets) self.assertDatasetIdentical(expected, actual) # Your data includes "time" and "station" dimensions, and each year's # data has a different set of stations. datasets = [Dataset({'a': ('x', [2, 3]), 'x': [1, 2]}), Dataset({'a': ('x', [1, 2]), 'x': [0, 1]})] expected = Dataset({'a': (('t', 'x'), [[np.nan, 2, 3], [1, 2, np.nan]])}, {'x': [0, 1, 2]}) actual = auto_combine(datasets, concat_dim='t') self.assertDatasetIdentical(expected, actual) @requires_dask # only for toolz def test_auto_combine_still_fails(self): # concat can't handle new variables (yet): # https://github.com/pydata/xarray/issues/508 datasets = [Dataset({'x': 0}, {'y': 0}), Dataset({'x': 1}, {'y': 1, 'z': 1})] with self.assertRaises(ValueError): auto_combine(datasets, 'y') @requires_dask # only for toolz def test_auto_combine_no_concat(self): objs = [Dataset({'x': 0}), Dataset({'y': 1})] actual = auto_combine(objs) expected = Dataset({'x': 0, 'y': 1}) self.assertDatasetIdentical(expected, actual) objs = [Dataset({'x': 0, 'y': 1}), Dataset({'y': np.nan, 'z': 2})] actual = auto_combine(objs) expected = Dataset({'x': 0, 'y': 1, 'z': 2}) self.assertDatasetIdentical(expected, actual) data = Dataset({'x': 0}) actual = auto_combine([data, data, data], concat_dim=None) self.assertDatasetIdentical(data, actual) ������������xarray-0.9.1/xarray/tests/test_computation.py�������������������������������������������������������0000644�1311115�0011610�00000054501�13043573225�021563� 0����������������������������������������������������������������������������������������������������ustar �shoyer��������������������������eng�����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������import functools import operator from collections import OrderedDict import numpy as np from numpy.testing import assert_array_equal import pytest import xarray as xr from xarray.core.computation import ( UFuncSignature, broadcast_compat_data, collect_dict_values, join_dict_keys, ordered_set_intersection, ordered_set_union, unified_dim_sizes, apply_ufunc) from . import requires_dask def assert_identical(a, b): if hasattr(a, 'identical'): msg = 'not identical:\n%r\n%r' % (a, b) assert a.identical(b), msg else: assert_array_equal(a, b) def test_parse_signature(): assert (UFuncSignature([['x']]) == UFuncSignature.from_string('(x)->()')) assert (UFuncSignature([['x', 'y']]) == UFuncSignature.from_string('(x,y)->()')) assert (UFuncSignature([['x'], ['y']]) == UFuncSignature.from_string('(x),(y)->()')) assert (UFuncSignature([['x']], [['y'], []]) == UFuncSignature.from_string('(x)->(y),()')) with pytest.raises(ValueError): UFuncSignature.from_string('(x)(y)->()') with pytest.raises(ValueError): UFuncSignature.from_string('(x),(y)->') with pytest.raises(ValueError): UFuncSignature.from_string('((x))->(x)') def test_signature_properties(): sig = UFuncSignature.from_string('(x),(x,y)->(z)') assert sig.input_core_dims == (('x',), ('x', 'y')) assert sig.output_core_dims == (('z',),) assert sig.all_input_core_dims == frozenset(['x', 'y']) assert sig.all_output_core_dims == frozenset(['z']) assert sig.n_inputs == 2 assert sig.n_outputs == 1 # dimension names matter assert UFuncSignature([['x']]) != UFuncSignature([['y']]) def test_ordered_set_union(): assert list(ordered_set_union([[1, 2]])) == [1, 2] assert list(ordered_set_union([[1, 2], [2, 1]])) == [1, 2] assert list(ordered_set_union([[0], [1, 2], [1, 3]])) == [0, 1, 2, 3] def test_ordered_set_intersection(): assert list(ordered_set_intersection([[1, 2]])) == [1, 2] assert list(ordered_set_intersection([[1, 2], [2, 1]])) == [1, 2] assert list(ordered_set_intersection([[1, 2], [1, 3]])) == [1] assert list(ordered_set_intersection([[1, 2], [2]])) == [2] def test_join_dict_keys(): dicts = [OrderedDict.fromkeys(keys) for keys in [['x', 'y'], ['y', 'z']]] assert list(join_dict_keys(dicts, 'left')) == ['x', 'y'] assert list(join_dict_keys(dicts, 'right')) == ['y', 'z'] assert list(join_dict_keys(dicts, 'inner')) == ['y'] assert list(join_dict_keys(dicts, 'outer')) == ['x', 'y', 'z'] with pytest.raises(KeyError): join_dict_keys(dicts, 'foobar') def test_collect_dict_values(): dicts = [{'x': 1, 'y': 2, 'z': 3}, {'z': 4}, 5] expected = [[1, 0, 5], [2, 0, 5], [3, 4, 5]] collected = collect_dict_values(dicts, ['x', 'y', 'z'], fill_value=0) assert collected == expected def test_apply_identity(): array = np.arange(10) variable = xr.Variable('x', array) data_array = xr.DataArray(variable, [('x', -array)]) dataset = xr.Dataset({'y': variable}, {'x': -array}) identity = functools.partial(apply_ufunc, lambda x: x) assert_identical(array, identity(array)) assert_identical(variable, identity(variable)) assert_identical(data_array, identity(data_array)) assert_identical(data_array, identity(data_array.groupby('x'))) assert_identical(dataset, identity(dataset)) assert_identical(dataset, identity(dataset.groupby('x'))) def add(a, b): return apply_ufunc(operator.add, a, b) def test_apply_two_inputs(): array = np.array([1, 2, 3]) variable = xr.Variable('x', array) data_array = xr.DataArray(variable, [('x', -array)]) dataset = xr.Dataset({'y': variable}, {'x': -array}) zero_array = np.zeros_like(array) zero_variable = xr.Variable('x', zero_array) zero_data_array = xr.DataArray(zero_variable, [('x', -array)]) zero_dataset = xr.Dataset({'y': zero_variable}, {'x': -array}) assert_identical(array, add(array, zero_array)) assert_identical(array, add(zero_array, array)) assert_identical(variable, add(variable, zero_array)) assert_identical(variable, add(variable, zero_variable)) assert_identical(variable, add(zero_array, variable)) assert_identical(variable, add(zero_variable, variable)) assert_identical(data_array, add(data_array, zero_array)) assert_identical(data_array, add(data_array, zero_variable)) assert_identical(data_array, add(data_array, zero_data_array)) assert_identical(data_array, add(zero_array, data_array)) assert_identical(data_array, add(zero_variable, data_array)) assert_identical(data_array, add(zero_data_array, data_array)) assert_identical(dataset, add(dataset, zero_array)) assert_identical(dataset, add(dataset, zero_variable)) assert_identical(dataset, add(dataset, zero_data_array)) assert_identical(dataset, add(dataset, zero_dataset)) assert_identical(dataset, add(zero_array, dataset)) assert_identical(dataset, add(zero_variable, dataset)) assert_identical(dataset, add(zero_data_array, dataset)) assert_identical(dataset, add(zero_dataset, dataset)) assert_identical(data_array, add(data_array.groupby('x'), zero_data_array)) assert_identical(data_array, add(zero_data_array, data_array.groupby('x'))) assert_identical(dataset, add(data_array.groupby('x'), zero_dataset)) assert_identical(dataset, add(zero_dataset, data_array.groupby('x'))) assert_identical(dataset, add(dataset.groupby('x'), zero_data_array)) assert_identical(dataset, add(dataset.groupby('x'), zero_dataset)) assert_identical(dataset, add(zero_data_array, dataset.groupby('x'))) assert_identical(dataset, add(zero_dataset, dataset.groupby('x'))) def test_apply_1d_and_0d(): array = np.array([1, 2, 3]) variable = xr.Variable('x', array) data_array = xr.DataArray(variable, [('x', -array)]) dataset = xr.Dataset({'y': variable}, {'x': -array}) zero_array = 0 zero_variable = xr.Variable((), zero_array) zero_data_array = xr.DataArray(zero_variable) zero_dataset = xr.Dataset({'y': zero_variable}) assert_identical(array, add(array, zero_array)) assert_identical(array, add(zero_array, array)) assert_identical(variable, add(variable, zero_array)) assert_identical(variable, add(variable, zero_variable)) assert_identical(variable, add(zero_array, variable)) assert_identical(variable, add(zero_variable, variable)) assert_identical(data_array, add(data_array, zero_array)) assert_identical(data_array, add(data_array, zero_variable)) assert_identical(data_array, add(data_array, zero_data_array)) assert_identical(data_array, add(zero_array, data_array)) assert_identical(data_array, add(zero_variable, data_array)) assert_identical(data_array, add(zero_data_array, data_array)) assert_identical(dataset, add(dataset, zero_array)) assert_identical(dataset, add(dataset, zero_variable)) assert_identical(dataset, add(dataset, zero_data_array)) assert_identical(dataset, add(dataset, zero_dataset)) assert_identical(dataset, add(zero_array, dataset)) assert_identical(dataset, add(zero_variable, dataset)) assert_identical(dataset, add(zero_data_array, dataset)) assert_identical(dataset, add(zero_dataset, dataset)) assert_identical(data_array, add(data_array.groupby('x'), zero_data_array)) assert_identical(data_array, add(zero_data_array, data_array.groupby('x'))) assert_identical(dataset, add(data_array.groupby('x'), zero_dataset)) assert_identical(dataset, add(zero_dataset, data_array.groupby('x'))) assert_identical(dataset, add(dataset.groupby('x'), zero_data_array)) assert_identical(dataset, add(dataset.groupby('x'), zero_dataset)) assert_identical(dataset, add(zero_data_array, dataset.groupby('x'))) assert_identical(dataset, add(zero_dataset, dataset.groupby('x'))) def test_apply_two_outputs(): array = np.arange(5) variable = xr.Variable('x', array) data_array = xr.DataArray(variable, [('x', -array)]) dataset = xr.Dataset({'y': variable}, {'x': -array}) def twice(obj): func = lambda x: (x, x) signature = '()->(),()' return apply_ufunc(func, obj, signature=signature) out0, out1 = twice(array) assert_identical(out0, array) assert_identical(out1, array) out0, out1 = twice(variable) assert_identical(out0, variable) assert_identical(out1, variable) out0, out1 = twice(data_array) assert_identical(out0, data_array) assert_identical(out1, data_array) out0, out1 = twice(dataset) assert_identical(out0, dataset) assert_identical(out1, dataset) out0, out1 = twice(data_array.groupby('x')) assert_identical(out0, data_array) assert_identical(out1, data_array) out0, out1 = twice(dataset.groupby('x')) assert_identical(out0, dataset) assert_identical(out1, dataset) def test_apply_input_core_dimension(): def first_element(obj, dim): func = lambda x: x[..., 0] sig = ([(dim,)], [()]) return apply_ufunc(func, obj, signature=sig) array = np.array([[1, 2], [3, 4]]) variable = xr.Variable(['x', 'y'], array) data_array = xr.DataArray(variable, {'x': ['a', 'b'], 'y': [-1, -2]}) dataset = xr.Dataset({'data': data_array}) expected_variable_x = xr.Variable(['y'], [1, 2]) expected_data_array_x = xr.DataArray(expected_variable_x, {'y': [-1, -2]}) expected_dataset_x = xr.Dataset({'data': expected_data_array_x}) expected_variable_y = xr.Variable(['x'], [1, 3]) expected_data_array_y = xr.DataArray(expected_variable_y, {'x': ['a', 'b']}) expected_dataset_y = xr.Dataset({'data': expected_data_array_y}) assert_identical(expected_variable_x, first_element(variable, 'x')) assert_identical(expected_variable_y, first_element(variable, 'y')) assert_identical(expected_data_array_x, first_element(data_array, 'x')) assert_identical(expected_data_array_y, first_element(data_array, 'y')) assert_identical(expected_dataset_x, first_element(dataset, 'x')) assert_identical(expected_dataset_y, first_element(dataset, 'y')) assert_identical(expected_data_array_x, first_element(data_array.groupby('y'), 'x')) assert_identical(expected_dataset_x, first_element(dataset.groupby('y'), 'x')) def test_apply_output_core_dimension(): def stack_negative(obj): func = lambda x: xr.core.npcompat.stack([x, -x], axis=-1) sig = ([()], [('sign',)]) result = apply_ufunc(func, obj, signature=sig) if isinstance(result, (xr.Dataset, xr.DataArray)): result.coords['sign'] = [1, -1] return result array = np.array([[1, 2], [3, 4]]) variable = xr.Variable(['x', 'y'], array) data_array = xr.DataArray(variable, {'x': ['a', 'b'], 'y': [-1, -2]}) dataset = xr.Dataset({'data': data_array}) stacked_array = np.array([[[1, -1], [2, -2]], [[3, -3], [4, -4]]]) stacked_variable = xr.Variable(['x', 'y', 'sign'], stacked_array) stacked_coords = {'x': ['a', 'b'], 'y': [-1, -2], 'sign': [1, -1]} stacked_data_array = xr.DataArray(stacked_variable, stacked_coords) stacked_dataset = xr.Dataset({'data': stacked_data_array}) assert_identical(stacked_array, stack_negative(array)) assert_identical(stacked_variable, stack_negative(variable)) assert_identical(stacked_data_array, stack_negative(data_array)) assert_identical(stacked_dataset, stack_negative(dataset)) assert_identical(stacked_data_array, stack_negative(data_array.groupby('x'))) assert_identical(stacked_dataset, stack_negative(dataset.groupby('x'))) def original_and_stack_negative(obj): func = lambda x: (x, xr.core.npcompat.stack([x, -x], axis=-1)) sig = ([()], [(), ('sign',)]) result = apply_ufunc(func, obj, signature=sig) if isinstance(result[1], (xr.Dataset, xr.DataArray)): result[1].coords['sign'] = [1, -1] return result out0, out1 = original_and_stack_negative(array) assert_identical(array, out0) assert_identical(stacked_array, out1) out0, out1 = original_and_stack_negative(variable) assert_identical(variable, out0) assert_identical(stacked_variable, out1) out0, out1 = original_and_stack_negative(data_array) assert_identical(data_array, out0) assert_identical(stacked_data_array, out1) out0, out1 = original_and_stack_negative(dataset) assert_identical(dataset, out0) assert_identical(stacked_dataset, out1) out0, out1 = original_and_stack_negative(data_array.groupby('x')) assert_identical(data_array, out0) assert_identical(stacked_data_array, out1) out0, out1 = original_and_stack_negative(dataset.groupby('x')) assert_identical(dataset, out0) assert_identical(stacked_dataset, out1) def stack_invalid(obj): func = lambda x: xr.core.npcompat.stack([x, -x], axis=-1) sig = ([()], [('sign',)]) # no new_coords return apply_ufunc(func, obj, signature=sig, dataset_fill_value=np.nan) def test_apply_exclude(): def concatenate(objects, dim='x'): sig = ([(dim,)] * len(objects), [(dim,)]) new_coord = np.concatenate( [obj.coords[dim] if hasattr(obj, 'coords') else [] for obj in objects]) func = lambda *x: np.concatenate(x, axis=-1) result = apply_ufunc(func, *objects, signature=sig, exclude_dims={dim}) if isinstance(result, (xr.Dataset, xr.DataArray)): result.coords[dim] = new_coord return result arrays = [np.array([1]), np.array([2, 3])] variables = [xr.Variable('x', a) for a in arrays] data_arrays = [xr.DataArray(v, {'x': c, 'y': ('x', range(len(c)))}) for v, c in zip(variables, [['a'], ['b', 'c']])] datasets = [xr.Dataset({'data': data_array}) for data_array in data_arrays] expected_array = np.array([1, 2, 3]) expected_variable = xr.Variable('x', expected_array) expected_data_array = xr.DataArray(expected_variable, [('x', list('abc'))]) expected_dataset = xr.Dataset({'data': expected_data_array}) assert_identical(expected_array, concatenate(arrays)) assert_identical(expected_variable, concatenate(variables)) assert_identical(expected_data_array, concatenate(data_arrays)) assert_identical(expected_dataset, concatenate(datasets)) identity = lambda x: x # must also be a core dimension with pytest.raises(ValueError): apply_ufunc(identity, variables[0], exclude_dims={'x'}) def test_apply_groupby_add(): array = np.arange(5) variable = xr.Variable('x', array) coords = {'x': -array, 'y': ('x', [0, 0, 1, 1, 2])} data_array = xr.DataArray(variable, coords, dims='x') dataset = xr.Dataset({'z': variable}, coords) other_variable = xr.Variable('y', [0, 10]) other_data_array = xr.DataArray(other_variable, dims='y') other_dataset = xr.Dataset({'z': other_variable}) expected_variable = xr.Variable('x', [0, 1, 12, 13, np.nan]) expected_data_array = xr.DataArray(expected_variable, coords, dims='x') expected_dataset = xr.Dataset({'z': expected_variable}, coords) assert_identical(expected_data_array, add(data_array.groupby('y'), other_data_array)) assert_identical(expected_dataset, add(data_array.groupby('y'), other_dataset)) assert_identical(expected_dataset, add(dataset.groupby('y'), other_data_array)) assert_identical(expected_dataset, add(dataset.groupby('y'), other_dataset)) # cannot be performed with xarray.Variable objects that share a dimension with pytest.raises(ValueError): add(data_array.groupby('y'), other_variable) # if they are all grouped the same way with pytest.raises(ValueError): add(data_array.groupby('y'), data_array[:4].groupby('y')) with pytest.raises(ValueError): add(data_array.groupby('y'), data_array[1:].groupby('y')) with pytest.raises(ValueError): add(data_array.groupby('y'), other_data_array.groupby('y')) with pytest.raises(ValueError): add(data_array.groupby('y'), data_array.groupby('x')) def test_unified_dim_sizes(): assert unified_dim_sizes([xr.Variable((), 0)]) == OrderedDict() assert (unified_dim_sizes([xr.Variable('x', [1]), xr.Variable('x', [1])]) == OrderedDict([('x', 1)])) assert (unified_dim_sizes([xr.Variable('x', [1]), xr.Variable('y', [1, 2])]) == OrderedDict([('x', 1), ('y', 2)])) assert (unified_dim_sizes([xr.Variable(('x', 'z'), [[1]]), xr.Variable(('y', 'z'), [[1, 2], [3, 4]])], exclude_dims={'z'}) == OrderedDict([('x', 1), ('y', 2)])) # duplicate dimensions with pytest.raises(ValueError): unified_dim_sizes([xr.Variable(('x', 'x'), [[1]])]) # mismatched lengths with pytest.raises(ValueError): unified_dim_sizes( [xr.Variable('x', [1]), xr.Variable('x', [1, 2])]) def test_broadcast_compat_data_1d(): data = np.arange(5) var = xr.Variable('x', data) assert_identical(data, broadcast_compat_data(var, ('x',), ())) assert_identical(data, broadcast_compat_data(var, (), ('x',))) assert_identical(data[None, :], broadcast_compat_data(var, ('w',), ('x',))) assert_identical(data[None, :, None], broadcast_compat_data(var, ('w', 'x', 'y'), ())) with pytest.raises(ValueError): broadcast_compat_data(var, ('x',), ('w',)) with pytest.raises(ValueError): broadcast_compat_data(var, (), ()) def test_broadcast_compat_data_2d(): data = np.arange(12).reshape(3, 4) var = xr.Variable(['x', 'y'], data) assert_identical(data, broadcast_compat_data(var, ('x', 'y'), ())) assert_identical(data, broadcast_compat_data(var, ('x',), ('y',))) assert_identical(data, broadcast_compat_data(var, (), ('x', 'y'))) assert_identical(data.T, broadcast_compat_data(var, ('y', 'x'), ())) assert_identical(data.T, broadcast_compat_data(var, ('y',), ('x',))) assert_identical(data[None, :, :], broadcast_compat_data(var, ('w', 'x'), ('y',))) assert_identical(data[None, :, :], broadcast_compat_data(var, ('w',), ('x', 'y'))) assert_identical(data.T[None, :, :], broadcast_compat_data(var, ('w',), ('y', 'x'))) assert_identical(data[None, :, :, None], broadcast_compat_data(var, ('w', 'x', 'y', 'z'), ())) assert_identical(data.T[None, :, :, None], broadcast_compat_data(var, ('w', 'y', 'x', 'z'), ())) def test_keep_attrs(): def add(a, b, keep_attrs): if keep_attrs: return apply_ufunc(operator.add, a, b, keep_attrs=keep_attrs) else: return apply_ufunc(operator.add, a, b) a = xr.DataArray([0, 1], [('x', [0, 1])]) a.attrs['attr'] = 'da' b = xr.DataArray([1, 2], [('x', [0, 1])]) actual = add(a, b, keep_attrs=False) assert not actual.attrs actual = add(a, b, keep_attrs=True) assert_identical(actual.attrs, a.attrs) a = xr.Dataset({'x': ('x', [1, 2]), 'x': [0, 1]}) a.attrs['attr'] = 'ds' a.x.attrs['attr'] = 'da' b = xr.Dataset({'x': ('x', [1, 1]), 'x': [0, 1]}) actual = add(a, b, keep_attrs=False) assert not actual.attrs actual = add(a, b, keep_attrs=True) assert_identical(actual.attrs, a.attrs) assert_identical(actual.x.attrs, a.x.attrs) def test_dataset_join(): import numpy as np ds0 = xr.Dataset({'a': ('x', [1, 2]), 'x': [0, 1]}) ds1 = xr.Dataset({'a': ('x', [99, 3]), 'x': [1, 2]}) with pytest.raises(TypeError): apply_ufunc(operator.add, ds0, ds1, dataset_join='outer') def add(a, b, join, dataset_join): return apply_ufunc(operator.add, a, b, join=join, dataset_join=dataset_join, dataset_fill_value=np.nan) actual = add(ds0, ds1, 'outer', 'inner') expected = xr.Dataset({'a': ('x', [np.nan, 101, np.nan]), 'x': [0, 1, 2]}) assert_identical(actual, expected) actual = add(ds0, ds1, 'outer', 'outer') assert_identical(actual, expected) # if variables don't match, join will perform add with np.nan ds2 = xr.Dataset({'b': ('x', [99, 3]), 'x': [1, 2]}) actual = add(ds0, ds2, 'outer', 'inner') expected = xr.Dataset({'x': [0, 1, 2]}) assert_identical(actual, expected) actual = add(ds0, ds2, 'outer', 'outer') expected = xr.Dataset({'a': ('x', [np.nan, np.nan, np.nan]), 'b': ('x', [np.nan, np.nan, np.nan]), 'x': [0, 1, 2]}) assert_identical(actual, expected) class _NoCacheVariable(xr.Variable): """Subclass of Variable for testing that does not cache values.""" # TODO: remove this class when we change the default behavior for caching # dask.array objects. def _data_cached(self): return np.asarray(self._data) @requires_dask def test_apply_dask(): import dask.array as da array = da.ones((2,), chunks=2) variable = _NoCacheVariable('x', array) coords = xr.DataArray(variable).coords.variables data_array = xr.DataArray(variable, coords, fastpath=True) dataset = xr.Dataset({'y': variable}) identity = lambda x: x # encountered dask array, but did not set dask_array='allowed' with pytest.raises(ValueError): apply_ufunc(identity, array) with pytest.raises(ValueError): apply_ufunc(identity, variable) with pytest.raises(ValueError): apply_ufunc(identity, data_array) with pytest.raises(ValueError): apply_ufunc(identity, dataset) # unknown setting for dask array handling with pytest.raises(ValueError): apply_ufunc(identity, array, dask_array='auto') def dask_safe_identity(x): return apply_ufunc(identity, x, dask_array='allowed') assert array is dask_safe_identity(array) actual = dask_safe_identity(variable) assert isinstance(actual.data, da.Array) assert_identical(variable, actual) actual = dask_safe_identity(data_array) assert isinstance(actual.data, da.Array) assert_identical(data_array, actual) actual = dask_safe_identity(dataset) assert isinstance(actual['y'].data, da.Array) assert_identical(dataset, actual) �����������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������xarray-0.9.1/xarray/tests/test_conventions.py�������������������������������������������������������0000644�1311115�0011610�00000071465�13043511243�021566� 0����������������������������������������������������������������������������������������������������ustar �shoyer��������������������������eng�����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������from __future__ import absolute_import from __future__ import division from __future__ import print_function import contextlib import numpy as np import pandas as pd import warnings from xarray import conventions, Variable, Dataset, open_dataset from xarray.core import utils, indexing from . import TestCase, requires_netCDF4, unittest from .test_backends import CFEncodedDataTest from xarray.core.pycompat import iteritems from xarray.backends.memory import InMemoryDataStore from xarray.backends.common import WritableCFDataStore from xarray.conventions import decode_cf class TestMaskedAndScaledArray(TestCase): def test(self): x = conventions.MaskedAndScaledArray(np.arange(3), fill_value=0) self.assertEqual(x.dtype, np.dtype('float')) self.assertEqual(x.shape, (3,)) self.assertEqual(x.size, 3) self.assertEqual(x.ndim, 1) self.assertEqual(len(x), 3) self.assertArrayEqual([np.nan, 1, 2], x) x = conventions.MaskedAndScaledArray(np.arange(3), add_offset=1) self.assertArrayEqual(np.arange(3) + 1, x) x = conventions.MaskedAndScaledArray(np.arange(3), scale_factor=2) self.assertArrayEqual(2 * np.arange(3), x) x = conventions.MaskedAndScaledArray(np.array([-99, -1, 0, 1, 2]), -99, 0.01, 1) expected = np.array([np.nan, 0.99, 1, 1.01, 1.02]) self.assertArrayEqual(expected, x) def test_0d(self): x = conventions.MaskedAndScaledArray(np.array(0), fill_value=0) self.assertTrue(np.isnan(x)) self.assertTrue(np.isnan(x[...])) x = conventions.MaskedAndScaledArray(np.array(0), fill_value=10) self.assertEqual(0, x[...]) def test_multiple_fill_value(self): x = conventions.MaskedAndScaledArray( np.arange(4), fill_value=np.array([0, 1])) self.assertArrayEqual([np.nan, np.nan, 2, 3], x) x = conventions.MaskedAndScaledArray( np.array(0), fill_value=np.array([0, 1])) self.assertTrue(np.isnan(x)) self.assertTrue(np.isnan(x[...])) class TestCharToStringArray(TestCase): def test_wrapper_class(self): array = np.array(list('abc'), dtype='S') actual = conventions.CharToStringArray(array) expected = np.array('abc', dtype='S') self.assertEqual(actual.dtype, expected.dtype) self.assertEqual(actual.shape, expected.shape) self.assertEqual(actual.size, expected.size) self.assertEqual(actual.ndim, expected.ndim) with self.assertRaises(TypeError): len(actual) self.assertArrayEqual(expected, actual) with self.assertRaises(IndexError): actual[:2] self.assertEqual(str(actual), 'abc') array = np.array([list('abc'), list('cdf')], dtype='S') actual = conventions.CharToStringArray(array) expected = np.array(['abc', 'cdf'], dtype='S') self.assertEqual(actual.dtype, expected.dtype) self.assertEqual(actual.shape, expected.shape) self.assertEqual(actual.size, expected.size) self.assertEqual(actual.ndim, expected.ndim) self.assertEqual(len(actual), len(expected)) self.assertArrayEqual(expected, actual) self.assertArrayEqual(expected[:1], actual[:1]) with self.assertRaises(IndexError): actual[:, :2] def test_char_to_string(self): array = np.array([['a', 'b', 'c'], ['d', 'e', 'f']]) expected = np.array(['abc', 'def']) actual = conventions.char_to_string(array) self.assertArrayEqual(actual, expected) expected = np.array(['ad', 'be', 'cf']) actual = conventions.char_to_string(array.T) # non-contiguous self.assertArrayEqual(actual, expected) def test_string_to_char(self): array = np.array([['ab', 'cd'], ['ef', 'gh']]) expected = np.array([[['a', 'b'], ['c', 'd']], [['e', 'f'], ['g', 'h']]]) actual = conventions.string_to_char(array) self.assertArrayEqual(actual, expected) expected = np.array([[['a', 'b'], ['e', 'f']], [['c', 'd'], ['g', 'h']]]) actual = conventions.string_to_char(array.T) self.assertArrayEqual(actual, expected) class TestBoolTypeArray(TestCase): def test_booltype_array(self): x = np.array([1, 0, 1, 1, 0], dtype='i1') bx = conventions.BoolTypeArray(x) self.assertEqual(bx.dtype, np.bool) self.assertArrayEqual(bx, np.array([True, False, True, True, False], dtype=np.bool)) @np.vectorize def _ensure_naive_tz(dt): if hasattr(dt, 'tzinfo'): return dt.replace(tzinfo=None) else: return dt class TestDatetime(TestCase): @requires_netCDF4 def test_cf_datetime(self): import netCDF4 as nc4 for num_dates, units in [ (np.arange(10), 'days since 2000-01-01'), (np.arange(10).reshape(2, 5), 'days since 2000-01-01'), (12300 + np.arange(5), 'hours since 1680-01-01 00:00:00'), # here we add a couple minor formatting errors to test # the robustness of the parsing algorithm. (12300 + np.arange(5), 'hour since 1680-01-01 00:00:00'), (12300 + np.arange(5), u'Hour since 1680-01-01 00:00:00'), (12300 + np.arange(5), ' Hour since 1680-01-01 00:00:00 '), (10, 'days since 2000-01-01'), ([10], 'daYs since 2000-01-01'), ([[10]], 'days since 2000-01-01'), ([10, 10], 'days since 2000-01-01'), (np.array(10), 'days since 2000-01-01'), (0, 'days since 1000-01-01'), ([0], 'days since 1000-01-01'), ([[0]], 'days since 1000-01-01'), (np.arange(2), 'days since 1000-01-01'), (np.arange(0, 100000, 20000), 'days since 1900-01-01'), (17093352.0, 'hours since 1-1-1 00:00:0.0'), ([0.5, 1.5], 'hours since 1900-01-01T00:00:00'), (0, 'milliseconds since 2000-01-01T00:00:00'), (0, 'microseconds since 2000-01-01T00:00:00'), ]: for calendar in ['standard', 'gregorian', 'proleptic_gregorian']: expected = _ensure_naive_tz(nc4.num2date(num_dates, units, calendar)) print(num_dates, units, calendar) with warnings.catch_warnings(): warnings.filterwarnings('ignore', 'Unable to decode time axis') actual = conventions.decode_cf_datetime(num_dates, units, calendar) if (isinstance(actual, np.ndarray) and np.issubdtype(actual.dtype, np.datetime64)): # self.assertEqual(actual.dtype.kind, 'M') # For some reason, numpy 1.8 does not compare ns precision # datetime64 arrays as equal to arrays of datetime objects, # but it works for us precision. Thus, convert to us # precision for the actual array equal comparison... actual_cmp = actual.astype('M8[us]') else: actual_cmp = actual self.assertArrayEqual(expected, actual_cmp) encoded, _, _ = conventions.encode_cf_datetime(actual, units, calendar) if '1-1-1' not in units: # pandas parses this date very strangely, so the original # units/encoding cannot be preserved in this case: # (Pdb) pd.to_datetime('1-1-1 00:00:0.0') # Timestamp('2001-01-01 00:00:00') self.assertArrayEqual(num_dates, np.around(encoded, 1)) if (hasattr(num_dates, 'ndim') and num_dates.ndim == 1 and '1000' not in units): # verify that wrapping with a pandas.Index works # note that it *does not* currently work to even put # non-datetime64 compatible dates into a pandas.Index :( encoded, _, _ = conventions.encode_cf_datetime( pd.Index(actual), units, calendar) self.assertArrayEqual(num_dates, np.around(encoded, 1)) @requires_netCDF4 def test_decode_cf_datetime_overflow(self): # checks for # https://github.com/pydata/pandas/issues/14068 # https://github.com/pydata/xarray/issues/975 from datetime import datetime units = 'days since 2000-01-01 00:00:00' # date after 2262 and before 1678 days = (-117608, 95795) expected = (datetime(1677, 12, 31), datetime(2262, 4, 12)) for i, day in enumerate(days): result = conventions.decode_cf_datetime(day, units) self.assertEqual(result, expected[i]) @requires_netCDF4 def test_decode_cf_datetime_transition_to_invalid(self): # manually create dataset with not-decoded date from datetime import datetime ds = Dataset(coords={'time' : [0, 266 * 365]}) units = 'days since 2000-01-01 00:00:00' ds.time.attrs = dict(units=units) ds_decoded = conventions.decode_cf(ds) expected = [datetime(2000, 1, 1, 0, 0), datetime(2265, 10, 28, 0, 0)] self.assertArrayEqual(ds_decoded.time.values, expected) def test_decoded_cf_datetime_array(self): actual = conventions.DecodedCFDatetimeArray( np.array([0, 1, 2]), 'days since 1900-01-01', 'standard') expected = pd.date_range('1900-01-01', periods=3).values self.assertEqual(actual.dtype, np.dtype('datetime64[ns]')) self.assertArrayEqual(actual, expected) # default calendar actual = conventions.DecodedCFDatetimeArray( np.array([0, 1, 2]), 'days since 1900-01-01') self.assertEqual(actual.dtype, np.dtype('datetime64[ns]')) self.assertArrayEqual(actual, expected) def test_slice_decoded_cf_datetime_array(self): actual = conventions.DecodedCFDatetimeArray( np.array([0, 1, 2]), 'days since 1900-01-01', 'standard') expected = pd.date_range('1900-01-01', periods=3).values self.assertEqual(actual.dtype, np.dtype('datetime64[ns]')) self.assertArrayEqual(actual[slice(0, 2)], expected[slice(0, 2)]) actual = conventions.DecodedCFDatetimeArray( np.array([0, 1, 2]), 'days since 1900-01-01', 'standard') expected = pd.date_range('1900-01-01', periods=3).values self.assertEqual(actual.dtype, np.dtype('datetime64[ns]')) self.assertArrayEqual(actual[[0, 2]], expected[[0, 2]]) def test_decode_cf_datetime_non_standard_units(self): expected = pd.date_range(periods=100, start='1970-01-01', freq='h') # netCDFs from madis.noaa.gov use this format for their time units # they cannot be parsed by netcdftime, but pd.Timestamp works units = 'hours since 1-1-1970' actual = conventions.decode_cf_datetime(np.arange(100), units) self.assertArrayEqual(actual, expected) def test_decode_cf_with_conflicting_fill_missing_value(self): var = Variable(['t'], np.arange(10), {'units': 'foobar', 'missing_value': 0, '_FillValue': 1}) self.assertRaisesRegexp(ValueError, "_FillValue and missing_value", lambda: conventions.decode_cf_variable(var)) var = Variable(['t'], np.arange(10), {'units': 'foobar', 'missing_value': np.nan, '_FillValue': np.nan}) var = conventions.decode_cf_variable(var) self.assertIsNotNone(var) var = Variable(['t'], np.arange(10), {'units': 'foobar', 'missing_value': np.float32(np.nan), '_FillValue': np.float32(np.nan)}) var = conventions.decode_cf_variable(var) self.assertIsNotNone(var) @requires_netCDF4 def test_decode_cf_datetime_non_iso_strings(self): # datetime strings that are _almost_ ISO compliant but not quite, # but which netCDF4.num2date can still parse correctly expected = pd.date_range(periods=100, start='2000-01-01', freq='h') cases = [(np.arange(100), 'hours since 2000-01-01 0'), (np.arange(100), 'hours since 2000-1-1 0'), (np.arange(100), 'hours since 2000-01-01 0:00')] for num_dates, units in cases: actual = conventions.decode_cf_datetime(num_dates, units) self.assertArrayEqual(actual, expected) @requires_netCDF4 def test_decode_non_standard_calendar(self): import netCDF4 as nc4 for calendar in ['noleap', '365_day', '360_day', 'julian', 'all_leap', '366_day']: units = 'days since 0001-01-01' times = pd.date_range('2001-04-01-00', end='2001-04-30-23', freq='H') noleap_time = nc4.date2num(times.to_pydatetime(), units, calendar=calendar) expected = times.values with warnings.catch_warnings(): warnings.filterwarnings('ignore', 'Unable to decode time axis') actual = conventions.decode_cf_datetime(noleap_time, units, calendar=calendar) self.assertEqual(actual.dtype, np.dtype('M8[ns]')) abs_diff = abs(actual - expected) # once we no longer support versions of netCDF4 older than 1.1.5, # we could do this check with near microsecond accuracy: # https://github.com/Unidata/netcdf4-python/issues/355 self.assertTrue((abs_diff <= np.timedelta64(1, 's')).all()) @requires_netCDF4 def test_decode_non_standard_calendar_single_element(self): units = 'days since 0001-01-01' for calendar in ['noleap', '365_day', '360_day', 'julian', 'all_leap', '366_day']: for num_time in [735368, [735368], [[735368]]]: with warnings.catch_warnings(): warnings.filterwarnings('ignore', 'Unable to decode time axis') actual = conventions.decode_cf_datetime(num_time, units, calendar=calendar) self.assertEqual(actual.dtype, np.dtype('M8[ns]')) @requires_netCDF4 def test_decode_non_standard_calendar_single_element_fallback(self): import netCDF4 as nc4 units = 'days since 0001-01-01' dt = nc4.netcdftime.datetime(2001, 2, 29) for calendar in ['360_day', 'all_leap', '366_day']: num_time = nc4.date2num(dt, units, calendar) with self.assertWarns('Unable to decode time axis'): actual = conventions.decode_cf_datetime(num_time, units, calendar=calendar) expected = np.asarray(nc4.num2date(num_time, units, calendar)) print(num_time, calendar, actual, expected) self.assertEqual(actual.dtype, np.dtype('O')) self.assertEqual(expected, actual) @requires_netCDF4 def test_decode_non_standard_calendar_multidim_time(self): import netCDF4 as nc4 calendar = 'noleap' units = 'days since 0001-01-01' times1 = pd.date_range('2001-04-01', end='2001-04-05', freq='D') times2 = pd.date_range('2001-05-01', end='2001-05-05', freq='D') noleap_time1 = nc4.date2num(times1.to_pydatetime(), units, calendar=calendar) noleap_time2 = nc4.date2num(times2.to_pydatetime(), units, calendar=calendar) mdim_time = np.empty((len(noleap_time1), 2), ) mdim_time[:, 0] = noleap_time1 mdim_time[:, 1] = noleap_time2 expected1 = times1.values expected2 = times2.values with warnings.catch_warnings(): warnings.filterwarnings('ignore', 'Unable to decode time axis') actual = conventions.decode_cf_datetime(mdim_time, units, calendar=calendar) self.assertEqual(actual.dtype, np.dtype('M8[ns]')) self.assertArrayEqual(actual[:, 0], expected1) self.assertArrayEqual(actual[:, 1], expected2) @requires_netCDF4 def test_decode_non_standard_calendar_fallback(self): import netCDF4 as nc4 # ensure leap year doesn't matter for year in [2010, 2011, 2012, 2013, 2014]: for calendar in ['360_day', '366_day', 'all_leap']: calendar = '360_day' units = 'days since {0}-01-01'.format(year) num_times = np.arange(100) expected = nc4.num2date(num_times, units, calendar) with warnings.catch_warnings(record=True) as w: warnings.simplefilter('always') actual = conventions.decode_cf_datetime(num_times, units, calendar=calendar) self.assertEqual(len(w), 1) self.assertIn('Unable to decode time axis', str(w[0].message)) self.assertEqual(actual.dtype, np.dtype('O')) self.assertArrayEqual(actual, expected) @requires_netCDF4 def test_cf_datetime_nan(self): for num_dates, units, expected_list in [ ([np.nan], 'days since 2000-01-01', ['NaT']), ([np.nan, 0], 'days since 2000-01-01', ['NaT', '2000-01-01T00:00:00Z']), ([np.nan, 0, 1], 'days since 2000-01-01', ['NaT', '2000-01-01T00:00:00Z', '2000-01-02T00:00:00Z']), ]: with warnings.catch_warnings(): warnings.filterwarnings('ignore', 'All-NaN') actual = conventions.decode_cf_datetime(num_dates, units) expected = np.array(expected_list, dtype='datetime64[ns]') self.assertArrayEqual(expected, actual) @requires_netCDF4 def test_decoded_cf_datetime_array_2d(self): # regression test for GH1229 array = conventions.DecodedCFDatetimeArray(np.array([[0, 1], [2, 3]]), 'days since 2000-01-01') assert array.dtype == 'datetime64[ns]' expected = pd.date_range('2000-01-01', periods=4).values.reshape(2, 2) self.assertArrayEqual(np.asarray(array), expected) def test_infer_datetime_units(self): for dates, expected in [(pd.date_range('1900-01-01', periods=5), 'days since 1900-01-01 00:00:00'), (pd.date_range('1900-01-01 12:00:00', freq='H', periods=2), 'hours since 1900-01-01 12:00:00'), (['1900-01-01', '1900-01-02', '1900-01-02 00:00:01'], 'seconds since 1900-01-01 00:00:00'), (pd.to_datetime(['1900-01-01', '1900-01-02', 'NaT']), 'days since 1900-01-01 00:00:00'), (pd.to_datetime(['1900-01-01', '1900-01-02T00:00:00.005']), 'seconds since 1900-01-01 00:00:00'), (pd.to_datetime(['NaT', '1900-01-01']), 'days since 1900-01-01 00:00:00'), (pd.to_datetime(['NaT']), 'days since 1970-01-01 00:00:00'), ]: self.assertEqual(expected, conventions.infer_datetime_units(dates)) def test_cf_timedelta(self): examples = [ ('1D', 'days', np.int64(1)), (['1D', '2D', '3D'], 'days', np.array([1, 2, 3], 'int64')), ('1h', 'hours', np.int64(1)), ('1ms', 'milliseconds', np.int64(1)), ('1us', 'microseconds', np.int64(1)), (['NaT', '0s', '1s'], None, [np.nan, 0, 1]), (['30m', '60m'], 'hours', [0.5, 1.0]), ] if pd.__version__ >= '0.16': # not quite sure why, but these examples don't work on older pandas examples.extend([(np.timedelta64('NaT', 'ns'), 'days', np.nan), (['NaT', 'NaT'], 'days', [np.nan, np.nan])]) for timedeltas, units, numbers in examples: timedeltas = pd.to_timedelta(timedeltas, box=False) numbers = np.array(numbers) expected = numbers actual, _ = conventions.encode_cf_timedelta(timedeltas, units) self.assertArrayEqual(expected, actual) self.assertEqual(expected.dtype, actual.dtype) if units is not None: expected = timedeltas actual = conventions.decode_cf_timedelta(numbers, units) self.assertArrayEqual(expected, actual) self.assertEqual(expected.dtype, actual.dtype) expected = np.timedelta64('NaT', 'ns') actual = conventions.decode_cf_timedelta(np.array(np.nan), 'days') self.assertArrayEqual(expected, actual) def test_cf_timedelta_2d(self): timedeltas, units, numbers = ['1D', '2D', '3D'], 'days', np.atleast_2d([1, 2, 3]) timedeltas = np.atleast_2d(pd.to_timedelta(timedeltas, box=False)) expected = timedeltas actual = conventions.decode_cf_timedelta(numbers, units) self.assertArrayEqual(expected, actual) self.assertEqual(expected.dtype, actual.dtype) def test_infer_timedelta_units(self): for deltas, expected in [ (pd.to_timedelta(['1 day', '2 days']), 'days'), (pd.to_timedelta(['1h', '1 day 1 hour']), 'hours'), (pd.to_timedelta(['1m', '2m', np.nan]), 'minutes'), (pd.to_timedelta(['1m3s', '1m4s']), 'seconds')]: self.assertEqual(expected, conventions.infer_timedelta_units(deltas)) def test_invalid_units_raises_eagerly(self): ds = Dataset({'time': ('time', [0, 1], {'units': 'foobar since 123'})}) with self.assertRaisesRegexp(ValueError, 'unable to decode time'): decode_cf(ds) @requires_netCDF4 def test_dataset_repr_with_netcdf4_datetimes(self): # regression test for #347 attrs = {'units': 'days since 0001-01-01', 'calendar': 'noleap'} with warnings.catch_warnings(): warnings.filterwarnings('ignore', 'unable to decode time') ds = decode_cf(Dataset({'time': ('time', [0, 1], attrs)})) self.assertIn('(time) object', repr(ds)) attrs = {'units': 'days since 1900-01-01'} ds = decode_cf(Dataset({'time': ('time', [0, 1], attrs)})) self.assertIn('(time) datetime64[ns]', repr(ds)) # this should not throw a warning (GH1111) with warnings.catch_warnings(): warnings.filterwarnings('error') conventions.DecodedCFDatetimeArray(np.asarray([722624]), "days since 0001-01-01") class TestNativeEndiannessArray(TestCase): def test(self): x = np.arange(5, dtype='>i8') expected = np.arange(5, dtype='int64') a = conventions.NativeEndiannessArray(x) assert a.dtype == expected.dtype assert a.dtype == expected[:].dtype self.assertArrayEqual(a, expected) @requires_netCDF4 class TestEncodeCFVariable(TestCase): def test_incompatible_attributes(self): invalid_vars = [ Variable(['t'], pd.date_range('2000-01-01', periods=3), {'units': 'foobar'}), Variable(['t'], pd.to_timedelta(['1 day']), {'units': 'foobar'}), Variable(['t'], [0, 1, 2], {'add_offset': 0}, {'add_offset': 2}), Variable(['t'], [0, 1, 2], {'_FillValue': 0}, {'_FillValue': 2}), ] for var in invalid_vars: with self.assertRaises(ValueError): conventions.encode_cf_variable(var) def test_missing_fillvalue(self): v = Variable(['x'], np.array([np.nan, 1, 2, 3])) v.encoding = {'dtype': 'int16'} with self.assertWarns('floating point data as an integer'): conventions.encode_cf_variable(v) @requires_netCDF4 class TestDecodeCF(TestCase): def test_dataset(self): original = Dataset({ 't': ('t', [0, 1, 2], {'units': 'days since 2000-01-01'}), 'foo': ('t', [0, 0, 0], {'coordinates': 'y', 'units': 'bar'}), 'y': ('t', [5, 10, -999], {'_FillValue': -999}) }) expected = Dataset({'foo': ('t', [0, 0, 0], {'units': 'bar'})}, {'t': pd.date_range('2000-01-01', periods=3), 'y': ('t', [5.0, 10.0, np.nan])}) actual = conventions.decode_cf(original) self.assertDatasetIdentical(expected, actual) def test_invalid_coordinates(self): # regression test for GH308 original = Dataset({'foo': ('t', [1, 2], {'coordinates': 'invalid'})}) actual = conventions.decode_cf(original) self.assertDatasetIdentical(original, actual) def test_decode_coordinates(self): # regression test for GH610 original = Dataset({'foo': ('t', [1, 2], {'coordinates': 'x'}), 'x': ('t', [4, 5])}) actual = conventions.decode_cf(original) self.assertEqual(actual.foo.encoding['coordinates'], 'x') def test_0d_int32_encoding(self): original = Variable((), np.int32(0), encoding={'dtype': 'int64'}) expected = Variable((), np.int64(0)) actual = conventions.maybe_encode_dtype(original) self.assertDatasetIdentical(expected, actual) def test_decode_cf_with_multiple_missing_values(self): original = Variable(['t'], [0, 1, 2], {'missing_value': np.array([0, 1])}) expected = Variable(['t'], [np.nan, np.nan, 2], {}) with warnings.catch_warnings(record=True) as w: actual = conventions.decode_cf_variable(original) self.assertDatasetIdentical(expected, actual) self.assertIn('variable has multiple fill', str(w[0].message)) def test_decode_cf_with_drop_variables(self): original = Dataset({ 't': ('t', [0, 1, 2], {'units': 'days since 2000-01-01'}), 'x': ("x", [9, 8, 7], {'units': 'km'}), 'foo': (('t', 'x'), [[0, 0, 0], [1, 1, 1], [2, 2, 2]], {'units': 'bar'}), 'y': ('t', [5, 10, -999], {'_FillValue': -999}) }) expected = Dataset({ 't': pd.date_range('2000-01-01', periods=3), 'foo': (('t', 'x'), [[0, 0, 0], [1, 1, 1], [2, 2, 2]], {'units': 'bar'}), 'y': ('t', [5, 10, np.nan]) }) actual = conventions.decode_cf(original, drop_variables=("x",)) actual2 = conventions.decode_cf(original, drop_variables="x") self.assertDatasetIdentical(expected, actual) self.assertDatasetIdentical(expected, actual2) class CFEncodedInMemoryStore(WritableCFDataStore, InMemoryDataStore): pass class NullWrapper(utils.NDArrayMixin): """ Just for testing, this lets us create a numpy array directly but make it look like its not in memory yet. """ def __init__(self, array): self.array = array def __getitem__(self, key): return self.array[indexing.orthogonal_indexer(key, self.shape)] def null_wrap(ds): """ Given a data store this wraps each variable in a NullWrapper so that it appears to be out of memory. """ variables = dict((k, Variable(v.dims, NullWrapper(v.values), v.attrs)) for k, v in iteritems(ds)) return InMemoryDataStore(variables=variables, attributes=ds.attrs) @requires_netCDF4 class TestCFEncodedDataStore(CFEncodedDataTest, TestCase): @contextlib.contextmanager def create_store(self): yield CFEncodedInMemoryStore() @contextlib.contextmanager def roundtrip(self, data, save_kwargs={}, open_kwargs={}, allow_cleanup_failure=False): store = CFEncodedInMemoryStore() data.dump_to_store(store, **save_kwargs) yield open_dataset(store, **open_kwargs) def test_roundtrip_coordinates(self): raise unittest.SkipTest('cannot roundtrip coordinates yet for ' 'CFEncodedInMemoryStore') def test_invalid_dataarray_names_raise(self): pass def test_encoding_kwarg(self): pass �����������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������xarray-0.9.1/xarray/tests/test_dask.py��������������������������������������������������������������0000644�1311115�0011610�00000033615�13043511243�020136� 0����������������������������������������������������������������������������������������������������ustar �shoyer��������������������������eng�����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������from __future__ import absolute_import from __future__ import division from __future__ import print_function import pickle import numpy as np import pandas as pd import xarray as xr from xarray import Variable, DataArray, Dataset import xarray.ufuncs as xu from xarray.core.pycompat import suppress from . import TestCase, requires_dask from xarray.tests import unittest with suppress(ImportError): import dask import dask.array as da class DaskTestCase(TestCase): def assertLazyAnd(self, expected, actual, test): with dask.set_options(get=dask.get): test(actual, expected) if isinstance(actual, Dataset): for k, v in actual.variables.items(): if k in actual.dims: self.assertIsInstance(var.data, np.ndarray) else: self.assertIsInstance(var.data, da.Array) elif isinstance(actual, DataArray): self.assertIsInstance(actual.data, da.Array) for k, v in actual.coords.items(): if k in actual.dims: self.assertIsInstance(v.data, np.ndarray) else: self.assertIsInstance(v.data, da.Array) elif isinstance(actual, Variable): self.assertIsInstance(actual.data, da.Array) else: assert False @requires_dask class TestVariable(DaskTestCase): def assertLazyAndIdentical(self, expected, actual): self.assertLazyAnd(expected, actual, self.assertVariableIdentical) def assertLazyAndAllClose(self, expected, actual): self.assertLazyAnd(expected, actual, self.assertVariableAllClose) def setUp(self): self.values = np.random.RandomState(0).randn(4, 6) self.data = da.from_array(self.values, chunks=(2, 2)) self.eager_var = Variable(('x', 'y'), self.values) self.lazy_var = Variable(('x', 'y'), self.data) def test_basics(self): v = self.lazy_var self.assertIs(self.data, v.data) self.assertEqual(self.data.chunks, v.chunks) self.assertArrayEqual(self.values, v) def test_copy(self): self.assertLazyAndIdentical(self.eager_var, self.lazy_var.copy()) self.assertLazyAndIdentical(self.eager_var, self.lazy_var.copy(deep=True)) def test_chunk(self): for chunks, expected in [(None, ((2, 2), (2, 2, 2))), (3, ((3, 1), (3, 3))), ({'x': 3, 'y': 3}, ((3, 1), (3, 3))), ({'x': 3}, ((3, 1), (2, 2, 2))), ({'x': (3, 1)}, ((3, 1), (2, 2, 2)))]: rechunked = self.lazy_var.chunk(chunks) self.assertEqual(rechunked.chunks, expected) self.assertLazyAndIdentical(self.eager_var, rechunked) def test_indexing(self): u = self.eager_var v = self.lazy_var self.assertLazyAndIdentical(u[0], v[0]) self.assertLazyAndIdentical(u[:1], v[:1]) self.assertLazyAndIdentical(u[[0, 1], [0, 1, 2]], v[[0, 1], [0, 1, 2]]) with self.assertRaisesRegexp(TypeError, 'stored in a dask array'): v[:1] = 0 def test_squeeze(self): u = self.eager_var v = self.lazy_var self.assertLazyAndIdentical(u[0].squeeze(), v[0].squeeze()) def test_equals(self): v = self.lazy_var self.assertTrue(v.equals(v)) self.assertIsInstance(v.data, da.Array) self.assertTrue(v.identical(v)) self.assertIsInstance(v.data, da.Array) def test_transpose(self): u = self.eager_var v = self.lazy_var self.assertLazyAndIdentical(u.T, v.T) def test_shift(self): u = self.eager_var v = self.lazy_var self.assertLazyAndIdentical(u.shift(x=2), v.shift(x=2)) self.assertLazyAndIdentical(u.shift(x=-2), v.shift(x=-2)) self.assertEqual(v.data.chunks, v.shift(x=1).data.chunks) def test_roll(self): u = self.eager_var v = self.lazy_var self.assertLazyAndIdentical(u.roll(x=2), v.roll(x=2)) self.assertEqual(v.data.chunks, v.roll(x=1).data.chunks) def test_unary_op(self): u = self.eager_var v = self.lazy_var self.assertLazyAndIdentical(-u, -v) self.assertLazyAndIdentical(abs(u), abs(v)) self.assertLazyAndIdentical(u.round(), v.round()) def test_binary_op(self): u = self.eager_var v = self.lazy_var self.assertLazyAndIdentical(2 * u, 2 * v) self.assertLazyAndIdentical(u + u, v + v) self.assertLazyAndIdentical(u[0] + u, v[0] + v) def test_reduce(self): u = self.eager_var v = self.lazy_var self.assertLazyAndAllClose(u.mean(), v.mean()) self.assertLazyAndAllClose(u.std(), v.std()) self.assertLazyAndAllClose(u.argmax(dim='x'), v.argmax(dim='x')) self.assertLazyAndAllClose((u > 1).any(), (v > 1).any()) self.assertLazyAndAllClose((u < 1).all('x'), (v < 1).all('x')) with self.assertRaisesRegexp(NotImplementedError, 'dask'): v.median() def test_missing_values(self): values = np.array([0, 1, np.nan, 3]) data = da.from_array(values, chunks=(2,)) eager_var = Variable('x', values) lazy_var = Variable('x', data) self.assertLazyAndIdentical(eager_var, lazy_var.fillna(lazy_var)) self.assertLazyAndIdentical(Variable('x', range(4)), lazy_var.fillna(2)) self.assertLazyAndIdentical(eager_var.count(), lazy_var.count()) def test_concat(self): u = self.eager_var v = self.lazy_var self.assertLazyAndIdentical(u, Variable.concat([v[:2], v[2:]], 'x')) self.assertLazyAndIdentical(u[:2], Variable.concat([v[0], v[1]], 'x')) self.assertLazyAndIdentical( u[:3], Variable.concat([v[[0, 2]], v[[1]]], 'x', positions=[[0, 2], [1]])) def test_missing_methods(self): v = self.lazy_var try: v.argsort() except NotImplementedError as err: self.assertIn('dask', str(err)) try: v[0].item() except NotImplementedError as err: self.assertIn('dask', str(err)) def test_univariate_ufunc(self): u = self.eager_var v = self.lazy_var self.assertLazyAndAllClose(np.sin(u), xu.sin(v)) @unittest.skip('currently broken in dask, see GH1090') def test_bivariate_ufunc(self): u = self.eager_var v = self.lazy_var self.assertLazyAndAllClose(np.maximum(u, 0), xu.maximum(v, 0)) self.assertLazyAndAllClose(np.maximum(u, 0), xu.maximum(0, v)) @requires_dask class TestDataArrayAndDataset(DaskTestCase): def assertLazyAndIdentical(self, expected, actual): self.assertLazyAnd(expected, actual, self.assertDataArrayIdentical) def assertLazyAndAllClose(self, expected, actual): self.assertLazyAnd(expected, actual, self.assertDataArrayAllClose) def assertLazyAndEqual(self, expected, actual): self.assertLazyAnd(expected, actual, self.assertDataArrayEqual) def setUp(self): self.values = np.random.randn(4, 6) self.data = da.from_array(self.values, chunks=(2, 2)) self.eager_array = DataArray(self.values, coords={'x': range(4)}, dims=('x', 'y'), name='foo') self.lazy_array = DataArray(self.data, coords={'x': range(4)}, dims=('x', 'y'), name='foo') def test_rechunk(self): chunked = self.eager_array.chunk({'x': 2}).chunk({'y': 2}) self.assertEqual(chunked.chunks, ((2,) * 2, (2,) * 3)) self.assertLazyAndIdentical(self.lazy_array, chunked) def test_new_chunk(self): chunked = self.eager_array.chunk() self.assertTrue(chunked.data.name.startswith('xarray-')) def test_lazy_dataset(self): lazy_ds = Dataset({'foo': (('x', 'y'), self.data)}) self.assertIsInstance(lazy_ds.foo.variable.data, da.Array) def test_lazy_array(self): u = self.eager_array v = self.lazy_array self.assertLazyAndAllClose(u, v) self.assertLazyAndAllClose(-u, -v) self.assertLazyAndAllClose(u.T, v.T) self.assertLazyAndAllClose(u.mean(), v.mean()) self.assertLazyAndAllClose(1 + u, 1 + v) actual = xr.concat([v[:2], v[2:]], 'x') self.assertLazyAndAllClose(u, actual) def test_groupby(self): u = self.eager_array v = self.lazy_array expected = u.groupby('x').mean() actual = v.groupby('x').mean() self.assertLazyAndAllClose(expected, actual) def test_groupby_first(self): u = self.eager_array v = self.lazy_array for coords in [u.coords, v.coords]: coords['ab'] = ('x', ['a', 'a', 'b', 'b']) with self.assertRaisesRegexp(NotImplementedError, 'dask'): v.groupby('ab').first() expected = u.groupby('ab').first() actual = v.groupby('ab').first(skipna=False) self.assertLazyAndAllClose(expected, actual) def test_reindex(self): u = self.eager_array.assign_coords(y=range(6)) v = self.lazy_array.assign_coords(y=range(6)) for kwargs in [{'x': [2, 3, 4]}, {'x': [1, 100, 2, 101, 3]}, {'x': [2.5, 3, 3.5], 'y': [2, 2.5, 3]}]: expected = u.reindex(**kwargs) actual = v.reindex(**kwargs) self.assertLazyAndAllClose(expected, actual) def test_to_dataset_roundtrip(self): u = self.eager_array v = self.lazy_array expected = u.assign_coords(x=u['x']) self.assertLazyAndEqual(expected, v.to_dataset('x').to_array('x')) def test_merge(self): def duplicate_and_merge(array): return xr.merge([array, array.rename('bar')]).to_array() expected = duplicate_and_merge(self.eager_array) actual = duplicate_and_merge(self.lazy_array) self.assertLazyAndEqual(expected, actual) def test_ufuncs(self): u = self.eager_array v = self.lazy_array self.assertLazyAndAllClose(np.sin(u), xu.sin(v)) def test_where_dispatching(self): a = np.arange(10) b = a > 3 x = da.from_array(a, 5) y = da.from_array(b, 5) expected = DataArray(a).where(b) self.assertLazyAndEqual(expected, DataArray(a).where(y)) self.assertLazyAndEqual(expected, DataArray(x).where(b)) self.assertLazyAndEqual(expected, DataArray(x).where(y)) def test_simultaneous_compute(self): ds = Dataset({'foo': ('x', range(5)), 'bar': ('x', range(5))}).chunk() count = [0] def counting_get(*args, **kwargs): count[0] += 1 return dask.get(*args, **kwargs) with dask.set_options(get=counting_get): ds.load() self.assertEqual(count[0], 1) def test_stack(self): data = da.random.normal(size=(2, 3, 4), chunks=(1, 3, 4)) arr = DataArray(data, dims=('w', 'x', 'y')) stacked = arr.stack(z=('x', 'y')) z = pd.MultiIndex.from_product([np.arange(3), np.arange(4)], names=['x', 'y']) expected = DataArray(data.reshape(2, -1), {'z': z}, dims=['w', 'z']) assert stacked.data.chunks == expected.data.chunks self.assertLazyAndEqual(expected, stacked) def test_dot(self): eager = self.eager_array.dot(self.eager_array[0]) lazy = self.lazy_array.dot(self.lazy_array[0]) self.assertLazyAndAllClose(eager, lazy) def test_variable_pickle(self): # Test that pickling/unpickling does not convert the dask # backend to numpy a1 = Variable(['x'], build_dask_array()) a1.compute() self.assertFalse(a1._in_memory) self.assertEquals(kernel_call_count, 1) a2 = pickle.loads(pickle.dumps(a1)) self.assertEquals(kernel_call_count, 1) self.assertVariableIdentical(a1, a2) self.assertFalse(a1._in_memory) self.assertFalse(a2._in_memory) def test_dataarray_pickle(self): # Test that pickling/unpickling does not convert the dask # backend to numpy a1 = DataArray(build_dask_array()) a1.compute() self.assertFalse(a1._in_memory) self.assertEquals(kernel_call_count, 1) a2 = pickle.loads(pickle.dumps(a1)) self.assertEquals(kernel_call_count, 1) self.assertDataArrayIdentical(a1, a2) self.assertFalse(a1._in_memory) self.assertFalse(a2._in_memory) def test_dataset_pickle(self): ds1 = Dataset({'a': DataArray(build_dask_array())}) ds1.compute() self.assertFalse(ds1['a']._in_memory) self.assertEquals(kernel_call_count, 1) ds2 = pickle.loads(pickle.dumps(ds1)) self.assertEquals(kernel_call_count, 1) self.assertDatasetIdentical(ds1, ds2) self.assertFalse(ds1['a']._in_memory) self.assertFalse(ds2['a']._in_memory) def test_values(self): # Test that invoking the values property does not convert the dask # backend to numpy a = DataArray([1,2]).chunk() self.assertFalse(a._in_memory) self.assertEquals(a.values.tolist(), [1, 2]) self.assertFalse(a._in_memory) def test_from_dask_variable(self): # Test array creation from Variable with dask backend. # This is used e.g. in broadcast() a = DataArray(self.lazy_array.variable, coords={'x': range(4)}, name='foo') self.assertLazyAndIdentical(self.lazy_array, a) kernel_call_count = 0 def kernel(): """Dask kernel to test pickling/unpickling. Must be global to make it pickleable. """ global kernel_call_count kernel_call_count += 1 return np.ones(1) def build_dask_array(): global kernel_call_count kernel_call_count = 0 return dask.array.Array( dask={('foo', 0): (kernel, )}, name='foo', chunks=((1,),), dtype=int) �������������������������������������������������������������������������������������������������������������������xarray-0.9.1/xarray/tests/test_dataarray.py���������������������������������������������������������0000644�1311115�0011610�00000315654�13043675344�021207� 0����������������������������������������������������������������������������������������������������ustar �shoyer��������������������������eng�����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������from __future__ import absolute_import from __future__ import division from __future__ import print_function import numpy as np import pandas as pd import pickle import pytest from copy import deepcopy from textwrap import dedent from distutils.version import LooseVersion import xarray as xr from xarray import (align, broadcast, Dataset, DataArray, IndexVariable, Variable) from xarray.core.pycompat import iteritems, OrderedDict from xarray.core.common import full_like from xarray.tests import ( TestCase, ReturnItem, source_ndarray, unittest, requires_dask, assert_identical, assert_equal, assert_allclose, assert_array_equal) class TestDataArray(TestCase): def setUp(self): self.attrs = {'attr1': 'value1', 'attr2': 2929} self.x = np.random.random((10, 20)) self.v = Variable(['x', 'y'], self.x) self.va = Variable(['x', 'y'], self.x, self.attrs) self.ds = Dataset({'foo': self.v}) self.dv = self.ds['foo'] self.mindex = pd.MultiIndex.from_product([['a', 'b'], [1, 2]], names=('level_1', 'level_2')) self.mda = DataArray([0, 1, 2, 3], coords={'x': self.mindex}, dims='x') def test_repr(self): v = Variable(['time', 'x'], [[1, 2, 3], [4, 5, 6]], {'foo': 'bar'}) coords = OrderedDict([('x', np.arange(3, dtype=np.int64)), ('other', np.int64(0))]) data_array = DataArray(v, coords, name='my_variable') expected = dedent("""\ array([[1, 2, 3], [4, 5, 6]]) Coordinates: * x (x) int64 0 1 2 other int64 0 Dimensions without coordinates: time Attributes: foo: bar""") self.assertEqual(expected, repr(data_array)) def test_repr_multiindex(self): expected = dedent("""\ array([0, 1, 2, 3]) Coordinates: * x (x) MultiIndex - level_1 (x) object 'a' 'a' 'b' 'b' - level_2 (x) int64 1 2 1 2""") self.assertEqual(expected, repr(self.mda)) def test_properties(self): self.assertVariableEqual(self.dv.variable, self.v) self.assertArrayEqual(self.dv.values, self.v.values) for attr in ['dims', 'dtype', 'shape', 'size', 'nbytes', 'ndim', 'attrs']: self.assertEqual(getattr(self.dv, attr), getattr(self.v, attr)) self.assertEqual(len(self.dv), len(self.v)) self.assertVariableEqual(self.dv.variable, self.v) self.assertItemsEqual(list(self.dv.coords), list(self.ds.coords)) for k, v in iteritems(self.dv.coords): self.assertArrayEqual(v, self.ds.coords[k]) with self.assertRaises(AttributeError): self.dv.dataset self.assertIsInstance(self.ds['x'].to_index(), pd.Index) with self.assertRaisesRegexp(ValueError, 'must be 1-dimensional'): self.ds['foo'].to_index() with self.assertRaises(AttributeError): self.dv.variable = self.v def test_data_property(self): array = DataArray(np.zeros((3, 4))) actual = array.copy() actual.values = np.ones((3, 4)) self.assertArrayEqual(np.ones((3, 4)), actual.values) actual.data = 2 * np.ones((3, 4)) self.assertArrayEqual(2 * np.ones((3, 4)), actual.data) self.assertArrayEqual(actual.data, actual.values) def test_indexes(self): array = DataArray(np.zeros((2, 3)), [('x', [0, 1]), ('y', ['a', 'b', 'c'])]) expected = OrderedDict([('x', pd.Index([0, 1])), ('y', pd.Index(['a', 'b', 'c']))]) assert array.indexes.keys() == expected.keys() for k in expected: assert array.indexes[k].equals(expected[k]) def test_get_index(self): array = DataArray(np.zeros((2, 3)), coords={'x': ['a', 'b']}, dims=['x', 'y']) assert array.get_index('x').equals(pd.Index(['a', 'b'])) assert array.get_index('y').equals(pd.Index([0, 1, 2])) with self.assertRaises(KeyError): array.get_index('z') def test_struct_array_dims(self): """ This test checks subraction of two DataArrays for the case when dimension is a structured array. """ # GH837, GH861 # checking array subraction when dims are the same p_data = np.array([('John', 180), ('Stacy', 150), ('Dick', 200)], dtype=[('name', '|S256'), ('height', object)]) p_data_1 = np.array([('John', 180), ('Stacy', 150), ('Dick', 200)], dtype=[('name', '|S256'), ('height', object)]) p_data_2 = np.array([('John', 180), ('Dick', 200)], dtype=[('name', '|S256'), ('height', object)]) weights_0 = DataArray([80, 56, 120], dims=['participant'], coords={'participant': p_data}) weights_1 = DataArray([81, 52, 115], dims=['participant'], coords={'participant': p_data_1}) actual = weights_1 - weights_0 expected = DataArray([1, -4, -5], dims=['participant'], coords={'participant': p_data}) self.assertDataArrayIdentical(actual, expected) # checking array subraction when dims are not the same p_data_1 = np.array([('John', 180), ('Stacy', 151), ('Dick', 200)], dtype=[('name', '|S256'), ('height', object)]) weights_1 = DataArray([81, 52, 115], dims=['participant'], coords={'participant': p_data_1}) actual = weights_1 - weights_0 expected = DataArray([1, -5], dims=['participant'], coords={'participant': p_data_2}) self.assertDataArrayIdentical(actual, expected) # checking array subraction when dims are not the same and one # is np.nan p_data_1 = np.array([('John', 180), ('Stacy', np.nan), ('Dick', 200)], dtype=[('name', '|S256'), ('height', object)]) weights_1 = DataArray([81, 52, 115], dims=['participant'], coords={'participant': p_data_1}) actual = weights_1 - weights_0 expected = DataArray([1, -5], dims=['participant'], coords={'participant': p_data_2}) self.assertDataArrayIdentical(actual, expected) def test_name(self): arr = self.dv self.assertEqual(arr.name, 'foo') copied = arr.copy() arr.name = 'bar' self.assertEqual(arr.name, 'bar') self.assertDataArrayEqual(copied, arr) actual = DataArray(IndexVariable('x', [3])) actual.name = 'y' expected = DataArray([3], [('x', [3])], name='y') self.assertDataArrayIdentical(actual, expected) def test_dims(self): arr = self.dv self.assertEqual(arr.dims, ('x', 'y')) with self.assertRaisesRegexp(AttributeError, 'you cannot assign'): arr.dims = ('w', 'z') def test_sizes(self): array = DataArray(np.zeros((3, 4)), dims=['x', 'y']) self.assertEqual(array.sizes, {'x': 3, 'y': 4}) self.assertEqual(tuple(array.sizes), array.dims) with self.assertRaises(TypeError): array.sizes['foo'] = 5 def test_encoding(self): expected = {'foo': 'bar'} self.dv.encoding['foo'] = 'bar' self.assertEquals(expected, self.dv.encoding) expected = {'baz': 0} self.dv.encoding = expected self.assertEquals(expected, self.dv.encoding) self.assertIsNot(expected, self.dv.encoding) def test_constructor(self): data = np.random.random((2, 3)) actual = DataArray(data) expected = Dataset({None: (['dim_0', 'dim_1'], data)})[None] self.assertDataArrayIdentical(expected, actual) actual = DataArray(data, [['a', 'b'], [-1, -2, -3]]) expected = Dataset({None: (['dim_0', 'dim_1'], data), 'dim_0': ('dim_0', ['a', 'b']), 'dim_1': ('dim_1', [-1, -2, -3])})[None] self.assertDataArrayIdentical(expected, actual) actual = DataArray(data, [pd.Index(['a', 'b'], name='x'), pd.Index([-1, -2, -3], name='y')]) expected = Dataset({None: (['x', 'y'], data), 'x': ('x', ['a', 'b']), 'y': ('y', [-1, -2, -3])})[None] self.assertDataArrayIdentical(expected, actual) coords = [['a', 'b'], [-1, -2, -3]] actual = DataArray(data, coords, ['x', 'y']) self.assertDataArrayIdentical(expected, actual) coords = [pd.Index(['a', 'b'], name='A'), pd.Index([-1, -2, -3], name='B')] actual = DataArray(data, coords, ['x', 'y']) self.assertDataArrayIdentical(expected, actual) coords = {'x': ['a', 'b'], 'y': [-1, -2, -3]} actual = DataArray(data, coords, ['x', 'y']) self.assertDataArrayIdentical(expected, actual) coords = [('x', ['a', 'b']), ('y', [-1, -2, -3])] actual = DataArray(data, coords) self.assertDataArrayIdentical(expected, actual) expected = Dataset({None: (['x', 'y'], data), 'x': ('x', ['a', 'b'])})[None] actual = DataArray(data, {'x': ['a', 'b']}, ['x', 'y']) self.assertDataArrayIdentical(expected, actual) actual = DataArray(data, dims=['x', 'y']) expected = Dataset({None: (['x', 'y'], data)})[None] self.assertDataArrayIdentical(expected, actual) actual = DataArray(data, dims=['x', 'y'], name='foo') expected = Dataset({'foo': (['x', 'y'], data)})['foo'] self.assertDataArrayIdentical(expected, actual) actual = DataArray(data, name='foo') expected = Dataset({'foo': (['dim_0', 'dim_1'], data)})['foo'] self.assertDataArrayIdentical(expected, actual) actual = DataArray(data, dims=['x', 'y'], attrs={'bar': 2}) expected = Dataset({None: (['x', 'y'], data, {'bar': 2})})[None] self.assertDataArrayIdentical(expected, actual) actual = DataArray(data, dims=['x', 'y'], encoding={'bar': 2}) expected = Dataset({None: (['x', 'y'], data, {}, {'bar': 2})})[None] self.assertDataArrayIdentical(expected, actual) def test_constructor_invalid(self): data = np.random.randn(3, 2) with self.assertRaisesRegexp(ValueError, 'coords is not dict-like'): DataArray(data, [[0, 1, 2]], ['x', 'y']) with self.assertRaisesRegexp(ValueError, 'not a subset of the .* dim'): DataArray(data, {'x': [0, 1, 2]}, ['a', 'b']) with self.assertRaisesRegexp(ValueError, 'not a subset of the .* dim'): DataArray(data, {'x': [0, 1, 2]}) with self.assertRaisesRegexp(TypeError, 'is not a string'): DataArray(data, dims=['x', None]) with self.assertRaisesRegexp(ValueError, 'conflicting sizes for dim'): DataArray([1, 2, 3], coords=[('x', [0, 1])]) with self.assertRaisesRegexp(ValueError, 'conflicting sizes for dim'): DataArray([1, 2], coords={'x': [0, 1], 'y': ('x', [1])}, dims='x') with self.assertRaisesRegexp(ValueError, 'conflicting MultiIndex'): DataArray(np.random.rand(4, 4), [('x', self.mindex), ('y', self.mindex)]) with self.assertRaisesRegexp(ValueError, 'conflicting MultiIndex'): DataArray(np.random.rand(4, 4), [('x', self.mindex), ('level_1', range(4))]) def test_constructor_from_self_described(self): data = [[-0.1, 21], [0, 2]] expected = DataArray(data, coords={'x': ['a', 'b'], 'y': [-1, -2]}, dims=['x', 'y'], name='foobar', attrs={'bar': 2}, encoding={'foo': 3}) actual = DataArray(expected) self.assertDataArrayIdentical(expected, actual) actual = DataArray(expected.values, actual.coords) self.assertDataArrayEqual(expected, actual) frame = pd.DataFrame(data, index=pd.Index(['a', 'b'], name='x'), columns=pd.Index([-1, -2], name='y')) actual = DataArray(frame) self.assertDataArrayEqual(expected, actual) series = pd.Series(data[0], index=pd.Index([-1, -2], name='y')) actual = DataArray(series) self.assertDataArrayEqual(expected[0].reset_coords('x', drop=True), actual) panel = pd.Panel({0: frame}) actual = DataArray(panel) expected = DataArray([data], expected.coords, ['dim_0', 'x', 'y']) expected['dim_0'] = [0] self.assertDataArrayIdentical(expected, actual) expected = DataArray(data, coords={'x': ['a', 'b'], 'y': [-1, -2], 'a': 0, 'z': ('x', [-0.5, 0.5])}, dims=['x', 'y']) actual = DataArray(expected) self.assertDataArrayIdentical(expected, actual) actual = DataArray(expected.values, expected.coords) self.assertDataArrayIdentical(expected, actual) expected = Dataset({'foo': ('foo', ['a', 'b'])})['foo'] actual = DataArray(pd.Index(['a', 'b'], name='foo')) self.assertDataArrayIdentical(expected, actual) actual = DataArray(IndexVariable('foo', ['a', 'b'])) self.assertDataArrayIdentical(expected, actual) def test_constructor_from_0d(self): expected = Dataset({None: ([], 0)})[None] actual = DataArray(0) self.assertDataArrayIdentical(expected, actual) def test_equals_and_identical(self): orig = DataArray(np.arange(5.0), {'a': 42}, dims='x') expected = orig actual = orig.copy() self.assertTrue(expected.equals(actual)) self.assertTrue(expected.identical(actual)) actual = expected.rename('baz') self.assertTrue(expected.equals(actual)) self.assertFalse(expected.identical(actual)) actual = expected.rename({'x': 'xxx'}) self.assertFalse(expected.equals(actual)) self.assertFalse(expected.identical(actual)) actual = expected.copy() actual.attrs['foo'] = 'bar' self.assertTrue(expected.equals(actual)) self.assertFalse(expected.identical(actual)) actual = expected.copy() actual['x'] = ('x', -np.arange(5)) self.assertFalse(expected.equals(actual)) self.assertFalse(expected.identical(actual)) actual = expected.reset_coords(drop=True) self.assertFalse(expected.equals(actual)) self.assertFalse(expected.identical(actual)) actual = orig.copy() actual[0] = np.nan expected = actual.copy() self.assertTrue(expected.equals(actual)) self.assertTrue(expected.identical(actual)) actual[:] = np.nan self.assertFalse(expected.equals(actual)) self.assertFalse(expected.identical(actual)) actual = expected.copy() actual['a'] = 100000 self.assertFalse(expected.equals(actual)) self.assertFalse(expected.identical(actual)) def test_equals_failures(self): orig = DataArray(np.arange(5.0), {'a': 42}, dims='x') self.assertFalse(orig.equals(np.arange(5))) self.assertFalse(orig.identical(123)) self.assertFalse(orig.broadcast_equals({1: 2})) def test_broadcast_equals(self): a = DataArray([0, 0], {'y': 0}, dims='x') b = DataArray([0, 0], {'y': ('x', [0, 0])}, dims='x') self.assertTrue(a.broadcast_equals(b)) self.assertTrue(b.broadcast_equals(a)) self.assertFalse(a.equals(b)) self.assertFalse(a.identical(b)) c = DataArray([0], coords={'x': 0}, dims='y') self.assertFalse(a.broadcast_equals(c)) self.assertFalse(c.broadcast_equals(a)) def test_getitem(self): # strings pull out dataarrays self.assertDataArrayIdentical(self.dv, self.ds['foo']) x = self.dv['x'] y = self.dv['y'] self.assertDataArrayIdentical(self.ds['x'], x) self.assertDataArrayIdentical(self.ds['y'], y) I = ReturnItem() for i in [I[:], I[...], I[x.values], I[x.variable], I[x], I[x, y], I[x.values > -1], I[x.variable > -1], I[x > -1], I[x > -1, y > -1]]: self.assertVariableEqual(self.dv, self.dv[i]) for i in [I[0], I[:, 0], I[:3, :2], I[x.values[:3]], I[x.variable[:3]], I[x[:3]], I[x[:3], y[:4]], I[x.values > 3], I[x.variable > 3], I[x > 3], I[x > 3, y > 3]]: assert_array_equal(self.v[i], self.dv[i]) def test_getitem_dict(self): actual = self.dv[{'x': slice(3), 'y': 0}] expected = self.dv.isel(x=slice(3), y=0) self.assertDataArrayIdentical(expected, actual) def test_getitem_coords(self): orig = DataArray([[10], [20]], {'x': [1, 2], 'y': [3], 'z': 4, 'x2': ('x', ['a', 'b']), 'y2': ('y', ['c']), 'xy': (['y', 'x'], [['d', 'e']])}, dims=['x', 'y']) self.assertDataArrayIdentical(orig, orig[:]) self.assertDataArrayIdentical(orig, orig[:, :]) self.assertDataArrayIdentical(orig, orig[...]) self.assertDataArrayIdentical(orig, orig[:2, :1]) self.assertDataArrayIdentical(orig, orig[[0, 1], [0]]) actual = orig[0, 0] expected = DataArray( 10, {'x': 1, 'y': 3, 'z': 4, 'x2': 'a', 'y2': 'c', 'xy': 'd'}) self.assertDataArrayIdentical(expected, actual) actual = orig[0, :] expected = DataArray( [10], {'x': 1, 'y': [3], 'z': 4, 'x2': 'a', 'y2': ('y', ['c']), 'xy': ('y', ['d'])}, dims='y') self.assertDataArrayIdentical(expected, actual) actual = orig[:, 0] expected = DataArray( [10, 20], {'x': [1, 2], 'y': 3, 'z': 4, 'x2': ('x', ['a', 'b']), 'y2': 'c', 'xy': ('x', ['d', 'e'])}, dims='x') self.assertDataArrayIdentical(expected, actual) def test_attr_sources_multiindex(self): # make sure attr-style access for multi-index levels # returns DataArray objects self.assertIsInstance(self.mda.level_1, DataArray) def test_pickle(self): data = DataArray(np.random.random((3, 3)), dims=('id', 'time')) roundtripped = pickle.loads(pickle.dumps(data)) self.assertDataArrayIdentical(data, roundtripped) @requires_dask def test_chunk(self): unblocked = DataArray(np.ones((3, 4))) self.assertIsNone(unblocked.chunks) blocked = unblocked.chunk() self.assertEqual(blocked.chunks, ((3,), (4,))) blocked = unblocked.chunk(chunks=((2, 1), (2, 2))) self.assertEqual(blocked.chunks, ((2, 1), (2, 2))) blocked = unblocked.chunk(chunks=(3, 3)) self.assertEqual(blocked.chunks, ((3,), (3, 1))) self.assertIsNone(blocked.load().chunks) def test_isel(self): self.assertDataArrayIdentical(self.dv[0], self.dv.isel(x=0)) self.assertDataArrayIdentical(self.dv, self.dv.isel(x=slice(None))) self.assertDataArrayIdentical(self.dv[:3], self.dv.isel(x=slice(3))) self.assertDataArrayIdentical(self.dv[:3, :5], self.dv.isel(x=slice(3), y=slice(5))) def test_sel(self): self.ds['x'] = ('x', np.array(list('abcdefghij'))) da = self.ds['foo'] self.assertDataArrayIdentical(da, da.sel(x=slice(None))) self.assertDataArrayIdentical(da[1], da.sel(x='b')) self.assertDataArrayIdentical(da[:3], da.sel(x=slice('c'))) self.assertDataArrayIdentical(da[:3], da.sel(x=['a', 'b', 'c'])) self.assertDataArrayIdentical(da[:, :4], da.sel(y=(self.ds['y'] < 4))) # verify that indexing with a dataarray works b = DataArray('b') self.assertDataArrayIdentical(da[1], da.sel(x=b)) self.assertDataArrayIdentical(da[[1]], da.sel(x=slice(b, b))) def test_sel_no_index(self): array = DataArray(np.arange(10), dims='x') self.assertDataArrayIdentical(array[0], array.sel(x=0)) self.assertDataArrayIdentical(array[:5], array.sel(x=slice(5))) self.assertDataArrayIdentical(array[[0, -1]], array.sel(x=[0, -1])) self.assertDataArrayIdentical( array[array < 5], array.sel(x=(array < 5))) def test_sel_method(self): data = DataArray(np.random.randn(3, 4), [('x', [0, 1, 2]), ('y', list('abcd'))]) expected = data.sel(y=['a', 'b']) actual = data.sel(y=['ab', 'ba'], method='pad') self.assertDataArrayIdentical(expected, actual) if pd.__version__ >= '0.17': expected = data.sel(x=[1, 2]) actual = data.sel(x=[0.9, 1.9], method='backfill', tolerance=1) self.assertDataArrayIdentical(expected, actual) else: with self.assertRaisesRegexp(TypeError, 'tolerance'): data.sel(x=[0.9, 1.9], method='backfill', tolerance=1) def test_sel_drop(self): data = DataArray([1, 2, 3], [('x', [0, 1, 2])]) expected = DataArray(1) selected = data.sel(x=0, drop=True) self.assertDataArrayIdentical(expected, selected) expected = DataArray(1, {'x': 0}) selected = data.sel(x=0, drop=False) self.assertDataArrayIdentical(expected, selected) data = DataArray([1, 2, 3], dims=['x']) expected = DataArray(1) selected = data.sel(x=0, drop=True) self.assertDataArrayIdentical(expected, selected) def test_isel_drop(self): data = DataArray([1, 2, 3], [('x', [0, 1, 2])]) expected = DataArray(1) selected = data.isel(x=0, drop=True) self.assertDataArrayIdentical(expected, selected) expected = DataArray(1, {'x': 0}) selected = data.isel(x=0, drop=False) self.assertDataArrayIdentical(expected, selected) def test_isel_points(self): shape = (10, 5, 6) np_array = np.random.random(shape) da = DataArray(np_array, dims=['time', 'y', 'x'], coords={'time': np.arange(0, 100, 10)}) y = [1, 3] x = [3, 0] expected = da.values[:, y, x] actual = da.isel_points(y=y, x=x, dim='test_coord') assert actual.coords['test_coord'].shape == (len(y), ) assert list(actual.coords) == ['time'] assert actual.dims == ('test_coord', 'time') actual = da.isel_points(y=y, x=x) assert 'points' in actual.dims # Note that because xarray always concatenates along the first # dimension, We must transpose the result to match the numpy style of # concatenation. np.testing.assert_equal(actual.T, expected) # a few corner cases da.isel_points(time=[1, 2], x=[2, 2], y=[3, 4]) np.testing.assert_allclose( da.isel_points(time=[1], x=[2], y=[4]).values.squeeze(), np_array[1, 4, 2].squeeze()) da.isel_points(time=[1, 2]) y = [-1, 0] x = [-2, 2] expected = da.values[:, y, x] actual = da.isel_points(x=x, y=y).values np.testing.assert_equal(actual.T, expected) # test that the order of the indexers doesn't matter self.assertDataArrayIdentical( da.isel_points(y=y, x=x), da.isel_points(x=x, y=y)) # make sure we're raising errors in the right places with self.assertRaisesRegexp(ValueError, 'All indexers must be the same length'): da.isel_points(y=[1, 2], x=[1, 2, 3]) with self.assertRaisesRegexp(ValueError, 'dimension bad_key does not exist'): da.isel_points(bad_key=[1, 2]) with self.assertRaisesRegexp(TypeError, 'Indexers must be integers'): da.isel_points(y=[1.5, 2.2]) with self.assertRaisesRegexp(TypeError, 'Indexers must be integers'): da.isel_points(x=[1, 2, 3], y=slice(3)) with self.assertRaisesRegexp(ValueError, 'Indexers must be 1 dimensional'): da.isel_points(y=1, x=2) with self.assertRaisesRegexp(ValueError, 'Existing dimension names are not'): da.isel_points(y=[1, 2], x=[1, 2], dim='x') # using non string dims actual = da.isel_points(y=[1, 2], x=[1, 2], dim=['A', 'B']) assert 'points' in actual.coords def test_loc(self): self.ds['x'] = ('x', np.array(list('abcdefghij'))) da = self.ds['foo'] self.assertDataArrayIdentical(da[:3], da.loc[:'c']) self.assertDataArrayIdentical(da[1], da.loc['b']) self.assertDataArrayIdentical(da[1], da.loc[{'x': 'b'}]) self.assertDataArrayIdentical(da[1], da.loc['b', ...]) self.assertDataArrayIdentical(da[:3], da.loc[['a', 'b', 'c']]) self.assertDataArrayIdentical(da[:3, :4], da.loc[['a', 'b', 'c'], np.arange(4)]) self.assertDataArrayIdentical(da[:, :4], da.loc[:, self.ds['y'] < 4]) da.loc['a':'j'] = 0 self.assertTrue(np.all(da.values == 0)) da.loc[{'x': slice('a', 'j')}] = 2 self.assertTrue(np.all(da.values == 2)) def test_loc_single_boolean(self): data = DataArray([0, 1], coords=[[True, False]]) self.assertEqual(data.loc[True], 0) self.assertEqual(data.loc[False], 1) def test_selection_multiindex(self): mindex = pd.MultiIndex.from_product([['a', 'b'], [1, 2], [-1, -2]], names=('one', 'two', 'three')) mdata = DataArray(range(8), [('x', mindex)]) def test_sel(lab_indexer, pos_indexer, replaced_idx=False, renamed_dim=None): da = mdata.sel(x=lab_indexer) expected_da = mdata.isel(x=pos_indexer) if not replaced_idx: self.assertDataArrayIdentical(da, expected_da) else: if renamed_dim: self.assertEqual(da.dims[0], renamed_dim) da = da.rename({renamed_dim: 'x'}) self.assertVariableIdentical(da.variable, expected_da.variable) self.assertVariableNotEqual(da['x'], expected_da['x']) test_sel(('a', 1, -1), 0) test_sel(('b', 2, -2), -1) test_sel(('a', 1), [0, 1], replaced_idx=True, renamed_dim='three') test_sel(('a',), range(4), replaced_idx=True) test_sel('a', range(4), replaced_idx=True) test_sel([('a', 1, -1), ('b', 2, -2)], [0, 7]) test_sel(slice('a', 'b'), range(8)) test_sel(slice(('a', 1), ('b', 1)), range(6)) test_sel({'one': 'a', 'two': 1, 'three': -1}, 0) test_sel({'one': 'a', 'two': 1}, [0, 1], replaced_idx=True, renamed_dim='three') test_sel({'one': 'a'}, range(4), replaced_idx=True) self.assertDataArrayIdentical(mdata.loc['a'], mdata.sel(x='a')) self.assertDataArrayIdentical(mdata.loc[('a', 1), ...], mdata.sel(x=('a', 1))) self.assertDataArrayIdentical(mdata.loc[{'one': 'a'}, ...], mdata.sel(x={'one': 'a'})) with self.assertRaises(IndexError): mdata.loc[('a', 1)] self.assertDataArrayIdentical(mdata.sel(x={'one': 'a', 'two': 1}), mdata.sel(one='a', two=1)) def test_virtual_default_coords(self): array = DataArray(np.zeros((5,)), dims='x') expected = DataArray(range(5), dims='x', name='x') self.assertDataArrayIdentical(expected, array['x']) self.assertDataArrayIdentical(expected, array.coords['x']) def test_virtual_time_components(self): dates = pd.date_range('2000-01-01', periods=10) da = DataArray(np.arange(1, 11), [('time', dates)]) self.assertArrayEqual(da['time.dayofyear'], da.values) self.assertArrayEqual(da.coords['time.dayofyear'], da.values) def test_coords(self): # use int64 to ensure repr() consistency on windows coords = [IndexVariable('x', np.array([-1, -2], 'int64')), IndexVariable('y', np.array([0, 1, 2], 'int64'))] da = DataArray(np.random.randn(2, 3), coords, name='foo') self.assertEquals(2, len(da.coords)) self.assertEqual(['x', 'y'], list(da.coords)) self.assertTrue(coords[0].identical(da.coords['x'])) self.assertTrue(coords[1].identical(da.coords['y'])) self.assertIn('x', da.coords) self.assertNotIn(0, da.coords) self.assertNotIn('foo', da.coords) with self.assertRaises(KeyError): da.coords[0] with self.assertRaises(KeyError): da.coords['foo'] expected = dedent("""\ Coordinates: * x (x) int64 -1 -2 * y (y) int64 0 1 2""") actual = repr(da.coords) self.assertEquals(expected, actual) del da.coords['x'] expected = DataArray(da.values, {'y': [0, 1, 2]}, dims=['x', 'y'], name='foo') self.assertDataArrayIdentical(da, expected) with self.assertRaisesRegexp(ValueError, 'conflicting MultiIndex'): self.mda['level_1'] = np.arange(4) self.mda.coords['level_1'] = np.arange(4) def test_coord_coords(self): orig = DataArray([10, 20], {'x': [1, 2], 'x2': ('x', ['a', 'b']), 'z': 4}, dims='x') actual = orig.coords['x'] expected = DataArray([1, 2], {'z': 4, 'x2': ('x', ['a', 'b']), 'x': [1, 2]}, dims='x', name='x') self.assertDataArrayIdentical(expected, actual) del actual.coords['x2'] self.assertDataArrayIdentical( expected.reset_coords('x2', drop=True), actual) actual.coords['x3'] = ('x', ['a', 'b']) expected = DataArray([1, 2], {'z': 4, 'x3': ('x', ['a', 'b']), 'x': [1, 2]}, dims='x', name='x') self.assertDataArrayIdentical(expected, actual) def test_reset_coords(self): data = DataArray(np.zeros((3, 4)), {'bar': ('x', ['a', 'b', 'c']), 'baz': ('y', range(4)), 'y': range(4)}, dims=['x', 'y'], name='foo') actual = data.reset_coords() expected = Dataset({'foo': (['x', 'y'], np.zeros((3, 4))), 'bar': ('x', ['a', 'b', 'c']), 'baz': ('y', range(4)), 'y': range(4)}) self.assertDatasetIdentical(actual, expected) actual = data.reset_coords(['bar', 'baz']) self.assertDatasetIdentical(actual, expected) actual = data.reset_coords('bar') expected = Dataset({'foo': (['x', 'y'], np.zeros((3, 4))), 'bar': ('x', ['a', 'b', 'c'])}, {'baz': ('y', range(4)), 'y': range(4)}) self.assertDatasetIdentical(actual, expected) actual = data.reset_coords(['bar']) self.assertDatasetIdentical(actual, expected) actual = data.reset_coords(drop=True) expected = DataArray(np.zeros((3, 4)), coords={'y': range(4)}, dims=['x', 'y'], name='foo') self.assertDataArrayIdentical(actual, expected) actual = data.copy() actual.reset_coords(drop=True, inplace=True) self.assertDataArrayIdentical(actual, expected) actual = data.reset_coords('bar', drop=True) expected = DataArray(np.zeros((3, 4)), {'baz': ('y', range(4)), 'y': range(4)}, dims=['x', 'y'], name='foo') self.assertDataArrayIdentical(actual, expected) with self.assertRaisesRegexp(ValueError, 'cannot reset coord'): data.reset_coords(inplace=True) with self.assertRaisesRegexp(ValueError, 'cannot be found'): data.reset_coords('foo', drop=True) with self.assertRaisesRegexp(ValueError, 'cannot be found'): data.reset_coords('not_found') with self.assertRaisesRegexp(ValueError, 'cannot remove index'): data.reset_coords('y') def test_assign_coords(self): array = DataArray(10) actual = array.assign_coords(c=42) expected = DataArray(10, {'c': 42}) self.assertDataArrayIdentical(actual, expected) array = DataArray([1, 2, 3, 4], {'c': ('x', [0, 0, 1, 1])}, dims='x') actual = array.groupby('c').assign_coords(d=lambda a: a.mean()) expected = array.copy() expected.coords['d'] = ('x', [1.5, 1.5, 3.5, 3.5]) self.assertDataArrayIdentical(actual, expected) with self.assertRaisesRegexp(ValueError, 'conflicting MultiIndex'): self.mda.assign_coords(level_1=range(4)) def test_coords_alignment(self): lhs = DataArray([1, 2, 3], [('x', [0, 1, 2])]) rhs = DataArray([2, 3, 4], [('x', [1, 2, 3])]) lhs.coords['rhs'] = rhs expected = DataArray([1, 2, 3], coords={'rhs': ('x', [np.nan, 2, 3]), 'x': [0, 1, 2]}, dims='x') self.assertDataArrayIdentical(lhs, expected) def test_coords_replacement_alignment(self): # regression test for GH725 arr = DataArray([0, 1, 2], dims=['abc']) new_coord = DataArray([1, 2, 3], dims=['abc'], coords=[[1, 2, 3]]) arr['abc'] = new_coord expected = DataArray([0, 1, 2], coords=[('abc', [1, 2, 3])]) self.assertDataArrayIdentical(arr, expected) def test_coords_non_string(self): arr = DataArray(0, coords={1: 2}) actual = arr.coords[1] expected = DataArray(2, coords={1: 2}, name=1) self.assertDataArrayIdentical(actual, expected) def test_reindex_like(self): foo = DataArray(np.random.randn(5, 6), [('x', range(5)), ('y', range(6))]) bar = foo[:2, :2] self.assertDataArrayIdentical(foo.reindex_like(bar), bar) expected = foo.copy() expected[:] = np.nan expected[:2, :2] = bar self.assertDataArrayIdentical(bar.reindex_like(foo), expected) def test_reindex_like_no_index(self): foo = DataArray(np.random.randn(5, 6), dims=['x', 'y']) self.assertDatasetIdentical(foo, foo.reindex_like(foo)) bar = foo[:4] with self.assertRaisesRegexp( ValueError, 'different size for unlabeled'): foo.reindex_like(bar) def test_reindex_regressions(self): # regression test for #279 expected = DataArray(np.random.randn(5), coords=[("time", range(5))]) time2 = DataArray(np.arange(5), dims="time2") actual = expected.reindex(time=time2) self.assertDataArrayIdentical(actual, expected) # regression test for #736, reindex can not change complex nums dtype x = np.array([1, 2, 3], dtype=np.complex) x = DataArray(x, coords=[[0.1, 0.2, 0.3]]) y = DataArray([2, 5, 6, 7, 8], coords=[[-1.1, 0.21, 0.31, 0.41, 0.51]]) re_dtype = x.reindex_like(y, method='pad').dtype self.assertEqual(x.dtype, re_dtype) def test_reindex_method(self): x = DataArray([10, 20], dims='y', coords={'y': [0, 1]}) y = [-0.1, 0.5, 1.1] if pd.__version__ >= '0.17': actual = x.reindex(y=y, method='backfill', tolerance=0.2) expected = DataArray([10, np.nan, np.nan], coords=[('y', y)]) self.assertDataArrayIdentical(expected, actual) alt = Dataset({'y': y}) actual = x.reindex_like(alt, method='backfill') expected = DataArray([10, 20, np.nan], coords=[('y', y)]) self.assertDatasetIdentical(expected, actual) def test_rename(self): renamed = self.dv.rename('bar') self.assertDatasetIdentical( renamed.to_dataset(), self.ds.rename({'foo': 'bar'})) self.assertEqual(renamed.name, 'bar') renamed = self.dv.x.rename({'x': 'z'}).rename('z') self.assertDatasetIdentical( renamed, self.ds.rename({'x': 'z'}).z) self.assertEqual(renamed.name, 'z') self.assertEqual(renamed.dims, ('z',)) def test_swap_dims(self): array = DataArray(np.random.randn(3), {'y': ('x', list('abc'))}, 'x') expected = DataArray(array.values, {'y': list('abc')}, dims='y') actual = array.swap_dims({'x': 'y'}) self.assertDataArrayIdentical(expected, actual) def test_set_index(self): indexes = [self.mindex.get_level_values(n) for n in self.mindex.names] coords = {idx.name: ('x', idx) for idx in indexes} array = DataArray(self.mda.values, coords=coords, dims='x') expected = self.mda.copy() level_3 = ('x', [1, 2, 3, 4]) array['level_3'] = level_3 expected['level_3'] = level_3 obj = array.set_index(x=self.mindex.names) self.assertDataArrayIdentical(obj, expected) obj = obj.set_index(x='level_3', append=True) expected = array.set_index(x=['level_1', 'level_2', 'level_3']) self.assertDataArrayIdentical(obj, expected) array.set_index(x=['level_1', 'level_2', 'level_3'], inplace=True) self.assertDataArrayIdentical(array, expected) array2d = DataArray(np.random.rand(2, 2), coords={'x': ('x', [0, 1]), 'level': ('y', [1, 2])}, dims=('x', 'y')) with self.assertRaisesRegexp(ValueError, 'dimension mismatch'): array2d.set_index(x='level') def test_reset_index(self): indexes = [self.mindex.get_level_values(n) for n in self.mindex.names] coords = {idx.name: ('x', idx) for idx in indexes} expected = DataArray(self.mda.values, coords=coords, dims='x') obj = self.mda.reset_index('x') self.assertDataArrayIdentical(obj, expected) obj = self.mda.reset_index(self.mindex.names) self.assertDataArrayIdentical(obj, expected) obj = self.mda.reset_index(['x', 'level_1']) self.assertDataArrayIdentical(obj, expected) coords = {'x': ('x', self.mindex.droplevel('level_1')), 'level_1': ('x', self.mindex.get_level_values('level_1'))} expected = DataArray(self.mda.values, coords=coords, dims='x') obj = self.mda.reset_index(['level_1']) self.assertDataArrayIdentical(obj, expected) expected = DataArray(self.mda.values, dims='x') obj = self.mda.reset_index('x', drop=True) self.assertDataArrayIdentical(obj, expected) array = self.mda.copy() array.reset_index(['x'], drop=True, inplace=True) self.assertDataArrayIdentical(array, expected) # single index array = DataArray([1, 2], coords={'x': ['a', 'b']}, dims='x') expected = DataArray([1, 2], coords={'x_': ('x', ['a', 'b'])}, dims='x') self.assertDataArrayIdentical(array.reset_index('x'), expected) def test_reorder_levels(self): midx = self.mindex.reorder_levels(['level_2', 'level_1']) expected = DataArray(self.mda.values, coords={'x': midx}, dims='x') obj = self.mda.reorder_levels(x=['level_2', 'level_1']) self.assertDataArrayIdentical(obj, expected) array = self.mda.copy() array.reorder_levels(x=['level_2', 'level_1'], inplace=True) self.assertDataArrayIdentical(array, expected) array = DataArray([1, 2], dims='x') with self.assertRaises(KeyError): array.reorder_levels(x=['level_1', 'level_2']) array['x'] = [0, 1] with self.assertRaisesRegexp(ValueError, 'has no MultiIndex'): array.reorder_levels(x=['level_1', 'level_2']) def test_dataset_getitem(self): dv = self.ds['foo'] self.assertDataArrayIdentical(dv, self.dv) def test_array_interface(self): self.assertArrayEqual(np.asarray(self.dv), self.x) # test patched in methods self.assertArrayEqual(self.dv.astype(float), self.v.astype(float)) assert_array_equal(self.dv.argsort(), self.v.argsort()) assert_array_equal(self.dv.clip(2, 3), self.v.clip(2, 3)) # test ufuncs expected = deepcopy(self.ds) expected['foo'][:] = np.sin(self.x) self.assertDataArrayEqual(expected['foo'], np.sin(self.dv)) assert_array_equal(self.dv, np.maximum(self.v, self.dv)) bar = Variable(['x', 'y'], np.zeros((10, 20))) self.assertDataArrayEqual(self.dv, np.maximum(self.dv, bar)) def test_is_null(self): x = np.random.RandomState(42).randn(5, 6) x[x < 0] = np.nan original = DataArray(x, [-np.arange(5), np.arange(6)], ['x', 'y']) expected = DataArray(pd.isnull(x), [-np.arange(5), np.arange(6)], ['x', 'y']) self.assertDataArrayIdentical(expected, original.isnull()) self.assertDataArrayIdentical(~expected, original.notnull()) def test_math(self): x = self.x v = self.v a = self.dv # variable math was already tested extensively, so let's just make sure # that all types are properly converted here self.assertDataArrayEqual(a, +a) self.assertDataArrayEqual(a, a + 0) self.assertDataArrayEqual(a, 0 + a) self.assertDataArrayEqual(a, a + 0 * v) self.assertDataArrayEqual(a, 0 * v + a) self.assertDataArrayEqual(a, a + 0 * x) self.assertDataArrayEqual(a, 0 * x + a) self.assertDataArrayEqual(a, a + 0 * a) self.assertDataArrayEqual(a, 0 * a + a) def test_math_automatic_alignment(self): a = DataArray(range(5), [('x', range(5))]) b = DataArray(range(5), [('x', range(1, 6))]) expected = DataArray(np.ones(4), [('x', [1, 2, 3, 4])]) self.assertDataArrayIdentical(a - b, expected) def test_non_overlapping_dataarrays_return_empty_result(self): a = DataArray(range(5), [('x', range(5))]) result = a.isel(x=slice(2)) + a.isel(x=slice(2, None)) self.assertEqual(len(result['x']), 0) def test_empty_dataarrays_return_empty_result(self): a = DataArray(data=[]) result = a * a self.assertEqual(len(result['dim_0']), 0) def test_inplace_math_basics(self): x = self.x a = self.dv v = a.variable b = a b += 1 self.assertIs(b, a) self.assertIs(b.variable, v) self.assertArrayEqual(b.values, x) self.assertIs(source_ndarray(b.values), x) def test_inplace_math_automatic_alignment(self): a = DataArray(range(5), [('x', range(5))]) b = DataArray(range(1, 6), [('x', range(1, 6))]) with self.assertRaises(xr.MergeError): a += b with self.assertRaises(xr.MergeError): b += a def test_math_name(self): # Verify that name is preserved only when it can be done unambiguously. # The rule (copied from pandas.Series) is keep the current name only if # the other object has the same name or no name attribute and this # object isn't a coordinate; otherwise reset to None. a = self.dv self.assertEqual((+a).name, 'foo') self.assertEqual((a + 0).name, 'foo') self.assertIs((a + a.rename(None)).name, None) self.assertIs((a + a.rename('bar')).name, None) self.assertEqual((a + a).name, 'foo') self.assertIs((+a['x']).name, 'x') self.assertIs((a['x'] + 0).name, 'x') self.assertIs((a + a['x']).name, None) def test_math_with_coords(self): coords = {'x': [-1, -2], 'y': ['ab', 'cd', 'ef'], 'lat': (['x', 'y'], [[1, 2, 3], [-1, -2, -3]]), 'c': -999} orig = DataArray(np.random.randn(2, 3), coords, dims=['x', 'y']) actual = orig + 1 expected = DataArray(orig.values + 1, orig.coords) self.assertDataArrayIdentical(expected, actual) actual = 1 + orig self.assertDataArrayIdentical(expected, actual) actual = orig + orig[0, 0] exp_coords = dict((k, v) for k, v in coords.items() if k != 'lat') expected = DataArray(orig.values + orig.values[0, 0], exp_coords, dims=['x', 'y']) self.assertDataArrayIdentical(expected, actual) actual = orig[0, 0] + orig self.assertDataArrayIdentical(expected, actual) actual = orig[0, 0] + orig[-1, -1] expected = DataArray(orig.values[0, 0] + orig.values[-1, -1], {'c': -999}) self.assertDataArrayIdentical(expected, actual) actual = orig[:, 0] + orig[0, :] exp_values = orig[:, 0].values[:, None] + orig[0, :].values[None, :] expected = DataArray(exp_values, exp_coords, dims=['x', 'y']) self.assertDataArrayIdentical(expected, actual) actual = orig[0, :] + orig[:, 0] self.assertDataArrayIdentical(expected.T, actual) actual = orig - orig.T expected = DataArray(np.zeros((2, 3)), orig.coords) self.assertDataArrayIdentical(expected, actual) actual = orig.T - orig self.assertDataArrayIdentical(expected.T, actual) alt = DataArray([1, 1], {'x': [-1, -2], 'c': 'foo', 'd': 555}, 'x') actual = orig + alt expected = orig + 1 expected.coords['d'] = 555 del expected.coords['c'] self.assertDataArrayIdentical(expected, actual) actual = alt + orig self.assertDataArrayIdentical(expected, actual) def test_index_math(self): orig = DataArray(range(3), dims='x', name='x') actual = orig + 1 expected = DataArray(1 + np.arange(3), dims='x', name='x') self.assertDataArrayIdentical(expected, actual) # regression tests for #254 actual = orig[0] < orig expected = DataArray([False, True, True], dims='x', name='x') self.assertDataArrayIdentical(expected, actual) actual = orig > orig[0] self.assertDataArrayIdentical(expected, actual) def test_dataset_math(self): # more comprehensive tests with multiple dataset variables obs = Dataset({'tmin': ('x', np.arange(5)), 'tmax': ('x', 10 + np.arange(5))}, {'x': ('x', 0.5 * np.arange(5)), 'loc': ('x', range(-2, 3))}) actual = 2 * obs['tmax'] expected = DataArray(2 * (10 + np.arange(5)), obs.coords, name='tmax') self.assertDataArrayIdentical(actual, expected) actual = obs['tmax'] - obs['tmin'] expected = DataArray(10 * np.ones(5), obs.coords) self.assertDataArrayIdentical(actual, expected) sim = Dataset({'tmin': ('x', 1 + np.arange(5)), 'tmax': ('x', 11 + np.arange(5)), # does *not* include 'loc' as a coordinate 'x': ('x', 0.5 * np.arange(5))}) actual = sim['tmin'] - obs['tmin'] expected = DataArray(np.ones(5), obs.coords, name='tmin') self.assertDataArrayIdentical(actual, expected) actual = -obs['tmin'] + sim['tmin'] self.assertDataArrayIdentical(actual, expected) actual = sim['tmin'].copy() actual -= obs['tmin'] self.assertDataArrayIdentical(actual, expected) actual = sim.copy() actual['tmin'] = sim['tmin'] - obs['tmin'] expected = Dataset({'tmin': ('x', np.ones(5)), 'tmax': ('x', sim['tmax'].values)}, obs.coords) self.assertDatasetIdentical(actual, expected) actual = sim.copy() actual['tmin'] -= obs['tmin'] self.assertDatasetIdentical(actual, expected) def test_stack_unstack(self): orig = DataArray([[0, 1], [2, 3]], dims=['x', 'y'], attrs={'foo': 2}) actual = orig.stack(z=['x', 'y']).unstack('z').drop(['x', 'y']) self.assertDataArrayIdentical(orig, actual) def test_stack_unstack_decreasing_coordinate(self): # regression test for GH980 orig = DataArray(np.random.rand(3, 4), dims=('y', 'x'), coords={'x': np.arange(4), 'y': np.arange(3, 0, -1)}) stacked = orig.stack(allpoints=['y', 'x']) actual = stacked.unstack('allpoints') self.assertDataArrayIdentical(orig, actual) def test_unstack_pandas_consistency(self): df = pd.DataFrame({'foo': range(3), 'x': ['a', 'b', 'b'], 'y': [0, 0, 1]}) s = df.set_index(['x', 'y'])['foo'] expected = DataArray(s.unstack(), name='foo') actual = DataArray(s, dims='z').unstack('z') self.assertDataArrayIdentical(expected, actual) def test_transpose(self): self.assertVariableEqual(self.dv.variable.transpose(), self.dv.transpose().variable) def test_squeeze(self): self.assertVariableEqual(self.dv.variable.squeeze(), self.dv.squeeze().variable) def test_squeeze_drop(self): array = DataArray([1], [('x', [0])]) expected = DataArray(1) actual = array.squeeze(drop=True) self.assertDataArrayIdentical(expected, actual) expected = DataArray(1, {'x': 0}) actual = array.squeeze(drop=False) self.assertDataArrayIdentical(expected, actual) def test_drop_coordinates(self): expected = DataArray(np.random.randn(2, 3), dims=['x', 'y']) arr = expected.copy() arr.coords['z'] = 2 actual = arr.drop('z') self.assertDataArrayIdentical(expected, actual) with self.assertRaises(ValueError): arr.drop('not found') with self.assertRaisesRegexp(ValueError, 'cannot be found'): arr.drop(None) renamed = arr.rename('foo') with self.assertRaisesRegexp(ValueError, 'cannot be found'): renamed.drop('foo') def test_drop_index_labels(self): arr = DataArray(np.random.randn(2, 3), coords={'y': [0, 1, 2]}, dims=['x', 'y']) actual = arr.drop([0, 1], dim='y') expected = arr[:, 2:] self.assertDataArrayIdentical(expected, actual) def test_dropna(self): x = np.random.randn(4, 4) x[::2, 0] = np.nan arr = DataArray(x, dims=['a', 'b']) actual = arr.dropna('a') expected = arr[1::2] self.assertDataArrayIdentical(actual, expected) actual = arr.dropna('b', how='all') self.assertDataArrayIdentical(actual, arr) actual = arr.dropna('a', thresh=1) self.assertDataArrayIdentical(actual, arr) actual = arr.dropna('b', thresh=3) expected = arr[:, 1:] self.assertDataArrayIdentical(actual, expected) def test_where(self): arr = DataArray(np.arange(4), dims='x') expected = arr.sel(x=slice(2)) actual = arr.where(arr.x < 2, drop=True) self.assertDataArrayIdentical(actual, expected) def test_cumops(self): coords = {'x': [-1, -2], 'y': ['ab', 'cd', 'ef'], 'lat': (['x', 'y'], [[1, 2, 3], [-1, -2, -3]]), 'c': -999} orig = DataArray([[-1, 0, 1], [-3, 0, 3]], coords, dims=['x', 'y']) actual = orig.cumsum('x') expected = DataArray([[-1, 0, 1], [-4, 0, 4]], coords, dims=['x', 'y']) self.assertDataArrayIdentical(expected, actual) actual = orig.cumsum('y') expected = DataArray([[-1, -1, 0], [-3, -3, 0]], coords, dims=['x', 'y']) self.assertDataArrayIdentical(expected, actual) actual = orig.cumprod('x') expected = DataArray([[-1, 0, 1], [3, 0, 3]], coords, dims=['x', 'y']) self.assertDataArrayIdentical(expected, actual) actual = orig.cumprod('y') expected = DataArray([[-1, 0, 0], [-3, 0, 0]], coords, dims=['x', 'y']) self.assertDataArrayIdentical(expected, actual) def test_reduce(self): coords = {'x': [-1, -2], 'y': ['ab', 'cd', 'ef'], 'lat': (['x', 'y'], [[1, 2, 3], [-1, -2, -3]]), 'c': -999} orig = DataArray([[-1, 0, 1], [-3, 0, 3]], coords, dims=['x', 'y']) actual = orig.mean() expected = DataArray(0, {'c': -999}) self.assertDataArrayIdentical(expected, actual) actual = orig.mean(['x', 'y']) self.assertDataArrayIdentical(expected, actual) actual = orig.mean('x') expected = DataArray([-2, 0, 2], {'y': coords['y'], 'c': -999}, 'y') self.assertDataArrayIdentical(expected, actual) actual = orig.mean(['x']) self.assertDataArrayIdentical(expected, actual) actual = orig.mean('y') expected = DataArray([0, 0], {'x': coords['x'], 'c': -999}, 'x') self.assertDataArrayIdentical(expected, actual) self.assertVariableEqual(self.dv.reduce(np.mean, 'x').variable, self.v.reduce(np.mean, 'x')) orig = DataArray([[1, 0, np.nan], [3, 0, 3]], coords, dims=['x', 'y']) actual = orig.count() expected = DataArray(5, {'c': -999}) self.assertDataArrayIdentical(expected, actual) @pytest.mark.skipif(LooseVersion(np.__version__) < LooseVersion('1.10.0'), reason='requires numpy version 1.10.0 or later') # skip due to bug in older versions of numpy.nanpercentile def test_quantile(self): for q in [0.25, [0.50], [0.25, 0.75]]: for axis, dim in zip([None, 0, [0], [0, 1]], [None, 'x', ['x'], ['x', 'y']]): actual = self.dv.quantile(q, dim=dim) expected = np.nanpercentile(self.dv.values, np.array(q) * 100, axis=axis) np.testing.assert_allclose(actual.values, expected) def test_reduce_keep_attrs(self): # Test dropped attrs vm = self.va.mean() self.assertEqual(len(vm.attrs), 0) self.assertEqual(vm.attrs, OrderedDict()) # Test kept attrs vm = self.va.mean(keep_attrs=True) self.assertEqual(len(vm.attrs), len(self.attrs)) self.assertEqual(vm.attrs, self.attrs) def test_fillna(self): a = DataArray([np.nan, 1, np.nan, 3], coords={'x': range(4)}, dims='x') actual = a.fillna(-1) expected = DataArray([-1, 1, -1, 3], coords={'x': range(4)}, dims='x') self.assertDataArrayIdentical(expected, actual) b = DataArray(range(4), coords={'x': range(4)}, dims='x') actual = a.fillna(b) expected = b.copy() self.assertDataArrayIdentical(expected, actual) actual = a.fillna(range(4)) self.assertDataArrayIdentical(expected, actual) actual = a.fillna(b[:3]) self.assertDataArrayIdentical(expected, actual) actual = a.fillna(b[:0]) self.assertDataArrayIdentical(a, actual) with self.assertRaisesRegexp(TypeError, 'fillna on a DataArray'): a.fillna({0: 0}) with self.assertRaisesRegexp(ValueError, 'broadcast'): a.fillna([1, 2]) fill_value = DataArray([0, 1], dims='y') actual = a.fillna(fill_value) expected = DataArray([[0, 1], [1, 1], [0, 1], [3, 3]], coords={'x': range(4)}, dims=('x', 'y')) self.assertDataArrayIdentical(expected, actual) expected = b.copy() for target in [a, expected]: target.coords['b'] = ('x', [0, 0, 1, 1]) actual = a.groupby('b').fillna(DataArray([0, 2], dims='b')) self.assertDataArrayIdentical(expected, actual) def test_groupby_iter(self): for ((act_x, act_dv), (exp_x, exp_ds)) in \ zip(self.dv.groupby('y'), self.ds.groupby('y')): self.assertEqual(exp_x, act_x) self.assertDataArrayIdentical(exp_ds['foo'], act_dv) for ((_, exp_dv), act_dv) in zip(self.dv.groupby('x'), self.dv): self.assertDataArrayIdentical(exp_dv, act_dv) def make_groupby_example_array(self): da = self.dv.copy() da.coords['abc'] = ('y', np.array(['a'] * 9 + ['c'] + ['b'] * 10)) da.coords['y'] = 20 + 100 * da['y'] return da def test_groupby_properties(self): grouped = self.make_groupby_example_array().groupby('abc') expected_groups = {'a': range(0, 9), 'c': [9], 'b': range(10, 20)} self.assertItemsEqual(expected_groups.keys(), grouped.groups.keys()) for key in expected_groups: self.assertArrayEqual(expected_groups[key], grouped.groups[key]) self.assertEqual(3, len(grouped)) def test_groupby_apply_identity(self): expected = self.make_groupby_example_array() idx = expected.coords['y'] def identity(x): return x for g in ['x', 'y', 'abc', idx]: for shortcut in [False, True]: for squeeze in [False, True]: grouped = expected.groupby(g, squeeze=squeeze) actual = grouped.apply(identity, shortcut=shortcut) self.assertDataArrayIdentical(expected, actual) def test_groupby_sum(self): array = self.make_groupby_example_array() grouped = array.groupby('abc') expected_sum_all = Dataset( {'foo': Variable(['abc'], np.array([self.x[:, :9].sum(), self.x[:, 10:].sum(), self.x[:, 9:10].sum()]).T), 'abc': Variable(['abc'], np.array(['a', 'b', 'c']))})['foo'] self.assertDataArrayAllClose(expected_sum_all, grouped.reduce(np.sum)) self.assertDataArrayAllClose(expected_sum_all, grouped.sum()) expected = DataArray([array['y'].values[idx].sum() for idx in [slice(9), slice(10, None), slice(9, 10)]], [['a', 'b', 'c']], ['abc']) actual = array['y'].groupby('abc').apply(np.sum) self.assertDataArrayAllClose(expected, actual) actual = array['y'].groupby('abc').sum() self.assertDataArrayAllClose(expected, actual) expected_sum_axis1 = Dataset( {'foo': (['x', 'abc'], np.array([self.x[:, :9].sum(1), self.x[:, 10:].sum(1), self.x[:, 9:10].sum(1)]).T), 'abc': Variable(['abc'], np.array(['a', 'b', 'c']))})['foo'] self.assertDataArrayAllClose(expected_sum_axis1, grouped.reduce(np.sum, 'y')) self.assertDataArrayAllClose(expected_sum_axis1, grouped.sum('y')) def test_groupby_count(self): array = DataArray([0, 0, np.nan, np.nan, 0, 0], coords={'cat': ('x', ['a', 'b', 'b', 'c', 'c', 'c'])}, dims='x') actual = array.groupby('cat').count() expected = DataArray([1, 1, 2], coords=[('cat', ['a', 'b', 'c'])]) self.assertDataArrayIdentical(actual, expected) @unittest.skip('needs to be fixed for shortcut=False, keep_attrs=False') def test_groupby_reduce_attrs(self): array = self.make_groupby_example_array() array.attrs['foo'] = 'bar' for shortcut in [True, False]: for keep_attrs in [True, False]: print('shortcut=%s, keep_attrs=%s' % (shortcut, keep_attrs)) actual = array.groupby('abc').reduce( np.mean, keep_attrs=keep_attrs, shortcut=shortcut) expected = array.groupby('abc').mean() if keep_attrs: expected.attrs['foo'] = 'bar' self.assertDataArrayIdentical(expected, actual) def test_groupby_apply_center(self): def center(x): return x - np.mean(x) array = self.make_groupby_example_array() grouped = array.groupby('abc') expected_ds = array.to_dataset() exp_data = np.hstack([center(self.x[:, :9]), center(self.x[:, 9:10]), center(self.x[:, 10:])]) expected_ds['foo'] = (['x', 'y'], exp_data) expected_centered = expected_ds['foo'] self.assertDataArrayAllClose(expected_centered, grouped.apply(center)) def test_groupby_apply_ndarray(self): # regression test for #326 array = self.make_groupby_example_array() grouped = array.groupby('abc') actual = grouped.apply(np.asarray) self.assertDataArrayEqual(array, actual) def test_groupby_apply_changes_metadata(self): def change_metadata(x): x.coords['x'] = x.coords['x'] * 2 x.attrs['fruit'] = 'lemon' return x array = self.make_groupby_example_array() grouped = array.groupby('abc') actual = grouped.apply(change_metadata) expected = array.copy() expected = change_metadata(expected) self.assertDataArrayEqual(expected, actual) def test_groupby_math(self): array = self.make_groupby_example_array() for squeeze in [True, False]: grouped = array.groupby('x', squeeze=squeeze) expected = array + array.coords['x'] actual = grouped + array.coords['x'] self.assertDataArrayIdentical(expected, actual) actual = array.coords['x'] + grouped self.assertDataArrayIdentical(expected, actual) ds = array.coords['x'].to_dataset('X') expected = array + ds actual = grouped + ds self.assertDatasetIdentical(expected, actual) actual = ds + grouped self.assertDatasetIdentical(expected, actual) grouped = array.groupby('abc') expected_agg = (grouped.mean() - np.arange(3)).rename(None) actual = grouped - DataArray(range(3), [('abc', ['a', 'b', 'c'])]) actual_agg = actual.groupby('abc').mean() self.assertDataArrayAllClose(expected_agg, actual_agg) with self.assertRaisesRegexp(TypeError, 'only support binary ops'): grouped + 1 with self.assertRaisesRegexp(TypeError, 'only support binary ops'): grouped + grouped with self.assertRaisesRegexp(TypeError, 'in-place operations'): array += grouped def test_groupby_math_not_aligned(self): array = DataArray(range(4), {'b': ('x', [0, 0, 1, 1]), 'x': [0, 1, 2, 3]}, dims='x') other = DataArray([10], coords={'b': [0]}, dims='b') actual = array.groupby('b') + other expected = DataArray([10, 11, np.nan, np.nan], array.coords) self.assertDataArrayIdentical(expected, actual) other = DataArray([10], coords={'c': 123, 'b': [0]}, dims='b') actual = array.groupby('b') + other expected.coords['c'] = (['x'], [123] * 2 + [np.nan] * 2) self.assertDataArrayIdentical(expected, actual) other = Dataset({'a': ('b', [10])}, {'b': [0]}) actual = array.groupby('b') + other expected = Dataset({'a': ('x', [10, 11, np.nan, np.nan])}, array.coords) self.assertDatasetIdentical(expected, actual) def test_groupby_restore_dim_order(self): array = DataArray(np.random.randn(5, 3), coords={'a': ('x', range(5)), 'b': ('y', range(3))}, dims=['x', 'y']) for by, expected_dims in [('x', ('x', 'y')), ('y', ('x', 'y')), ('a', ('a', 'y')), ('b', ('x', 'b'))]: result = array.groupby(by).apply(lambda x: x.squeeze()) self.assertEqual(result.dims, expected_dims) def test_groupby_first_and_last(self): array = DataArray([1, 2, 3, 4, 5], dims='x') by = DataArray(['a'] * 2 + ['b'] * 3, dims='x', name='ab') expected = DataArray([1, 3], [('ab', ['a', 'b'])]) actual = array.groupby(by).first() self.assertDataArrayIdentical(expected, actual) expected = DataArray([2, 5], [('ab', ['a', 'b'])]) actual = array.groupby(by).last() self.assertDataArrayIdentical(expected, actual) array = DataArray(np.random.randn(5, 3), dims=['x', 'y']) expected = DataArray(array[[0, 2]], {'ab': ['a', 'b']}, ['ab', 'y']) actual = array.groupby(by).first() self.assertDataArrayIdentical(expected, actual) actual = array.groupby('x').first() expected = array # should be a no-op self.assertDataArrayIdentical(expected, actual) def make_groupby_multidim_example_array(self): return DataArray([[[0, 1], [2, 3]], [[5, 10], [15, 20]]], coords={'lon': (['ny', 'nx'], [[30, 40], [40, 50]]), 'lat': (['ny', 'nx'], [[10, 10], [20, 20]])}, dims=['time', 'ny', 'nx']) def test_groupby_multidim(self): array = self.make_groupby_multidim_example_array() for dim, expected_sum in [ ('lon', DataArray([5, 28, 23], coords=[('lon', [30., 40., 50.])])), ('lat', DataArray([16, 40], coords=[('lat', [10., 20.])]))]: actual_sum = array.groupby(dim).sum() self.assertDataArrayIdentical(expected_sum, actual_sum) def test_groupby_multidim_apply(self): array = self.make_groupby_multidim_example_array() actual = array.groupby('lon').apply(lambda x: x - x.mean()) expected = DataArray([[[-2.5, -6.], [-5., -8.5]], [[2.5, 3.], [8., 8.5]]], coords=array.coords, dims=array.dims) self.assertDataArrayIdentical(expected, actual) def test_groupby_bins(self): array = DataArray(np.arange(4), dims='dim_0') # the first value should not be part of any group ("right" binning) array[0] = 99 # bins follow conventions for pandas.cut # http://pandas.pydata.org/pandas-docs/stable/generated/pandas.cut.html bins = [0, 1.5, 5] bin_coords = ['(0, 1.5]', '(1.5, 5]'] expected = DataArray([1, 5], dims='dim_0_bins', coords={'dim_0_bins': bin_coords}) # the problem with this is that it overwrites the dimensions of array! # actual = array.groupby('dim_0', bins=bins).sum() actual = array.groupby_bins('dim_0', bins).apply(lambda x: x.sum()) self.assertDataArrayIdentical(expected, actual) # make sure original array dims are unchanged self.assertEqual(len(array.dim_0), 4) def test_groupby_bins_empty(self): array = DataArray(np.arange(4), [('x', range(4))]) # one of these bins will be empty bins = [0, 4, 5] actual = array.groupby_bins('x', bins).sum() expected = DataArray([6, np.nan], dims='x_bins', coords={'x_bins': ['(0, 4]', '(4, 5]']}) self.assertDataArrayIdentical(expected, actual) # make sure original array is unchanged # (was a problem in earlier versions) self.assertEqual(len(array.x), 4) def test_groupby_bins_multidim(self): array = self.make_groupby_multidim_example_array() bins = [0,15,20] bin_coords = ['(0, 15]', '(15, 20]'] expected = DataArray([16, 40], dims='lat_bins', coords={'lat_bins': bin_coords}) actual = array.groupby_bins('lat', bins).apply(lambda x: x.sum()) self.assertDataArrayIdentical(expected, actual) # modify the array coordinates to be non-monotonic after unstacking array['lat'].data = np.array([[10., 20.], [20., 10.]]) expected = DataArray([28, 28], dims='lat_bins', coords={'lat_bins': bin_coords}) actual = array.groupby_bins('lat', bins).apply(lambda x: x.sum()) self.assertDataArrayIdentical(expected, actual) def test_groupby_bins_sort(self): data = xr.DataArray( np.arange(100), dims='x', coords={'x': np.linspace(-100, 100, num=100)}) binned_mean = data.groupby_bins('x', bins=11).mean() assert binned_mean.to_index().is_monotonic def test_resample(self): times = pd.date_range('2000-01-01', freq='6H', periods=10) array = DataArray(np.arange(10), [('time', times)]) actual = array.resample('6H', dim='time') self.assertDataArrayIdentical(array, actual) actual = array.resample('24H', dim='time') expected = DataArray(array.to_series().resample('24H', how='mean')) self.assertDataArrayIdentical(expected, actual) actual = array.resample('24H', dim='time', how=np.mean) self.assertDataArrayIdentical(expected, actual) with self.assertRaisesRegexp(ValueError, 'index must be monotonic'): array[[2, 0, 1]].resample('1D', dim='time') def test_resample_first(self): times = pd.date_range('2000-01-01', freq='6H', periods=10) array = DataArray(np.arange(10), [('time', times)]) actual = array.resample('1D', dim='time', how='first') expected = DataArray([0, 4, 8], [('time', times[::4])]) self.assertDataArrayIdentical(expected, actual) # verify that labels don't use the first value actual = array.resample('24H', dim='time', how='first') expected = DataArray(array.to_series().resample('24H', how='first')) self.assertDataArrayIdentical(expected, actual) # missing values array = array.astype(float) array[:2] = np.nan actual = array.resample('1D', dim='time', how='first') expected = DataArray([2, 4, 8], [('time', times[::4])]) self.assertDataArrayIdentical(expected, actual) actual = array.resample('1D', dim='time', how='first', skipna=False) expected = DataArray([np.nan, 4, 8], [('time', times[::4])]) self.assertDataArrayIdentical(expected, actual) # regression test for http://stackoverflow.com/questions/33158558/ array = Dataset({'time': times})['time'] actual = array.resample('1D', dim='time', how='last') expected_times = pd.to_datetime(['2000-01-01T18', '2000-01-02T18', '2000-01-03T06']) expected = DataArray(expected_times, [('time', times[::4])], name='time') self.assertDataArrayIdentical(expected, actual) def test_resample_first_keep_attrs(self): times = pd.date_range('2000-01-01', freq='6H', periods=10) array = DataArray(np.arange(10), [('time', times)]) array.attrs['meta'] = 'data' resampled_array = array.resample('1D', dim='time', how='first', keep_attrs=True) actual = resampled_array.attrs expected = array.attrs self.assertEqual(expected, actual) resampled_array = array.resample('1D', dim='time', how='first', keep_attrs=False) assert resampled_array.attrs == {} def test_resample_mean_keep_attrs(self): times = pd.date_range('2000-01-01', freq='6H', periods=10) array = DataArray(np.arange(10), [('time', times)]) array.attrs['meta'] = 'data' resampled_array = array.resample('1D', dim='time', how='mean', keep_attrs=True) actual = resampled_array.attrs expected = array.attrs self.assertEqual(expected, actual) resampled_array = array.resample('1D', dim='time', how='mean', keep_attrs=False) assert resampled_array.attrs == {} def test_resample_skipna(self): times = pd.date_range('2000-01-01', freq='6H', periods=10) array = DataArray(np.ones(10), [('time', times)]) array[1] = np.nan actual = array.resample('1D', dim='time', skipna=False) expected = DataArray([np.nan, 1, 1], [('time', times[::4])]) self.assertDataArrayIdentical(expected, actual) def test_resample_upsampling(self): times = pd.date_range('2000-01-01', freq='1D', periods=5) array = DataArray(np.arange(5), [('time', times)]) expected_time = pd.date_range('2000-01-01', freq='12H', periods=9) expected = array.reindex(time=expected_time) for how in ['mean', 'median', 'sum', 'first', 'last', np.mean]: actual = array.resample('12H', 'time', how=how) self.assertDataArrayIdentical(expected, actual) def test_align(self): array = DataArray(np.random.random((6, 8)), coords={'x': list('abcdef')}, dims=['x', 'y']) array1, array2 = align(array, array[:5], join='inner') self.assertDataArrayIdentical(array1, array[:5]) self.assertDataArrayIdentical(array2, array[:5]) def test_align_dtype(self): # regression test for #264 x1 = np.arange(30) x2 = np.arange(5, 35) a = DataArray(np.random.random((30,)).astype(np.float32), [('x', x1)]) b = DataArray(np.random.random((30,)).astype(np.float32), [('x', x2)]) c, d = align(a, b, join='outer') self.assertEqual(c.dtype, np.float32) def test_align_copy(self): x = DataArray([1, 2, 3], coords=[('a', [1, 2, 3])]) y = DataArray([1, 2], coords=[('a', [3, 1])]) expected_x2 = x expected_y2 = DataArray([2, np.nan, 1], coords=[('a', [1, 2, 3])]) x2, y2 = align(x, y, join='outer', copy=False) self.assertDataArrayIdentical(expected_x2, x2) self.assertDataArrayIdentical(expected_y2, y2) assert source_ndarray(x2.data) is source_ndarray(x.data) x2, y2 = align(x, y, join='outer', copy=True) self.assertDataArrayIdentical(expected_x2, x2) self.assertDataArrayIdentical(expected_y2, y2) assert source_ndarray(x2.data) is not source_ndarray(x.data) # Trivial align - 1 element x = DataArray([1, 2, 3], coords=[('a', [1, 2, 3])]) x2, = align(x, copy=False) self.assertDataArrayIdentical(x, x2) assert source_ndarray(x2.data) is source_ndarray(x.data) x2, = align(x, copy=True) self.assertDataArrayIdentical(x, x2) assert source_ndarray(x2.data) is not source_ndarray(x.data) def test_align_exclude(self): x = DataArray([[1, 2], [3, 4]], coords=[('a', [-1, -2]), ('b', [3, 4])]) y = DataArray([[1, 2], [3, 4]], coords=[('a', [-1, 20]), ('b', [5, 6])]) z = DataArray([1], dims=['a'], coords={'a': [20], 'b': 7}) x2, y2, z2 = align(x, y, z, join='outer', exclude=['b']) expected_x2 = DataArray([[3, 4], [1, 2], [np.nan, np.nan]], coords=[('a', [-2, -1, 20]), ('b', [3, 4])]) expected_y2 = DataArray([[np.nan, np.nan], [1, 2], [3, 4]], coords=[('a', [-2, -1, 20]), ('b', [5, 6])]) expected_z2 = DataArray([np.nan, np.nan, 1], dims=['a'], coords={'a': [-2, -1, 20], 'b': 7}) self.assertDataArrayIdentical(expected_x2, x2) self.assertDataArrayIdentical(expected_y2, y2) self.assertDataArrayIdentical(expected_z2, z2) def test_align_indexes(self): x = DataArray([1, 2, 3], coords=[('a', [-1, 10, -2])]) y = DataArray([1, 2], coords=[('a', [-2, -1])]) x2, y2 = align(x, y, join='outer', indexes={'a': [10, -1, -2]}) expected_x2 = DataArray([2, 1, 3], coords=[('a', [10, -1, -2])]) expected_y2 = DataArray([np.nan, 2, 1], coords=[('a', [10, -1, -2])]) self.assertDataArrayIdentical(expected_x2, x2) self.assertDataArrayIdentical(expected_y2, y2) x2, = align(x, join='outer', indexes={'a': [-2, 7, 10, -1]}) expected_x2 = DataArray([3, np.nan, 2, 1], coords=[('a', [-2, 7, 10, -1])]) self.assertDataArrayIdentical(expected_x2, x2) def test_align_without_indexes_exclude(self): arrays = [DataArray([1, 2, 3], dims=['x']), DataArray([1, 2], dims=['x'])] result0, result1 = align(*arrays, exclude=['x']) self.assertDatasetIdentical(result0, arrays[0]) self.assertDatasetIdentical(result1, arrays[1]) def test_align_mixed_indexes(self): array_no_coord = DataArray([1, 2], dims=['x']) array_with_coord = DataArray([1, 2], coords=[('x', ['a', 'b'])]) result0, result1 = align(array_no_coord, array_with_coord) self.assertDatasetIdentical(result0, array_with_coord) self.assertDatasetIdentical(result1, array_with_coord) result0, result1 = align(array_no_coord, array_with_coord, exclude=['x']) self.assertDatasetIdentical(result0, array_no_coord) self.assertDatasetIdentical(result1, array_with_coord) def test_align_without_indexes_errors(self): with self.assertRaisesRegexp(ValueError, 'cannot be aligned'): align(DataArray([1, 2, 3], dims=['x']), DataArray([1, 2], dims=['x'])) with self.assertRaisesRegexp(ValueError, 'cannot be aligned'): align(DataArray([1, 2, 3], dims=['x']), DataArray([1, 2], coords=[('x', [0, 1])])) def test_broadcast_arrays(self): x = DataArray([1, 2], coords=[('a', [-1, -2])], name='x') y = DataArray([1, 2], coords=[('b', [3, 4])], name='y') x2, y2 = broadcast(x, y) expected_coords = [('a', [-1, -2]), ('b', [3, 4])] expected_x2 = DataArray([[1, 1], [2, 2]], expected_coords, name='x') expected_y2 = DataArray([[1, 2], [1, 2]], expected_coords, name='y') self.assertDataArrayIdentical(expected_x2, x2) self.assertDataArrayIdentical(expected_y2, y2) x = DataArray(np.random.randn(2, 3), dims=['a', 'b']) y = DataArray(np.random.randn(3, 2), dims=['b', 'a']) x2, y2 = broadcast(x, y) expected_x2 = x expected_y2 = y.T self.assertDataArrayIdentical(expected_x2, x2) self.assertDataArrayIdentical(expected_y2, y2) def test_broadcast_arrays_misaligned(self): # broadcast on misaligned coords must auto-align x = DataArray([[1, 2], [3, 4]], coords=[('a', [-1, -2]), ('b', [3, 4])]) y = DataArray([1, 2], coords=[('a', [-1, 20])]) expected_x2 = DataArray([[3, 4], [1, 2], [np.nan, np.nan]], coords=[('a', [-2, -1, 20]), ('b', [3, 4])]) expected_y2 = DataArray([[np.nan, np.nan], [1, 1], [2, 2]], coords=[('a', [-2, -1, 20]), ('b', [3, 4])]) x2, y2 = broadcast(x, y) self.assertDataArrayIdentical(expected_x2, x2) self.assertDataArrayIdentical(expected_y2, y2) def test_broadcast_arrays_nocopy(self): # Test that input data is not copied over in case no alteration is needed x = DataArray([1, 2], coords=[('a', [-1, -2])], name='x') y = DataArray(3, name='y') expected_x2 = DataArray([1, 2], coords=[('a', [-1, -2])], name='x') expected_y2 = DataArray([3, 3], coords=[('a', [-1, -2])], name='y') x2, y2 = broadcast(x, y) self.assertDataArrayIdentical(expected_x2, x2) self.assertDataArrayIdentical(expected_y2, y2) assert source_ndarray(x2.data) is source_ndarray(x.data) # single-element broadcast (trivial case) x2, = broadcast(x) self.assertDataArrayIdentical(x, x2) assert source_ndarray(x2.data) is source_ndarray(x.data) def test_broadcast_arrays_exclude(self): x = DataArray([[1, 2], [3, 4]], coords=[('a', [-1, -2]), ('b', [3, 4])]) y = DataArray([1, 2], coords=[('a', [-1, 20])]) z = DataArray(5, coords={'b': 5}) x2, y2, z2 = broadcast(x, y, z, exclude=['b']) expected_x2 = DataArray([[3, 4], [1, 2], [np.nan, np.nan]], coords=[('a', [-2, -1, 20]), ('b', [3, 4])]) expected_y2 = DataArray([np.nan, 1, 2], coords=[('a', [-2, -1, 20])]) expected_z2 = DataArray([5, 5, 5], dims=['a'], coords={'a': [-2, -1, 20], 'b': 5}) self.assertDataArrayIdentical(expected_x2, x2) self.assertDataArrayIdentical(expected_y2, y2) self.assertDataArrayIdentical(expected_z2, z2) def test_broadcast_coordinates(self): # regression test for GH649 ds = Dataset({'a': (['x', 'y'], np.ones((5, 6)))}) x_bc, y_bc, a_bc = broadcast(ds.x, ds.y, ds.a) self.assertDataArrayIdentical(ds.a, a_bc) X, Y = np.meshgrid(np.arange(5), np.arange(6), indexing='ij') exp_x = DataArray(X, dims=['x', 'y'], name='x') exp_y = DataArray(Y, dims=['x', 'y'], name='y') self.assertDataArrayIdentical(exp_x, x_bc) self.assertDataArrayIdentical(exp_y, y_bc) def test_to_pandas(self): # 0d actual = DataArray(42).to_pandas() expected = np.array(42) self.assertArrayEqual(actual, expected) # 1d values = np.random.randn(3) index = pd.Index(['a', 'b', 'c'], name='x') da = DataArray(values, coords=[index]) actual = da.to_pandas() self.assertArrayEqual(actual.values, values) self.assertArrayEqual(actual.index, index) self.assertArrayEqual(actual.index.name, 'x') # 2d values = np.random.randn(3, 2) da = DataArray(values, coords=[('x', ['a', 'b', 'c']), ('y', [0, 1])], name='foo') actual = da.to_pandas() self.assertArrayEqual(actual.values, values) self.assertArrayEqual(actual.index, ['a', 'b', 'c']) self.assertArrayEqual(actual.columns, [0, 1]) # roundtrips for shape in [(3,), (3, 4), (3, 4, 5)]: dims = list('abc')[:len(shape)] da = DataArray(np.random.randn(*shape), dims=dims) roundtripped = DataArray(da.to_pandas()).drop(dims) self.assertDataArrayIdentical(da, roundtripped) with self.assertRaisesRegexp(ValueError, 'cannot convert'): DataArray(np.random.randn(1, 2, 3, 4, 5)).to_pandas() def test_to_dataframe(self): # regression test for #260 arr = DataArray(np.random.randn(3, 4), [('B', [1, 2, 3]), ('A', list('cdef'))], name='foo') expected = arr.to_series() actual = arr.to_dataframe()['foo'] self.assertArrayEqual(expected.values, actual.values) self.assertArrayEqual(expected.name, actual.name) self.assertArrayEqual(expected.index.values, actual.index.values) # regression test for coords with different dimensions arr.coords['C'] = ('B', [-1, -2, -3]) expected = arr.to_series().to_frame() expected['C'] = [-1] * 4 + [-2] * 4 + [-3] * 4 expected = expected[['C', 'foo']] actual = arr.to_dataframe() self.assertArrayEqual(expected.values, actual.values) self.assertArrayEqual(expected.columns.values, actual.columns.values) self.assertArrayEqual(expected.index.values, actual.index.values) arr.name = None # unnamed with self.assertRaisesRegexp(ValueError, 'unnamed'): arr.to_dataframe() def test_to_pandas_name_matches_coordinate(self): # coordinate with same name as array arr = DataArray([1, 2, 3], dims='x', name='x') series = arr.to_series() self.assertArrayEqual([1, 2, 3], series.values) self.assertArrayEqual([0, 1, 2], series.index.values) self.assertEqual('x', series.name) self.assertEqual('x', series.index.name) frame = arr.to_dataframe() expected = series.to_frame() self.assertTrue(expected.equals(frame)) def test_to_and_from_series(self): expected = self.dv.to_dataframe()['foo'] actual = self.dv.to_series() self.assertArrayEqual(expected.values, actual.values) self.assertArrayEqual(expected.index.values, actual.index.values) self.assertEqual('foo', actual.name) # test roundtrip self.assertDataArrayIdentical( self.dv, DataArray.from_series(actual).drop(['x', 'y'])) # test name is None actual.name = None expected_da = self.dv.rename(None) self.assertDataArrayIdentical( expected_da, DataArray.from_series(actual).drop(['x', 'y'])) def test_series_categorical_index(self): # regression test for GH700 if not hasattr(pd, 'CategoricalIndex'): raise unittest.SkipTest('requires pandas with CategoricalIndex') s = pd.Series(range(5), index=pd.CategoricalIndex(list('aabbc'))) arr = DataArray(s) assert "'a'" in repr(arr) # should not error def test_to_and_from_dict(self): array = DataArray(np.random.randn(2, 3), {'x': ['a', 'b']}, ['x', 'y'], name='foo') expected = {'name': 'foo', 'dims': ('x', 'y'), 'data': array.values.tolist(), 'attrs': {}, 'coords': {'x': {'dims': ('x',), 'data': ['a', 'b'], 'attrs': {}}}} actual = array.to_dict() # check that they are identical self.assertEqual(expected, actual) # check roundtrip self.assertDataArrayIdentical(array, DataArray.from_dict(actual)) # a more bare bones representation still roundtrips d = {'name': 'foo', 'dims': ('x', 'y'), 'data': array.values.tolist(), 'coords': {'x': {'dims': 'x', 'data': ['a', 'b']}}} self.assertDataArrayIdentical(array, DataArray.from_dict(d)) # and the most bare bones representation still roundtrips d = {'name': 'foo', 'dims': ('x', 'y'), 'data': array.values} self.assertDataArrayIdentical(array.drop('x'), DataArray.from_dict(d)) # missing a dims in the coords d = {'dims': ('x', 'y'), 'data': array.values, 'coords': {'x': {'data': ['a', 'b']}}} with self.assertRaisesRegexp( ValueError, "cannot convert dict when coords are missing the key 'dims'"): DataArray.from_dict(d) # this one is missing some necessary information d = {'dims': ('t')} with self.assertRaisesRegexp( ValueError, "cannot convert dict without the key 'data'"): DataArray.from_dict(d) def test_to_and_from_dict_with_time_dim(self): x = np.random.randn(10, 3) t = pd.date_range('20130101', periods=10) lat = [77.7, 83.2, 76] da = DataArray(x, {'t': t, 'lat': lat}, dims=['t', 'lat']) roundtripped = DataArray.from_dict(da.to_dict()) self.assertDataArrayIdentical(da, roundtripped) def test_to_and_from_dict_with_nan_nat(self): y = np.random.randn(10, 3) y[2] = np.nan t = pd.Series(pd.date_range('20130101', periods=10)) t[2] = np.nan lat = [77.7, 83.2, 76] da = DataArray(y, {'t': t, 'lat': lat}, dims=['t', 'lat']) roundtripped = DataArray.from_dict(da.to_dict()) self.assertDataArrayIdentical(da, roundtripped) def test_to_dict_with_numpy_attrs(self): # this doesn't need to roundtrip x = np.random.randn(10, 3) t = list('abcdefghij') lat = [77.7, 83.2, 76] attrs = {'created': np.float64(1998), 'coords': np.array([37, -110.1, 100]), 'maintainer': 'bar'} da = DataArray(x, {'t': t, 'lat': lat}, dims=['t', 'lat'], attrs=attrs) expected_attrs = {'created': np.asscalar(attrs['created']), 'coords': attrs['coords'].tolist(), 'maintainer': 'bar'} actual = da.to_dict() # check that they are identical self.assertEqual(expected_attrs, actual['attrs']) def test_to_masked_array(self): rs = np.random.RandomState(44) x = rs.random_sample(size=(10, 20)) x_masked = np.ma.masked_where(x < 0.5, x) da = DataArray(x_masked) # Test round trip x_masked_2 = da.to_masked_array() da_2 = DataArray(x_masked_2) self.assertArrayEqual(x_masked, x_masked_2) self.assertDataArrayEqual(da, da_2) da_masked_array = da.to_masked_array(copy=True) self.assertTrue(isinstance(da_masked_array, np.ma.MaskedArray)) # Test masks self.assertArrayEqual(da_masked_array.mask, x_masked.mask) # Test that mask is unpacked correctly self.assertArrayEqual(da.values, x_masked.filled(np.nan)) # Test that the underlying data (including nans) hasn't changed self.assertArrayEqual(da_masked_array, x_masked.filled(np.nan)) # Test that copy=False gives access to values masked_array = da.to_masked_array(copy=False) masked_array[0, 0] = 10. self.assertEqual(masked_array[0, 0], 10.) self.assertEqual(da[0, 0].values, 10.) self.assertTrue(masked_array.base is da.values) self.assertIsInstance(masked_array, np.ma.MaskedArray) # Test with some odd arrays for v in [4, np.nan, True, '4', 'four']: da = DataArray(v) ma = da.to_masked_array() self.assertIsInstance(ma, np.ma.MaskedArray) # Fix GH issue 684 - masked arrays mask should be an array not a scalar N = 4 v = range(N) da = DataArray(v) ma = da.to_masked_array() self.assertEqual(len(ma.mask), N) def test_to_and_from_cdms2(self): pytest.importorskip('cdms2') original = DataArray(np.arange(6).reshape(2, 3), [('distance', [-2, 2], {'units': 'meters'}), ('time', pd.date_range('2000-01-01', periods=3))], name='foo', attrs={'baz': 123}) expected_coords = [IndexVariable('distance', [-2, 2]), IndexVariable('time', [0, 1, 2])] actual = original.to_cdms2() self.assertArrayEqual(actual, original) self.assertEqual(actual.id, original.name) self.assertItemsEqual(actual.getAxisIds(), original.dims) for axis, coord in zip(actual.getAxisList(), expected_coords): self.assertEqual(axis.id, coord.name) self.assertArrayEqual(axis, coord.values) self.assertEqual(actual.baz, original.attrs['baz']) component_times = actual.getAxis(1).asComponentTime() self.assertEqual(len(component_times), 3) self.assertEqual(str(component_times[0]), '2000-1-1 0:0:0.0') roundtripped = DataArray.from_cdms2(actual) self.assertDataArrayIdentical(original, roundtripped) def test_to_dataset_whole(self): unnamed = DataArray([1, 2], dims='x') with self.assertRaisesRegexp(ValueError, 'unable to convert unnamed'): unnamed.to_dataset() actual = unnamed.to_dataset(name='foo') expected = Dataset({'foo': ('x', [1, 2])}) self.assertDatasetIdentical(expected, actual) named = DataArray([1, 2], dims='x', name='foo') actual = named.to_dataset() expected = Dataset({'foo': ('x', [1, 2])}) self.assertDatasetIdentical(expected, actual) expected = Dataset({'bar': ('x', [1, 2])}) with self.assertWarns('order of the arguments'): actual = named.to_dataset('bar') self.assertDatasetIdentical(expected, actual) def test_to_dataset_split(self): array = DataArray([1, 2, 3], coords=[('x', list('abc'))], attrs={'a': 1}) expected = Dataset(OrderedDict([('a', 1), ('b', 2), ('c', 3)]), attrs={'a': 1}) actual = array.to_dataset('x') self.assertDatasetIdentical(expected, actual) with self.assertRaises(TypeError): array.to_dataset('x', name='foo') roundtripped = actual.to_array(dim='x') self.assertDataArrayIdentical(array, roundtripped) array = DataArray([1, 2, 3], dims='x') expected = Dataset(OrderedDict([(0, 1), (1, 2), (2, 3)])) actual = array.to_dataset('x') self.assertDatasetIdentical(expected, actual) def test_to_dataset_retains_keys(self): # use dates as convenient non-str objects. Not a specific date test import datetime dates = [datetime.date(2000,1,d) for d in range(1,4)] array = DataArray([1, 2, 3], coords=[('x', dates)], attrs={'a': 1}) # convert to dateset and back again result = array.to_dataset('x').to_array(dim='x') self.assertDatasetEqual(array, result) def test__title_for_slice(self): array = DataArray(np.ones((4, 3, 2)), dims=['a', 'b', 'c'], coords={'a': range(4), 'b': range(3), 'c': range(2)}) self.assertEqual('', array._title_for_slice()) self.assertEqual('c = 0', array.isel(c=0)._title_for_slice()) title = array.isel(b=1, c=0)._title_for_slice() self.assertTrue('b = 1, c = 0' == title or 'c = 0, b = 1' == title) a2 = DataArray(np.ones((4, 1)), dims=['a', 'b']) self.assertEqual('', a2._title_for_slice()) def test__title_for_slice_truncate(self): array = DataArray(np.ones((4))) array.coords['a'] = 'a' * 100 array.coords['b'] = 'b' * 100 nchar = 80 title = array._title_for_slice(truncate=nchar) self.assertEqual(nchar, len(title)) self.assertTrue(title.endswith('...')) def test_dataarray_diff_n1(self): da = DataArray(np.random.randn(3, 4), dims=['x', 'y']) actual = da.diff('y') expected = DataArray(np.diff(da.values, axis=1), dims=['x', 'y']) self.assertDataArrayEqual(expected, actual) def test_coordinate_diff(self): # regression test for GH634 arr = DataArray(range(0, 20, 2), dims=['lon'], coords=[range(10)]) lon = arr.coords['lon'] expected = DataArray([1] * 9, dims=['lon'], coords=[range(1, 10)], name='lon') actual = lon.diff('lon') self.assertDataArrayEqual(expected, actual) def test_shift(self): arr = DataArray([1, 2, 3], dims='x') actual = arr.shift(x=1) expected = DataArray([np.nan, 1, 2], dims='x') self.assertDataArrayIdentical(expected, actual) arr = DataArray([1, 2, 3], [('x', ['a', 'b', 'c'])]) for offset in [-5, -2, -1, 0, 1, 2, 5]: expected = DataArray(arr.to_pandas().shift(offset)) actual = arr.shift(x=offset) self.assertDataArrayIdentical(expected, actual) def test_roll(self): arr = DataArray([1, 2, 3], coords={'x': range(3)}, dims='x') actual = arr.roll(x=1) expected = DataArray([3, 1, 2], coords=[('x', [2, 0, 1])]) self.assertDataArrayIdentical(expected, actual) def test_real_and_imag(self): array = DataArray(1 + 2j) self.assertDataArrayIdentical(array.real, DataArray(1)) self.assertDataArrayIdentical(array.imag, DataArray(2)) def test_setattr_raises(self): array = DataArray(0, coords={'scalar': 1}, attrs={'foo': 'bar'}) with self.assertRaisesRegexp(AttributeError, 'cannot set attr'): array.scalar = 2 with self.assertRaisesRegexp(AttributeError, 'cannot set attr'): array.foo = 2 with self.assertRaisesRegexp(AttributeError, 'cannot set attr'): array.other = 2 def test_full_like(self): # For more thorough tests, see test_variable.py da = DataArray(np.random.random(size=(2, 2)), dims=('x', 'y'), attrs={'attr1': 'value1'}, coords={'x': [4, 3]}, name='helloworld') actual = full_like(da, 2) expect = da.copy(deep=True) expect.values = [[2.0, 2.0], [2.0, 2.0]] self.assertDataArrayIdentical(expect, actual) # override dtype actual = full_like(da, fill_value=True, dtype=bool) expect.values = [[True, True], [True, True]] self.assertEquals(expect.dtype, bool) self.assertDataArrayIdentical(expect, actual) def test_dot(self): x = np.linspace(-3, 3, 6) y = np.linspace(-3, 3, 5) z = range(4) da_vals = np.arange(6 * 5 * 4).reshape((6, 5, 4)) da = DataArray(da_vals, coords=[x, y, z], dims=['x', 'y', 'z']) dm_vals = range(4) dm = DataArray(dm_vals, coords=[z], dims=['z']) # nd dot 1d actual = da.dot(dm) expected_vals = np.tensordot(da_vals, dm_vals, [2, 0]) expected = DataArray(expected_vals, coords=[x, y], dims=['x', 'y']) self.assertDataArrayEqual(expected, actual) # all shared dims actual = da.dot(da) expected_vals = np.tensordot(da_vals, da_vals, axes=([0, 1, 2], [0, 1, 2])) expected = DataArray(expected_vals) self.assertDataArrayEqual(expected, actual) # multiple shared dims dm_vals = np.arange(20 * 5 * 4).reshape((20, 5, 4)) j = np.linspace(-3, 3, 20) dm = DataArray(dm_vals, coords=[j, y, z], dims=['j', 'y', 'z']) actual = da.dot(dm) expected_vals = np.tensordot(da_vals, dm_vals, axes=([1, 2], [1, 2])) expected = DataArray(expected_vals, coords=[x, j], dims=['x', 'j']) self.assertDataArrayEqual(expected, actual) with self.assertRaises(NotImplementedError): da.dot(dm.to_dataset(name='dm')) with self.assertRaises(TypeError): da.dot(dm.values) with self.assertRaisesRegexp(ValueError, 'no shared dimensions'): da.dot(DataArray(1)) def test_binary_op_join_setting(self): dim = 'x' align_type = "outer" coords_l, coords_r = [0, 1, 2], [1, 2, 3] missing_3 = xr.DataArray(coords_l, [(dim, coords_l)]) missing_0 = xr.DataArray(coords_r, [(dim, coords_r)]) with xr.set_options(arithmetic_join=align_type): actual = missing_0 + missing_3 missing_0_aligned, missing_3_aligned = xr.align(missing_0, missing_3, join=align_type) expected = xr.DataArray([np.nan, 2, 4, np.nan], [(dim, [0, 1, 2, 3])]) self.assertDataArrayEqual(actual, expected) def test_combine_first(self): ar0 = DataArray([[0, 0], [0, 0]], [('x', ['a', 'b']), ('y', [-1, 0])]) ar1 = DataArray([[1, 1], [1, 1]], [('x', ['b', 'c']), ('y', [0, 1])]) ar2 = DataArray([2], [('x', ['d'])]) actual = ar0.combine_first(ar1) expected = DataArray([[0, 0, np.nan], [0, 0, 1], [np.nan, 1, 1]], [('x', ['a', 'b', 'c']), ('y', [-1, 0, 1])]) self.assertDataArrayEqual(actual, expected) actual = ar1.combine_first(ar0) expected = DataArray([[0, 0, np.nan], [0, 1, 1], [np.nan, 1, 1]], [('x', ['a', 'b', 'c']), ('y', [-1, 0, 1])]) self.assertDataArrayEqual(actual, expected) actual = ar0.combine_first(ar2) expected = DataArray([[0, 0], [0, 0], [2, 2]], [('x', ['a', 'b', 'd']), ('y', [-1, 0])]) self.assertDataArrayEqual(actual, expected) @pytest.fixture(params=[1]) def da(request): if request.param == 1: times = pd.date_range('2000-01-01', freq='1D', periods=21) values = np.random.random((21, 4)) da = DataArray(values, dims=('time', 'x')) da['time'] = times return da if request.param == 2: return DataArray([0, np.nan, 1, 2, np.nan, 3, 4, 5, np.nan, 6, 7], dims='time') def test_rolling_iter(da): rolling_obj = da.rolling(time=7) assert len(rolling_obj.window_labels) == len(da['time']) assert_identical(rolling_obj.window_labels, da['time']) for i, (label, window_da) in enumerate(rolling_obj): assert label == da['time'].isel(time=i) def test_rolling_properties(da): pytest.importorskip('bottleneck', minversion='1.0') rolling_obj = da.rolling(time=4) assert rolling_obj._axis_num == 0 # catching invalid args with pytest.raises(ValueError) as exception: da.rolling(time=7, x=2) assert 'exactly one dim/window should' in str(exception) with pytest.raises(ValueError) as exception: da.rolling(time=-2) assert 'window must be > 0' in str(exception) with pytest.raises(ValueError) as exception: da.rolling(time=2, min_periods=0) assert 'min_periods must be greater than zero' in str(exception) @pytest.mark.parametrize('name', ('sum', 'mean', 'std', 'min', 'max', 'median')) @pytest.mark.parametrize('center', (True, False, None)) @pytest.mark.parametrize('min_periods', (1, None)) def test_rolling_wrapped_bottleneck(da, name, center, min_periods): pytest.importorskip('bottleneck') import bottleneck as bn # skip if median and min_periods if (min_periods == 1) and (name == 'median'): pytest.skip() # Test all bottleneck functions rolling_obj = da.rolling(time=7, min_periods=min_periods) func_name = 'move_{0}'.format(name) actual = getattr(rolling_obj, name)() expected = getattr(bn, func_name)(da.values, window=7, axis=0, min_count=min_periods) assert_array_equal(actual.values, expected) # Test center rolling_obj = da.rolling(time=7, center=center) actual = getattr(rolling_obj, name)()['time'] assert_equal(actual, da['time']) def test_rolling_invalid_args(da): pytest.importorskip('bottleneck') with pytest.raises(ValueError) as exception: da.rolling(time=7, min_periods=1).median() assert 'Rolling.median does not' in str(exception) @pytest.mark.parametrize('center', (True, False)) @pytest.mark.parametrize('min_periods', (None, 1, 2, 3)) @pytest.mark.parametrize('window', (1, 2, 3, 4)) def test_rolling_pandas_compat(da, center, window, min_periods): s = pd.Series(range(10)) da = DataArray.from_series(s) if min_periods is not None and window < min_periods: min_periods = window s_rolling = pd.rolling_mean(s, window, center=center, min_periods=min_periods) da_rolling = da.rolling(index=window, center=center, min_periods=min_periods).mean() # pandas does some fancy stuff in the last position, # we're not going to do that yet! np.testing.assert_allclose(s_rolling.values[:-1], da_rolling.values[:-1]) np.testing.assert_allclose(s_rolling.index, da_rolling['index']) @pytest.mark.parametrize('da', (1, 2), indirect=True) @pytest.mark.parametrize('center', (True, False)) @pytest.mark.parametrize('min_periods', (None, 1, 2, 3)) @pytest.mark.parametrize('window', (1, 2, 3, 4)) @pytest.mark.parametrize('name', ('sum', 'mean', 'std', 'max')) def test_rolling_reduce(da, center, min_periods, window, name): if min_periods is not None and window < min_periods: min_periods = window rolling_obj = da.rolling(time=window, center=center, min_periods=min_periods) # add nan prefix to numpy methods to get similar # behavior as bottleneck actual = rolling_obj.reduce(getattr(np, 'nan%s' % name)) expected = getattr(rolling_obj, name)() assert_allclose(actual, expected) ������������������������������������������������������������������������������������xarray-0.9.1/xarray/tests/test_dataset.py�����������������������������������������������������������0000644�1311115�0011610�00000406034�13043675344�020655� 0����������������������������������������������������������������������������������������������������ustar �shoyer��������������������������eng�����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������# -*- coding: utf-8 -*- from __future__ import absolute_import from __future__ import division from __future__ import print_function from copy import copy, deepcopy from textwrap import dedent try: import cPickle as pickle except ImportError: import pickle try: import dask.array as da except ImportError: pass from io import StringIO from distutils.version import LooseVersion import numpy as np import pandas as pd import xarray as xr import pytest from xarray import (align, broadcast, backends, Dataset, DataArray, Variable, IndexVariable, open_dataset, set_options, MergeError) from xarray.core import indexing, utils from xarray.core.pycompat import iteritems, OrderedDict, unicode_type from xarray.core.common import full_like from . import (TestCase, InaccessibleArray, UnexpectedDataAccess, requires_dask, source_ndarray) def create_test_data(seed=None): rs = np.random.RandomState(seed) _vars = {'var1': ['dim1', 'dim2'], 'var2': ['dim1', 'dim2'], 'var3': ['dim3', 'dim1']} _dims = {'dim1': 8, 'dim2': 9, 'dim3': 10} obj = Dataset() obj['time'] = ('time', pd.date_range('2000-01-01', periods=20)) obj['dim2'] = ('dim2', 0.5 * np.arange(_dims['dim2'])) obj['dim3'] = ('dim3', list('abcdefghij')) for v, dims in sorted(_vars.items()): data = rs.normal(size=tuple(_dims[d] for d in dims)) obj[v] = (dims, data, {'foo': 'variable'}) obj.coords['numbers'] = ('dim3', np.array([0, 1, 2, 0, 0, 1, 1, 2, 2, 3], dtype='int64')) assert all(obj.data.flags.writeable for obj in obj.values()) return obj def create_test_multiindex(): mindex = pd.MultiIndex.from_product([['a', 'b'], [1, 2]], names=('level_1', 'level_2')) return Dataset({}, {'x': mindex}) class InaccessibleVariableDataStore(backends.InMemoryDataStore): def __init__(self, writer=None): super(InaccessibleVariableDataStore, self).__init__(writer) self._indexvars = set() def store(self, variables, *args, **kwargs): super(InaccessibleVariableDataStore, self).store( variables, *args, **kwargs) for k, v in variables.items(): if isinstance(v, IndexVariable): self._indexvars.add(k) def get_variables(self): def lazy_inaccessible(k, v): if k in self._indexvars: return v data = indexing.LazilyIndexedArray(InaccessibleArray(v.values)) return Variable(v.dims, data, v.attrs) return dict((k, lazy_inaccessible(k, v)) for k, v in iteritems(self._variables)) class TestDataset(TestCase): def test_repr(self): data = create_test_data(seed=123) data.attrs['foo'] = 'bar' # need to insert str dtype at runtime to handle both Python 2 & 3 expected = dedent("""\ Dimensions: (dim1: 8, dim2: 9, dim3: 10, time: 20) Coordinates: * time (time) datetime64[ns] 2000-01-01 2000-01-02 2000-01-03 ... * dim2 (dim2) float64 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 * dim3 (dim3) %s 'a' 'b' 'c' 'd' 'e' 'f' 'g' 'h' 'i' 'j' numbers (dim3) int64 0 1 2 0 0 1 1 2 2 3 Dimensions without coordinates: dim1 Data variables: var1 (dim1, dim2) float64 -1.086 0.9973 0.283 -1.506 -0.5786 1.651 ... var2 (dim1, dim2) float64 1.162 -1.097 -2.123 1.04 -0.4034 -0.126 ... var3 (dim3, dim1) float64 0.5565 -0.2121 0.4563 1.545 -0.2397 0.1433 ... Attributes: foo: bar""") % data['dim3'].dtype actual = '\n'.join(x.rstrip() for x in repr(data).split('\n')) print(actual) self.assertEqual(expected, actual) with set_options(display_width=100): max_len = max(map(len, repr(data).split('\n'))) assert 90 < max_len < 100 expected = dedent("""\ Dimensions: () Data variables: *empty*""") actual = '\n'.join(x.rstrip() for x in repr(Dataset()).split('\n')) print(actual) self.assertEqual(expected, actual) # verify that ... doesn't appear for scalar coordinates data = Dataset({'foo': ('x', np.ones(10))}).mean() expected = dedent("""\ Dimensions: () Data variables: foo float64 1.0""") actual = '\n'.join(x.rstrip() for x in repr(data).split('\n')) print(actual) self.assertEqual(expected, actual) # verify long attributes are truncated data = Dataset(attrs={'foo': 'bar' * 1000}) self.assertTrue(len(repr(data)) < 1000) def test_repr_multiindex(self): data = create_test_multiindex() expected = dedent("""\ Dimensions: (x: 4) Coordinates: * x (x) MultiIndex - level_1 (x) object 'a' 'a' 'b' 'b' - level_2 (x) int64 1 2 1 2 Data variables: *empty*""") actual = '\n'.join(x.rstrip() for x in repr(data).split('\n')) print(actual) self.assertEqual(expected, actual) # verify that long level names are not truncated mindex = pd.MultiIndex.from_product( [['a', 'b'], [1, 2]], names=('a_quite_long_level_name', 'level_2')) data = Dataset({}, {'x': mindex}) expected = dedent("""\ Dimensions: (x: 4) Coordinates: * x (x) MultiIndex - a_quite_long_level_name (x) object 'a' 'a' 'b' 'b' - level_2 (x) int64 1 2 1 2 Data variables: *empty*""") actual = '\n'.join(x.rstrip() for x in repr(data).split('\n')) print(actual) self.assertEqual(expected, actual) def test_repr_period_index(self): data = create_test_data(seed=456) data.coords['time'] = pd.period_range('2000-01-01', periods=20, freq='B') # check that creating the repr doesn't raise an error #GH645 repr(data) def test_unicode_data(self): # regression test for GH834 data = Dataset({u'foø': [u'ba®']}, attrs={u'å': u'∑'}) repr(data) # should not raise expected = dedent(u"""\ Dimensions: (foø: 1) Coordinates: * foø (foø) = '0.17': with self.assertRaises(KeyError): data.sel_points(x=[2.5], y=[2.0], method='pad', tolerance=1e-3) def test_sel_method(self): data = create_test_data() if pd.__version__ >= '0.16': expected = data.sel(dim2=1) actual = data.sel(dim2=0.95, method='nearest') self.assertDatasetIdentical(expected, actual) if pd.__version__ >= '0.17': actual = data.sel(dim2=0.95, method='nearest', tolerance=1) self.assertDatasetIdentical(expected, actual) with self.assertRaises(KeyError): actual = data.sel(dim2=np.pi, method='nearest', tolerance=0) expected = data.sel(dim2=[1.5]) actual = data.sel(dim2=[1.45], method='backfill') self.assertDatasetIdentical(expected, actual) with self.assertRaisesRegexp(NotImplementedError, 'slice objects'): data.sel(dim2=slice(1, 3), method='ffill') with self.assertRaisesRegexp(TypeError, '``method``'): # this should not pass silently data.sel(data) # cannot pass method if there is no associated coordinate with self.assertRaisesRegexp(ValueError, 'cannot supply'): data.sel(dim1=0, method='nearest') def test_loc(self): data = create_test_data() expected = data.sel(dim3='a') actual = data.loc[dict(dim3='a')] self.assertDatasetIdentical(expected, actual) with self.assertRaisesRegexp(TypeError, 'can only lookup dict'): data.loc['a'] with self.assertRaises(TypeError): data.loc[dict(dim3='a')] = 0 def test_selection_multiindex(self): mindex = pd.MultiIndex.from_product([['a', 'b'], [1, 2], [-1, -2]], names=('one', 'two', 'three')) mdata = Dataset(data_vars={'var': ('x', range(8))}, coords={'x': mindex}) def test_sel(lab_indexer, pos_indexer, replaced_idx=False, renamed_dim=None): ds = mdata.sel(x=lab_indexer) expected_ds = mdata.isel(x=pos_indexer) if not replaced_idx: self.assertDatasetIdentical(ds, expected_ds) else: if renamed_dim: self.assertEqual(ds['var'].dims[0], renamed_dim) ds = ds.rename({renamed_dim: 'x'}) self.assertVariableIdentical(ds['var'].variable, expected_ds['var'].variable) self.assertVariableNotEqual(ds['x'], expected_ds['x']) test_sel(('a', 1, -1), 0) test_sel(('b', 2, -2), -1) test_sel(('a', 1), [0, 1], replaced_idx=True, renamed_dim='three') test_sel(('a',), range(4), replaced_idx=True) test_sel('a', range(4), replaced_idx=True) test_sel([('a', 1, -1), ('b', 2, -2)], [0, 7]) test_sel(slice('a', 'b'), range(8)) test_sel(slice(('a', 1), ('b', 1)), range(6)) test_sel({'one': 'a', 'two': 1, 'three': -1}, 0) test_sel({'one': 'a', 'two': 1}, [0, 1], replaced_idx=True, renamed_dim='three') test_sel({'one': 'a'}, range(4), replaced_idx=True) self.assertDatasetIdentical(mdata.loc[{'x': {'one': 'a'}}], mdata.sel(x={'one': 'a'})) self.assertDatasetIdentical(mdata.loc[{'x': 'a'}], mdata.sel(x='a')) self.assertDatasetIdentical(mdata.loc[{'x': ('a', 1)}], mdata.sel(x=('a', 1))) self.assertDatasetIdentical(mdata.loc[{'x': ('a', 1, -1)}], mdata.sel(x=('a', 1, -1))) self.assertDatasetIdentical(mdata.sel(x={'one': 'a', 'two': 1}), mdata.sel(one='a', two=1)) def test_reindex_like(self): data = create_test_data() data['letters'] = ('dim3', 10 * ['a']) expected = data.isel(dim1=slice(10), time=slice(13)) actual = data.reindex_like(expected) self.assertDatasetIdentical(actual, expected) expected = data.copy(deep=True) expected['dim3'] = ('dim3', list('cdefghijkl')) expected['var3'][:-2] = expected['var3'][2:] expected['var3'][-2:] = np.nan expected['letters'] = expected['letters'].astype(object) expected['letters'][-2:] = np.nan expected['numbers'] = expected['numbers'].astype(float) expected['numbers'][:-2] = expected['numbers'][2:].values expected['numbers'][-2:] = np.nan actual = data.reindex_like(expected) self.assertDatasetIdentical(actual, expected) def test_reindex(self): data = create_test_data() self.assertDatasetIdentical(data, data.reindex()) expected = data.assign_coords(dim1=data['dim1']) actual = data.reindex(dim1=data['dim1']) self.assertDatasetIdentical(actual, expected) actual = data.reindex(dim1=data['dim1'].values) self.assertDatasetIdentical(actual, expected) actual = data.reindex(dim1=data['dim1'].to_index()) self.assertDatasetIdentical(actual, expected) with self.assertRaisesRegexp( ValueError, 'cannot reindex or align along dimension'): data.reindex(dim1=data['dim1'][:5]) expected = data.isel(dim2=slice(5)) actual = data.reindex(dim2=data['dim2'][:5]) self.assertDatasetIdentical(actual, expected) # test dict-like argument actual = data.reindex({'dim2': data['dim2']}) expected = data self.assertDatasetIdentical(actual, expected) with self.assertRaisesRegexp(ValueError, 'cannot specify both'): data.reindex({'x': 0}, x=0) with self.assertRaisesRegexp(ValueError, 'dictionary'): data.reindex('foo') # invalid dimension with self.assertRaisesRegexp(ValueError, 'invalid reindex dim'): data.reindex(invalid=0) # out of order expected = data.sel(dim2=data['dim2'][:5:-1]) actual = data.reindex(dim2=data['dim2'][:5:-1]) self.assertDatasetIdentical(actual, expected) # regression test for #279 expected = Dataset({'x': ('time', np.random.randn(5))}, {'time': range(5)}) time2 = DataArray(np.arange(5), dims="time2") actual = expected.reindex(time=time2) self.assertDatasetIdentical(actual, expected) # another regression test ds = Dataset({'foo': (['x', 'y'], np.zeros((3, 4)))}, {'x': range(3), 'y': range(4)}) expected = Dataset({'foo': (['x', 'y'], np.zeros((3, 2)))}, {'x': [0, 1, 3], 'y': [0, 1]}) expected['foo'][-1] = np.nan actual = ds.reindex(x=[0, 1, 3], y=[0, 1]) self.assertDatasetIdentical(expected, actual) def test_reindex_variables_copied(self): data = create_test_data() reindexed_data = data.reindex(copy=False) for k in data.variables: assert reindexed_data.variables[k] is not data.variables[k] def test_reindex_method(self): ds = Dataset({'x': ('y', [10, 20]), 'y': [0, 1]}) y = [-0.5, 0.5, 1.5] actual = ds.reindex(y=y, method='backfill') expected = Dataset({'x': ('y', [10, 20, np.nan]), 'y': y}) self.assertDatasetIdentical(expected, actual) if pd.__version__ >= '0.17': actual = ds.reindex(y=y, method='backfill', tolerance=0.1) expected = Dataset({'x': ('y', 3 * [np.nan]), 'y': y}) self.assertDatasetIdentical(expected, actual) else: with self.assertRaisesRegexp(TypeError, 'tolerance'): ds.reindex(y=y, method='backfill', tolerance=0.1) actual = ds.reindex(y=y, method='pad') expected = Dataset({'x': ('y', [np.nan, 10, 20]), 'y': y}) self.assertDatasetIdentical(expected, actual) alt = Dataset({'y': y}) actual = ds.reindex_like(alt, method='pad') self.assertDatasetIdentical(expected, actual) def test_align(self): left = create_test_data() right = left.copy(deep=True) right['dim3'] = ('dim3', list('cdefghijkl')) right['var3'][:-2] = right['var3'][2:] right['var3'][-2:] = np.random.randn(*right['var3'][-2:].shape) right['numbers'][:-2] = right['numbers'][2:] right['numbers'][-2:] = -10 intersection = list('cdefghij') union = list('abcdefghijkl') left2, right2 = align(left, right, join='inner') self.assertArrayEqual(left2['dim3'], intersection) self.assertDatasetIdentical(left2, right2) left2, right2 = align(left, right, join='outer') self.assertVariableEqual(left2['dim3'].variable, right2['dim3'].variable) self.assertArrayEqual(left2['dim3'], union) self.assertDatasetIdentical(left2.sel(dim3=intersection), right2.sel(dim3=intersection)) self.assertTrue(np.isnan(left2['var3'][-2:]).all()) self.assertTrue(np.isnan(right2['var3'][:2]).all()) left2, right2 = align(left, right, join='left') self.assertVariableEqual(left2['dim3'].variable, right2['dim3'].variable) self.assertVariableEqual(left2['dim3'].variable, left['dim3'].variable) self.assertDatasetIdentical(left2.sel(dim3=intersection), right2.sel(dim3=intersection)) self.assertTrue(np.isnan(right2['var3'][:2]).all()) left2, right2 = align(left, right, join='right') self.assertVariableEqual(left2['dim3'].variable, right2['dim3'].variable) self.assertVariableEqual(left2['dim3'].variable, right['dim3'].variable) self.assertDatasetIdentical(left2.sel(dim3=intersection), right2.sel(dim3=intersection)) self.assertTrue(np.isnan(left2['var3'][-2:]).all()) with self.assertRaisesRegexp(ValueError, 'invalid value for join'): align(left, right, join='foobar') with self.assertRaises(TypeError): align(left, right, foo='bar') def test_align_exclude(self): x = Dataset({'foo': DataArray([[1, 2], [3, 4]], dims=['x', 'y'], coords={'x': [1, 2], 'y': [3, 4]})}) y = Dataset({'bar': DataArray([[1, 2], [3, 4]], dims=['x', 'y'], coords={'x': [1, 3], 'y': [5, 6]})}) x2, y2 = align(x, y, exclude=['y'], join='outer') expected_x2 = Dataset( {'foo': DataArray([[1, 2], [3, 4], [np.nan, np.nan]], dims=['x', 'y'], coords={'x': [1, 2, 3], 'y': [3, 4]})}) expected_y2 = Dataset( {'bar': DataArray([[1, 2], [np.nan, np.nan], [3, 4]], dims=['x', 'y'], coords={'x': [1, 2, 3], 'y': [5, 6]})}) self.assertDatasetIdentical(expected_x2, x2) self.assertDatasetIdentical(expected_y2, y2) def test_align_nocopy(self): x = Dataset({'foo': DataArray([1, 2, 3], coords=[('x', [1, 2, 3])])}) y = Dataset({'foo': DataArray([1, 2], coords=[('x', [1, 2])])}) expected_x2 = x expected_y2 = Dataset({'foo': DataArray([1, 2, np.nan], coords=[('x', [1, 2, 3])])}) x2, y2 = align(x, y, copy=False, join='outer') self.assertDatasetIdentical(expected_x2, x2) self.assertDatasetIdentical(expected_y2, y2) assert source_ndarray(x['foo'].data) is source_ndarray(x2['foo'].data) x2, y2 = align(x, y, copy=True, join='outer') self.assertDatasetIdentical(expected_x2, x2) self.assertDatasetIdentical(expected_y2, y2) assert source_ndarray(x['foo'].data) is not source_ndarray(x2['foo'].data) def test_align_indexes(self): x = Dataset({'foo': DataArray([1, 2, 3], coords=[('x', [1, 2, 3])])}) x2, = align(x, indexes={'x': [2, 3, 1]}) expected_x2 = Dataset({'foo': DataArray([2, 3, 1], coords={'x': [2, 3, 1]})}) self.assertDatasetIdentical(expected_x2, x2) def test_align_non_unique(self): x = Dataset({'foo': ('x', [3, 4, 5]), 'x': [0, 0, 1]}) x1, x2 = align(x, x) assert x1.identical(x) and x2.identical(x) y = Dataset({'bar': ('x', [6, 7]), 'x': [0, 1]}) with self.assertRaisesRegexp(ValueError, 'cannot reindex or align'): align(x, y) def test_broadcast(self): ds = Dataset({'foo': 0, 'bar': ('x', [1]), 'baz': ('y', [2, 3])}, {'c': ('x', [4])}) expected = Dataset({'foo': (('x', 'y'), [[0, 0]]), 'bar': (('x', 'y'), [[1, 1]]), 'baz': (('x', 'y'), [[2, 3]])}, {'c': ('x', [4])}) actual, = broadcast(ds) self.assertDatasetIdentical(expected, actual) ds_x = Dataset({'foo': ('x', [1])}) ds_y = Dataset({'bar': ('y', [2, 3])}) expected_x = Dataset({'foo': (('x', 'y'), [[1, 1]])}) expected_y = Dataset({'bar': (('x', 'y'), [[2, 3]])}) actual_x, actual_y = broadcast(ds_x, ds_y) self.assertDatasetIdentical(expected_x, actual_x) self.assertDatasetIdentical(expected_y, actual_y) array_y = ds_y['bar'] expected_y = expected_y['bar'] actual_x, actual_y = broadcast(ds_x, array_y) self.assertDatasetIdentical(expected_x, actual_x) self.assertDataArrayIdentical(expected_y, actual_y) def test_broadcast_nocopy(self): # Test that data is not copied if not needed x = Dataset({'foo': (('x', 'y'), [[1, 1]])}) y = Dataset({'bar': ('y', [2, 3])}) actual_x, = broadcast(x) self.assertDatasetIdentical(x, actual_x) assert source_ndarray(actual_x['foo'].data) is source_ndarray(x['foo'].data) actual_x, actual_y = broadcast(x, y) self.assertDatasetIdentical(x, actual_x) assert source_ndarray(actual_x['foo'].data) is source_ndarray(x['foo'].data) def test_broadcast_exclude(self): x = Dataset({ 'foo': DataArray([[1, 2],[3, 4]], dims=['x', 'y'], coords={'x': [1, 2], 'y': [3, 4]}), 'bar': DataArray(5), }) y = Dataset({ 'foo': DataArray([[1, 2]], dims=['z', 'y'], coords={'z': [1], 'y': [5, 6]}), }) x2, y2 = broadcast(x, y, exclude=['y']) expected_x2 = Dataset({ 'foo': DataArray([[[1, 2]], [[3, 4]]], dims=['x', 'z', 'y'], coords={'z': [1], 'x': [1, 2], 'y': [3, 4]}), 'bar': DataArray([[5], [5]], dims=['x', 'z'], coords={'x': [1, 2], 'z': [1]}), }) expected_y2 = Dataset({ 'foo': DataArray([[[1, 2]], [[1, 2]]], dims=['x', 'z', 'y'], coords={'z': [1], 'x': [1, 2], 'y': [5, 6]}), }) self.assertDatasetIdentical(expected_x2, x2) self.assertDatasetIdentical(expected_y2, y2) def test_broadcast_misaligned(self): x = Dataset({'foo': DataArray([1, 2, 3], coords=[('x', [-1, -2, -3])])}) y = Dataset({'bar': DataArray([[1, 2], [3, 4]], dims=['y', 'x'], coords={'y': [1, 2], 'x': [10, -3]})}) x2, y2 = broadcast(x, y) expected_x2 = Dataset( {'foo': DataArray([[3, 3], [2, 2], [1, 1], [np.nan, np.nan]], dims=['x', 'y'], coords={'y': [1, 2], 'x': [-3, -2, -1, 10]})}) expected_y2 = Dataset( {'bar': DataArray( [[2, 4], [np.nan, np.nan], [np.nan, np.nan], [1, 3]], dims=['x', 'y'], coords={'y': [1, 2], 'x': [-3, -2, -1, 10]})}) self.assertDatasetIdentical(expected_x2, x2) self.assertDatasetIdentical(expected_y2, y2) def test_variable_indexing(self): data = create_test_data() v = data['var1'] d1 = data['dim1'] d2 = data['dim2'] self.assertVariableEqual(v, v[d1.values]) self.assertVariableEqual(v, v[d1]) self.assertVariableEqual(v[:3], v[d1 < 3]) self.assertVariableEqual(v[:, 3:], v[:, d2 >= 1.5]) self.assertVariableEqual(v[:3, 3:], v[d1 < 3, d2 >= 1.5]) self.assertVariableEqual(v[:3, :2], v[range(3), range(2)]) self.assertVariableEqual(v[:3, :2], v.loc[d1[:3], d2[:2]]) def test_drop_variables(self): data = create_test_data() self.assertDatasetIdentical(data, data.drop([])) expected = Dataset(dict((k, data[k]) for k in data if k != 'time')) actual = data.drop('time') self.assertDatasetIdentical(expected, actual) actual = data.drop(['time']) self.assertDatasetIdentical(expected, actual) with self.assertRaisesRegexp(ValueError, 'cannot be found'): data.drop('not_found_here') def test_drop_index_labels(self): data = Dataset({'A': (['x', 'y'], np.random.randn(2, 3)), 'x': ['a', 'b']}) actual = data.drop(['a'], 'x') expected = data.isel(x=[1]) self.assertDatasetIdentical(expected, actual) actual = data.drop(['a', 'b'], 'x') expected = data.isel(x=slice(0, 0)) self.assertDatasetIdentical(expected, actual) with self.assertRaises(ValueError): # not contained in axis data.drop(['c'], dim='x') with self.assertRaisesRegexp( ValueError, 'does not have coordinate labels'): data.drop(1, 'y') def test_copy(self): data = create_test_data() for copied in [data.copy(deep=False), copy(data)]: self.assertDatasetIdentical(data, copied) # Note: IndexVariable objects with string dtype are always # copied because of xarray.core.util.safe_cast_to_index. # Limiting the test to data variables. for k in data.data_vars: v0 = data.variables[k] v1 = copied.variables[k] assert source_ndarray(v0.data) is source_ndarray(v1.data) copied['foo'] = ('z', np.arange(5)) self.assertNotIn('foo', data) for copied in [data.copy(deep=True), deepcopy(data)]: self.assertDatasetIdentical(data, copied) for k in data: v0 = data.variables[k] v1 = copied.variables[k] self.assertIsNot(v0, v1) def test_rename(self): data = create_test_data() newnames = {'var1': 'renamed_var1', 'dim2': 'renamed_dim2'} renamed = data.rename(newnames) variables = OrderedDict(data.variables) for k, v in iteritems(newnames): variables[v] = variables.pop(k) for k, v in iteritems(variables): dims = list(v.dims) for name, newname in iteritems(newnames): if name in dims: dims[dims.index(name)] = newname self.assertVariableEqual(Variable(dims, v.values, v.attrs), renamed[k].variable.to_base_variable()) self.assertEqual(v.encoding, renamed[k].encoding) self.assertEqual(type(v), type(renamed.variables[k])) self.assertTrue('var1' not in renamed) self.assertTrue('dim2' not in renamed) with self.assertRaisesRegexp(ValueError, "cannot rename 'not_a_var'"): data.rename({'not_a_var': 'nada'}) with self.assertRaisesRegexp(ValueError, "'var1' already exists"): data.rename({'var2': 'var1'}) # verify that we can rename a variable without accessing the data var1 = data['var1'] data['var1'] = (var1.dims, InaccessibleArray(var1.values)) renamed = data.rename(newnames) with self.assertRaises(UnexpectedDataAccess): renamed['renamed_var1'].values def test_rename_same_name(self): data = create_test_data() newnames = {'var1': 'var1', 'dim2': 'dim2'} renamed = data.rename(newnames) self.assertDatasetIdentical(renamed, data) def test_rename_inplace(self): times = pd.date_range('2000-01-01', periods=3) data = Dataset({'z': ('x', [2, 3, 4]), 't': ('t', times)}) copied = data.copy() renamed = data.rename({'x': 'y'}) data.rename({'x': 'y'}, inplace=True) self.assertDatasetIdentical(data, renamed) self.assertFalse(data.equals(copied)) self.assertEquals(data.dims, {'y': 3, 't': 3}) # check virtual variables self.assertArrayEqual(data['t.dayofyear'], [1, 2, 3]) def test_swap_dims(self): original = Dataset({'x': [1, 2, 3], 'y': ('x', list('abc')), 'z': 42}) expected = Dataset({'z': 42}, {'x': ('y', [1, 2, 3]), 'y': list('abc')}) actual = original.swap_dims({'x': 'y'}) self.assertDatasetIdentical(expected, actual) self.assertIsInstance(actual.variables['y'], IndexVariable) self.assertIsInstance(actual.variables['x'], Variable) roundtripped = actual.swap_dims({'y': 'x'}) self.assertDatasetIdentical(original.set_coords('y'), roundtripped) actual = original.copy() actual.swap_dims({'x': 'y'}, inplace=True) self.assertDatasetIdentical(expected, actual) with self.assertRaisesRegexp(ValueError, 'cannot swap'): original.swap_dims({'y': 'x'}) with self.assertRaisesRegexp(ValueError, 'replacement dimension'): original.swap_dims({'x': 'z'}) def test_set_index(self): expected = create_test_multiindex() mindex = expected['x'].to_index() indexes = [mindex.get_level_values(n) for n in mindex.names] coords = {idx.name: ('x', idx) for idx in indexes} ds = Dataset({}, coords=coords) obj = ds.set_index(x=mindex.names) self.assertDatasetIdentical(obj, expected) ds.set_index(x=mindex.names, inplace=True) self.assertDatasetIdentical(ds, expected) def test_reset_index(self): ds = create_test_multiindex() mindex = ds['x'].to_index() indexes = [mindex.get_level_values(n) for n in mindex.names] coords = {idx.name: ('x', idx) for idx in indexes} expected = Dataset({}, coords=coords) obj = ds.reset_index('x') self.assertDatasetIdentical(obj, expected) ds.reset_index('x', inplace=True) self.assertDatasetIdentical(ds, expected) def test_reorder_levels(self): ds = create_test_multiindex() mindex = ds['x'].to_index() midx = mindex.reorder_levels(['level_2', 'level_1']) expected = Dataset({}, coords={'x': midx}) reindexed = ds.reorder_levels(x=['level_2', 'level_1']) self.assertDatasetIdentical(reindexed, expected) ds.reorder_levels(x=['level_2', 'level_1'], inplace=True) self.assertDatasetIdentical(ds, expected) ds = Dataset({}, coords={'x': [1, 2]}) with self.assertRaisesRegexp(ValueError, 'has no MultiIndex'): ds.reorder_levels(x=['level_1', 'level_2']) def test_stack(self): ds = Dataset({'a': ('x', [0, 1]), 'b': (('x', 'y'), [[0, 1], [2, 3]]), 'y': ['a', 'b']}) exp_index = pd.MultiIndex.from_product([[0, 1], ['a', 'b']], names=['x', 'y']) expected = Dataset({'a': ('z', [0, 0, 1, 1]), 'b': ('z', [0, 1, 2, 3]), 'z': exp_index}) actual = ds.stack(z=['x', 'y']) self.assertDatasetIdentical(expected, actual) exp_index = pd.MultiIndex.from_product([['a', 'b'], [0, 1]], names=['y', 'x']) expected = Dataset({'a': ('z', [0, 1, 0, 1]), 'b': ('z', [0, 2, 1, 3]), 'z': exp_index}) actual = ds.stack(z=['y', 'x']) self.assertDatasetIdentical(expected, actual) def test_unstack(self): index = pd.MultiIndex.from_product([[0, 1], ['a', 'b']], names=['x', 'y']) ds = Dataset({'b': ('z', [0, 1, 2, 3]), 'z': index}) expected = Dataset({'b': (('x', 'y'), [[0, 1], [2, 3]]), 'x': [0, 1], 'y': ['a', 'b']}) actual = ds.unstack('z') self.assertDatasetIdentical(actual, expected) def test_unstack_errors(self): ds = Dataset({'x': [1, 2, 3]}) with self.assertRaisesRegexp(ValueError, 'invalid dimension'): ds.unstack('foo') with self.assertRaisesRegexp(ValueError, 'does not have a MultiIndex'): ds.unstack('x') def test_stack_unstack(self): ds = Dataset({'a': ('x', [0, 1]), 'b': (('x', 'y'), [[0, 1], [2, 3]]), 'x': [0, 1], 'y': ['a', 'b']}) actual = ds.stack(z=['x', 'y']).unstack('z') assert actual.broadcast_equals(ds) actual = ds[['b']].stack(z=['x', 'y']).unstack('z') assert actual.identical(ds[['b']]) def test_update(self): data = create_test_data(seed=0) expected = data.copy() var2 = Variable('dim1', np.arange(8)) actual = data.update({'var2': var2}) expected['var2'] = var2 self.assertDatasetIdentical(expected, actual) actual = data.copy() actual_result = actual.update(data, inplace=True) self.assertIs(actual_result, actual) self.assertDatasetIdentical(expected, actual) actual = data.update(data, inplace=False) expected = data self.assertIsNot(actual, expected) self.assertDatasetIdentical(expected, actual) other = Dataset(attrs={'new': 'attr'}) actual = data.copy() actual.update(other) self.assertDatasetIdentical(expected, actual) def test_update_auto_align(self): ds = Dataset({'x': ('t', [3, 4])}, {'t': [0, 1]}) expected = Dataset({'x': ('t', [3, 4]), 'y': ('t', [np.nan, 5])}, {'t': [0, 1]}) actual = ds.copy() other = {'y': ('t', [5]), 't': [1]} with self.assertRaisesRegexp(ValueError, 'conflicting sizes'): actual.update(other) actual.update(Dataset(other)) self.assertDatasetIdentical(expected, actual) actual = ds.copy() other = Dataset({'y': ('t', [5]), 't': [100]}) actual.update(other) expected = Dataset({'x': ('t', [3, 4]), 'y': ('t', [np.nan] * 2)}, {'t': [0, 1]}) self.assertDatasetIdentical(expected, actual) def test_getitem(self): data = create_test_data() self.assertIsInstance(data['var1'], DataArray) self.assertVariableEqual(data['var1'].variable, data.variables['var1']) with self.assertRaises(KeyError): data['notfound'] with self.assertRaises(KeyError): data[['var1', 'notfound']] actual = data[['var1', 'var2']] expected = Dataset({'var1': data['var1'], 'var2': data['var2']}) self.assertDatasetEqual(expected, actual) actual = data['numbers'] expected = DataArray(data['numbers'].variable, {'dim3': data['dim3'], 'numbers': data['numbers']}, dims='dim3', name='numbers') self.assertDataArrayIdentical(expected, actual) actual = data[dict(dim1=0)] expected = data.isel(dim1=0) self.assertDatasetIdentical(expected, actual) def test_getitem_hashable(self): data = create_test_data() data[(3, 4)] = data['var1'] + 1 expected = data['var1'] + 1 expected.name = (3, 4) self.assertDataArrayIdentical(expected, data[(3, 4)]) with self.assertRaisesRegexp(KeyError, "('var1', 'var2')"): data[('var1', 'var2')] def test_virtual_variables_default_coords(self): dataset = Dataset({'foo': ('x', range(10))}) expected = DataArray(range(10), dims='x', name='x') actual = dataset['x'] self.assertDataArrayIdentical(expected, actual) self.assertIsInstance(actual.variable, IndexVariable) actual = dataset[['x', 'foo']] expected = dataset.assign_coords(x=range(10)) self.assertDatasetIdentical(expected, actual) def test_virtual_variables_time(self): # access virtual variables data = create_test_data() expected = DataArray(1 + np.arange(20), coords=[data['time']], dims='time', name='dayofyear') self.assertDataArrayIdentical(expected, data['time.dayofyear']) self.assertArrayEqual(data['time.month'].values, data.variables['time'].to_index().month) self.assertArrayEqual(data['time.season'].values, 'DJF') # test virtual variable math self.assertArrayEqual(data['time.dayofyear'] + 1, 2 + np.arange(20)) self.assertArrayEqual(np.sin(data['time.dayofyear']), np.sin(1 + np.arange(20))) # ensure they become coordinates expected = Dataset({}, {'dayofyear': data['time.dayofyear']}) actual = data[['time.dayofyear']] self.assertDatasetEqual(expected, actual) # non-coordinate variables ds = Dataset({'t': ('x', pd.date_range('2000-01-01', periods=3))}) self.assertTrue((ds['t.year'] == 2000).all()) def test_virtual_variable_same_name(self): # regression test for GH367 times = pd.date_range('2000-01-01', freq='H', periods=5) data = Dataset({'time': times}) actual = data['time.time'] expected = DataArray(times.time, [('time', times)], name='time') self.assertDataArrayIdentical(actual, expected) def test_virtual_variable_multiindex(self): # access multi-index levels as virtual variables data = create_test_multiindex() expected = DataArray(['a', 'a', 'b', 'b'], name='level_1', coords=[data['x'].to_index()], dims='x') self.assertDataArrayIdentical(expected, data['level_1']) # combine multi-index level and datetime dr_index = pd.date_range('1/1/2011', periods=4, freq='H') mindex = pd.MultiIndex.from_arrays([['a', 'a', 'b', 'b'], dr_index], names=('level_str', 'level_date')) data = Dataset({}, {'x': mindex}) expected = DataArray(mindex.get_level_values('level_date').hour, name='hour', coords=[mindex], dims='x') self.assertDataArrayIdentical(expected, data['level_date.hour']) # attribute style access self.assertDataArrayIdentical(data.level_str, data['level_str']) def test_time_season(self): ds = Dataset({'t': pd.date_range('2000-01-01', periods=12, freq='M')}) expected = ['DJF'] * 2 + ['MAM'] * 3 + ['JJA'] * 3 + ['SON'] * 3 + ['DJF'] self.assertArrayEqual(expected, ds['t.season']) def test_slice_virtual_variable(self): data = create_test_data() self.assertVariableEqual(data['time.dayofyear'][:10].variable, Variable(['time'], 1 + np.arange(10))) self.assertVariableEqual(data['time.dayofyear'][0].variable, Variable([], 1)) def test_setitem(self): # assign a variable var = Variable(['dim1'], np.random.randn(8)) data1 = create_test_data() data1['A'] = var data2 = data1.copy() data2['A'] = var self.assertDatasetIdentical(data1, data2) # assign a dataset array dv = 2 * data2['A'] data1['B'] = dv.variable data2['B'] = dv self.assertDatasetIdentical(data1, data2) # can't assign an ND array without dimensions with self.assertRaisesRegexp(ValueError, 'without explicit dimension names'): data2['C'] = var.values.reshape(2, 4) # but can assign a 1D array data1['C'] = var.values data2['C'] = ('C', var.values) self.assertDatasetIdentical(data1, data2) # can assign a scalar data1['scalar'] = 0 data2['scalar'] = ([], 0) self.assertDatasetIdentical(data1, data2) # can't use the same dimension name as a scalar var with self.assertRaisesRegexp(ValueError, 'already exists as a scalar'): data1['newvar'] = ('scalar', [3, 4, 5]) # can't resize a used dimension with self.assertRaisesRegexp(ValueError, 'arguments without labels'): data1['dim1'] = data1['dim1'][:5] # override an existing value data1['A'] = 3 * data2['A'] self.assertVariableEqual(data1['A'], 3 * data2['A']) with self.assertRaises(NotImplementedError): data1[{'x': 0}] = 0 def test_setitem_pandas(self): ds = self.make_example_math_dataset() ds['x'] = np.arange(3) ds_copy = ds.copy() ds_copy['bar'] = ds['bar'].to_pandas() self.assertDatasetEqual(ds, ds_copy) def test_setitem_auto_align(self): ds = Dataset() ds['x'] = ('y', range(3)) ds['y'] = 1 + np.arange(3) expected = Dataset({'x': ('y', range(3)), 'y': 1 + np.arange(3)}) self.assertDatasetIdentical(ds, expected) ds['y'] = DataArray(range(3), dims='y') expected = Dataset({'x': ('y', range(3))}, {'y': range(3)}) self.assertDatasetIdentical(ds, expected) ds['x'] = DataArray([1, 2], coords=[('y', [0, 1])]) expected = Dataset({'x': ('y', [1, 2, np.nan])}, {'y': range(3)}) self.assertDatasetIdentical(ds, expected) ds['x'] = 42 expected = Dataset({'x': 42, 'y': range(3)}) self.assertDatasetIdentical(ds, expected) ds['x'] = DataArray([4, 5, 6, 7], coords=[('y', [0, 1, 2, 3])]) expected = Dataset({'x': ('y', [4, 5, 6])}, {'y': range(3)}) self.assertDatasetIdentical(ds, expected) def test_setitem_align_new_indexes(self): ds = Dataset({'foo': ('x', [1, 2, 3])}, {'x': [0, 1, 2]}) ds['bar'] = DataArray([2, 3, 4], [('x', [1, 2, 3])]) expected = Dataset({'foo': ('x', [1, 2, 3]), 'bar': ('x', [np.nan, 2, 3])}, {'x': [0, 1, 2]}) self.assertDatasetIdentical(ds, expected) def test_assign(self): ds = Dataset() actual = ds.assign(x = [0, 1, 2], y = 2) expected = Dataset({'x': [0, 1, 2], 'y': 2}) self.assertDatasetIdentical(actual, expected) self.assertEqual(list(actual), ['x', 'y']) self.assertDatasetIdentical(ds, Dataset()) actual = actual.assign(y = lambda ds: ds.x ** 2) expected = Dataset({'y': ('x', [0, 1, 4]), 'x': [0, 1, 2]}) self.assertDatasetIdentical(actual, expected) actual = actual.assign_coords(z = 2) expected = Dataset({'y': ('x', [0, 1, 4])}, {'z': 2, 'x': [0, 1, 2]}) self.assertDatasetIdentical(actual, expected) ds = Dataset({'a': ('x', range(3))}, {'b': ('x', ['A'] * 2 + ['B'])}) actual = ds.groupby('b').assign(c = lambda ds: 2 * ds.a) expected = ds.merge({'c': ('x', [0, 2, 4])}) self.assertDatasetIdentical(actual, expected) actual = ds.groupby('b').assign(c = lambda ds: ds.a.sum()) expected = ds.merge({'c': ('x', [1, 1, 2])}) self.assertDatasetIdentical(actual, expected) actual = ds.groupby('b').assign_coords(c = lambda ds: ds.a.sum()) expected = expected.set_coords('c') self.assertDatasetIdentical(actual, expected) def test_assign_multiindex_level(self): data = create_test_multiindex() with self.assertRaisesRegexp(ValueError, 'conflicting MultiIndex'): data.assign(level_1=range(4)) data.assign_coords(level_1=range(4)) def test_setitem_original_non_unique_index(self): # regression test for GH943 original = Dataset({'data': ('x', np.arange(5))}, coords={'x': [0, 1, 2, 0, 1]}) expected = Dataset({'data': ('x', np.arange(5))}, {'x': range(5)}) actual = original.copy() actual['x'] = list(range(5)) self.assertDatasetIdentical(actual, expected) actual = original.copy() actual['x'] = ('x', list(range(5))) self.assertDatasetIdentical(actual, expected) actual = original.copy() actual.coords['x'] = list(range(5)) self.assertDatasetIdentical(actual, expected) def test_setitem_both_non_unique_index(self): # regression test for GH956 names = ['joaquin', 'manolo', 'joaquin'] values = np.random.randint(0, 256, (3, 4, 4)) array = DataArray(values, dims=['name', 'row', 'column'], coords=[names, range(4), range(4)]) expected = Dataset({'first': array, 'second': array}) actual = array.rename('first').to_dataset() actual['second'] = array self.assertDatasetIdentical(expected, actual) def test_setitem_multiindex_level(self): data = create_test_multiindex() with self.assertRaisesRegexp(ValueError, 'conflicting MultiIndex'): data['level_1'] = range(4) def test_delitem(self): data = create_test_data() all_items = set(data) self.assertItemsEqual(data, all_items) del data['var1'] self.assertItemsEqual(data, all_items - set(['var1'])) del data['numbers'] self.assertItemsEqual(data, all_items - set(['var1', 'numbers'])) self.assertNotIn('numbers', data.coords) def test_squeeze(self): data = Dataset({'foo': (['x', 'y', 'z'], [[[1], [2]]])}) for args in [[], [['x']], [['x', 'z']]]: def get_args(v): return [set(args[0]) & set(v.dims)] if args else [] expected = Dataset(dict((k, v.squeeze(*get_args(v))) for k, v in iteritems(data.variables))) expected.set_coords(data.coords, inplace=True) self.assertDatasetIdentical(expected, data.squeeze(*args)) # invalid squeeze with self.assertRaisesRegexp(ValueError, 'cannot select a dimension'): data.squeeze('y') def test_squeeze_drop(self): data = Dataset({'foo': ('x', [1])}, {'x': [0]}) expected = Dataset({'foo': 1}) selected = data.squeeze(drop=True) self.assertDatasetIdentical(expected, selected) expected = Dataset({'foo': 1}, {'x': 0}) selected = data.squeeze(drop=False) self.assertDatasetIdentical(expected, selected) data = Dataset({'foo': (('x', 'y'), [[1]])}, {'x': [0], 'y': [0]}) expected = Dataset({'foo': 1}) selected = data.squeeze(drop=True) self.assertDatasetIdentical(expected, selected) expected = Dataset({'foo': ('x', [1])}, {'x': [0]}) selected = data.squeeze(dim='y', drop=True) self.assertDatasetIdentical(expected, selected) data = Dataset({'foo': (('x',), [])}, {'x': []}) selected = data.squeeze(drop=True) self.assertDatasetIdentical(data, selected) def test_groupby(self): data = Dataset({'z': (['x', 'y'], np.random.randn(3, 5))}, {'x': ('x', list('abc')), 'c': ('x', [0, 1, 0]), 'y': range(5)}) groupby = data.groupby('x') self.assertEqual(len(groupby), 3) expected_groups = {'a': 0, 'b': 1, 'c': 2} self.assertEqual(groupby.groups, expected_groups) expected_items = [('a', data.isel(x=0)), ('b', data.isel(x=1)), ('c', data.isel(x=2))] for actual, expected in zip(groupby, expected_items): self.assertEqual(actual[0], expected[0]) self.assertDatasetEqual(actual[1], expected[1]) identity = lambda x: x for k in ['x', 'c', 'y']: actual = data.groupby(k, squeeze=False).apply(identity) self.assertDatasetEqual(data, actual) def test_groupby_returns_new_type(self): data = Dataset({'z': (['x', 'y'], np.random.randn(3, 5))}) actual = data.groupby('x').apply(lambda ds: ds['z']) expected = data['z'] self.assertDataArrayIdentical(expected, actual) actual = data['z'].groupby('x').apply(lambda x: x.to_dataset()) expected = data self.assertDatasetIdentical(expected, actual) def test_groupby_iter(self): data = create_test_data() for n, (t, sub) in enumerate(list(data.groupby('dim1'))[:3]): self.assertEqual(data['dim1'][n], t) self.assertVariableEqual(data['var1'][n], sub['var1']) self.assertVariableEqual(data['var2'][n], sub['var2']) self.assertVariableEqual(data['var3'][:, n], sub['var3']) def test_groupby_errors(self): data = create_test_data() with self.assertRaisesRegexp(TypeError, '`group` must be'): data.groupby(np.arange(10)) with self.assertRaisesRegexp(ValueError, 'length does not match'): data.groupby(data['dim1'][:3]) with self.assertRaisesRegexp(TypeError, "`group` must be"): data.groupby(data.coords['dim1'].to_index()) def test_groupby_reduce(self): data = Dataset({'xy': (['x', 'y'], np.random.randn(3, 4)), 'xonly': ('x', np.random.randn(3)), 'yonly': ('y', np.random.randn(4)), 'letters': ('y', ['a', 'a', 'b', 'b'])}) expected = data.mean('y') expected['yonly'] = expected['yonly'].variable.expand_dims({'x': 3}) actual = data.groupby('x').mean() self.assertDatasetAllClose(expected, actual) actual = data.groupby('x').mean('y') self.assertDatasetAllClose(expected, actual) letters = data['letters'] expected = Dataset({'xy': data['xy'].groupby(letters).mean(), 'xonly': (data['xonly'].mean().variable .expand_dims({'letters': 2})), 'yonly': data['yonly'].groupby(letters).mean()}) actual = data.groupby('letters').mean() self.assertDatasetAllClose(expected, actual) def test_groupby_math(self): reorder_dims = lambda x: x.transpose('dim1', 'dim2', 'dim3', 'time') ds = create_test_data() ds['dim1'] = ds['dim1'] for squeeze in [True, False]: grouped = ds.groupby('dim1', squeeze=squeeze) expected = reorder_dims(ds + ds.coords['dim1']) actual = grouped + ds.coords['dim1'] self.assertDatasetIdentical(expected, reorder_dims(actual)) actual = ds.coords['dim1'] + grouped self.assertDatasetIdentical(expected, reorder_dims(actual)) ds2 = 2 * ds expected = reorder_dims(ds + ds2) actual = grouped + ds2 self.assertDatasetIdentical(expected, reorder_dims(actual)) actual = ds2 + grouped self.assertDatasetIdentical(expected, reorder_dims(actual)) grouped = ds.groupby('numbers') zeros = DataArray([0, 0, 0, 0], [('numbers', range(4))]) expected = ((ds + Variable('dim3', np.zeros(10))) .transpose('dim3', 'dim1', 'dim2', 'time')) actual = grouped + zeros self.assertDatasetEqual(expected, actual) actual = zeros + grouped self.assertDatasetEqual(expected, actual) with self.assertRaisesRegexp(ValueError, 'incompat.* grouped binary'): grouped + ds with self.assertRaisesRegexp(ValueError, 'incompat.* grouped binary'): ds + grouped with self.assertRaisesRegexp(TypeError, 'only support binary ops'): grouped + 1 with self.assertRaisesRegexp(TypeError, 'only support binary ops'): grouped + grouped with self.assertRaisesRegexp(TypeError, 'in-place operations'): ds += grouped ds = Dataset({'x': ('time', np.arange(100)), 'time': pd.date_range('2000-01-01', periods=100)}) with self.assertRaisesRegexp(ValueError, 'incompat.* grouped binary'): ds + ds.groupby('time.month') def test_groupby_math_virtual(self): ds = Dataset({'x': ('t', [1, 2, 3])}, {'t': pd.date_range('20100101', periods=3)}) grouped = ds.groupby('t.day') actual = grouped - grouped.mean() expected = Dataset({'x': ('t', [0, 0, 0])}, ds[['t', 't.day']]) self.assertDatasetIdentical(actual, expected) def test_groupby_nan(self): # nan should be excluded from groupby ds = Dataset({'foo': ('x', [1, 2, 3, 4])}, {'bar': ('x', [1, 1, 2, np.nan])}) actual = ds.groupby('bar').mean() expected = Dataset({'foo': ('bar', [1.5, 3]), 'bar': [1, 2]}) self.assertDatasetIdentical(actual, expected) def test_groupby_order(self): # groupby should preserve variables order ds = Dataset() for vn in ['a', 'b', 'c']: ds[vn] = DataArray(np.arange(10), dims=['t']) all_vars_ref = list(ds.variables.keys()) data_vars_ref = list(ds.data_vars.keys()) ds = ds.groupby('t').mean() all_vars = list(ds.variables.keys()) data_vars = list(ds.data_vars.keys()) self.assertEqual(data_vars, data_vars_ref) # coords are now at the end of the list, so the test below fails # self.assertEqual(all_vars, all_vars_ref) def test_resample_and_first(self): times = pd.date_range('2000-01-01', freq='6H', periods=10) ds = Dataset({'foo': (['time', 'x', 'y'], np.random.randn(10, 5, 3)), 'bar': ('time', np.random.randn(10), {'meta': 'data'}), 'time': times}) actual = ds.resample('1D', dim='time', how='first', keep_attrs=True) expected = ds.isel(time=[0, 4, 8]) self.assertDatasetIdentical(expected, actual) # upsampling expected_time = pd.date_range('2000-01-01', freq='3H', periods=19) expected = ds.reindex(time=expected_time) for how in ['mean', 'sum', 'first', 'last', np.mean]: actual = ds.resample('3H', 'time', how=how) self.assertDatasetEqual(expected, actual) def test_resample_by_mean_with_keep_attrs(self): times = pd.date_range('2000-01-01', freq='6H', periods=10) ds = Dataset({'foo': (['time', 'x', 'y'], np.random.randn(10, 5, 3)), 'bar': ('time', np.random.randn(10), {'meta': 'data'}), 'time': times}) ds.attrs['dsmeta'] = 'dsdata' resampled_ds = ds.resample('1D', dim='time', how='mean', keep_attrs=True) actual = resampled_ds['bar'].attrs expected = ds['bar'].attrs self.assertEqual(expected, actual) actual = resampled_ds.attrs expected = ds.attrs self.assertEqual(expected, actual) def test_resample_by_mean_discarding_attrs(self): times = pd.date_range('2000-01-01', freq='6H', periods=10) ds = Dataset({'foo': (['time', 'x', 'y'], np.random.randn(10, 5, 3)), 'bar': ('time', np.random.randn(10), {'meta': 'data'}), 'time': times}) ds.attrs['dsmeta'] = 'dsdata' resampled_ds = ds.resample('1D', dim='time', how='mean', keep_attrs=False) assert resampled_ds['bar'].attrs == {} assert resampled_ds.attrs == {} def test_resample_by_last_discarding_attrs(self): times = pd.date_range('2000-01-01', freq='6H', periods=10) ds = Dataset({'foo': (['time', 'x', 'y'], np.random.randn(10, 5, 3)), 'bar': ('time', np.random.randn(10), {'meta': 'data'}), 'time': times}) ds.attrs['dsmeta'] = 'dsdata' resampled_ds = ds.resample('1D', dim='time', how='last', keep_attrs=False) assert resampled_ds['bar'].attrs == {} assert resampled_ds.attrs == {} def test_to_array(self): ds = Dataset(OrderedDict([('a', 1), ('b', ('x', [1, 2, 3]))]), coords={'c': 42}, attrs={'Conventions': 'None'}) data = [[1, 1, 1], [1, 2, 3]] coords = {'c': 42, 'variable': ['a', 'b']} dims = ('variable', 'x') expected = DataArray(data, coords, dims, attrs=ds.attrs) actual = ds.to_array() self.assertDataArrayIdentical(expected, actual) actual = ds.to_array('abc', name='foo') expected = expected.rename({'variable': 'abc'}).rename('foo') self.assertDataArrayIdentical(expected, actual) def test_to_and_from_dataframe(self): x = np.random.randn(10) y = np.random.randn(10) t = list('abcdefghij') ds = Dataset(OrderedDict([('a', ('t', x)), ('b', ('t', y)), ('t', ('t', t))])) expected = pd.DataFrame(np.array([x, y]).T, columns=['a', 'b'], index=pd.Index(t, name='t')) actual = ds.to_dataframe() # use the .equals method to check all DataFrame metadata assert expected.equals(actual), (expected, actual) # verify coords are included actual = ds.set_coords('b').to_dataframe() assert expected.equals(actual), (expected, actual) # check roundtrip self.assertDatasetIdentical(ds, Dataset.from_dataframe(actual)) # test a case with a MultiIndex w = np.random.randn(2, 3) ds = Dataset({'w': (('x', 'y'), w)}) ds['y'] = ('y', list('abc')) exp_index = pd.MultiIndex.from_arrays( [[0, 0, 0, 1, 1, 1], ['a', 'b', 'c', 'a', 'b', 'c']], names=['x', 'y']) expected = pd.DataFrame(w.reshape(-1), columns=['w'], index=exp_index) actual = ds.to_dataframe() self.assertTrue(expected.equals(actual)) # check roundtrip self.assertDatasetIdentical(ds.assign_coords(x=[0, 1]), Dataset.from_dataframe(actual)) # check pathological cases df = pd.DataFrame([1]) actual = Dataset.from_dataframe(df) expected = Dataset({0: ('index', [1])}, {'index': [0]}) self.assertDatasetIdentical(expected, actual) df = pd.DataFrame() actual = Dataset.from_dataframe(df) expected = Dataset(coords={'index': []}) self.assertDatasetIdentical(expected, actual) # GH697 df = pd.DataFrame({'A' : []}) actual = Dataset.from_dataframe(df) expected = Dataset({'A': DataArray([], dims=('index',))}, {'index': []}) self.assertDatasetIdentical(expected, actual) # regression test for GH278 # use int64 to ensure consistent results for the pandas .equals method # on windows (which requires the same dtype) ds = Dataset({'x': pd.Index(['bar']), 'a': ('y', np.array([1], 'int64'))}).isel(x=0) # use .loc to ensure consistent results on Python 3 actual = ds.to_dataframe().loc[:, ['a', 'x']] expected = pd.DataFrame([[1, 'bar']], index=pd.Index([0], name='y'), columns=['a', 'x']) assert expected.equals(actual), (expected, actual) ds = Dataset({'x': np.array([0], 'int64'), 'y': np.array([1], 'int64')}) actual = ds.to_dataframe() idx = pd.MultiIndex.from_arrays([[0], [1]], names=['x', 'y']) expected = pd.DataFrame([[]], index=idx) assert expected.equals(actual), (expected, actual) def test_from_dataframe_non_unique_columns(self): # regression test for GH449 df = pd.DataFrame(np.zeros((2, 2))) df.columns = ['foo', 'foo'] with self.assertRaisesRegexp(ValueError, 'non-unique columns'): Dataset.from_dataframe(df) def test_convert_dataframe_with_many_types_and_multiindex(self): # regression test for GH737 df = pd.DataFrame({'a': list('abc'), 'b': list(range(1, 4)), 'c': np.arange(3, 6).astype('u1'), 'd': np.arange(4.0, 7.0, dtype='float64'), 'e': [True, False, True], 'f': pd.Categorical(list('abc')), 'g': pd.date_range('20130101', periods=3), 'h': pd.date_range('20130101', periods=3, tz='US/Eastern')}) df.index = pd.MultiIndex.from_product([['a'], range(3)], names=['one', 'two']) roundtripped = Dataset.from_dataframe(df).to_dataframe() # we can't do perfectly, but we should be at least as faithful as # np.asarray expected = df.apply(np.asarray) if pd.__version__ < '0.17': # datetime with timezone dtype is not consistent on old pandas roundtripped = roundtripped.drop(['h'], axis=1) expected = expected.drop(['h'], axis=1) assert roundtripped.equals(expected) def test_to_and_from_dict(self): # # Dimensions: (t: 10) # Coordinates: # * t (t) 1) self.assertDatasetIdentical(expected, actual) actual = ds.where(ds.a > 1) self.assertDatasetIdentical(expected, actual) actual = ds.where(ds.a.values > 1) self.assertDatasetIdentical(expected, actual) actual = ds.where(True) self.assertDatasetIdentical(ds, actual) expected = ds.copy(deep=True) expected['a'].values = [np.nan] * 5 actual = ds.where(False) self.assertDatasetIdentical(expected, actual) # 2d ds = Dataset({'a': (('x', 'y'), [[0, 1], [2, 3]])}) expected = Dataset({'a': (('x', 'y'), [[np.nan, 1], [2, 3]])}) actual = ds.where(ds > 0) self.assertDatasetIdentical(expected, actual) # groupby ds = Dataset({'a': ('x', range(5))}, {'c': ('x', [0, 0, 1, 1, 1])}) cond = Dataset({'a': ('c', [True, False])}) expected = ds.copy(deep=True) expected['a'].values = [0, 1] + [np.nan] * 3 actual = ds.groupby('c').where(cond) self.assertDatasetIdentical(expected, actual) # attrs with groupby ds.attrs['attr'] = 'ds' ds.a.attrs['attr'] = 'da' actual = ds.groupby('c').where(cond) self.assertEqual(actual.attrs, ds.attrs) self.assertEqual(actual.a.name, 'a') self.assertEqual(actual.a.attrs, ds.a.attrs) # attrs da = DataArray(range(5), name='a', attrs={'attr':'da'}) actual = da.where(da.values > 1) self.assertEqual(actual.name, 'a') self.assertEqual(actual.attrs, da.attrs) ds = Dataset({'a': da}, attrs={'attr':'ds'}) actual = ds.where(ds > 0) self.assertEqual(actual.attrs, ds.attrs) self.assertEqual(actual.a.name, 'a') self.assertEqual(actual.a.attrs, ds.a.attrs) def test_where_drop(self): # if drop=True # 1d # data array case array = DataArray(range(5), coords=[range(5)], dims=['x']) expected = DataArray(range(5)[2:], coords=[range(5)[2:]], dims=['x']) actual = array.where(array > 1, drop=True) self.assertDatasetIdentical(expected, actual) # dataset case ds = Dataset({'a': array}) expected = Dataset({'a': expected}) actual = ds.where(ds > 1, drop=True) self.assertDatasetIdentical(expected, actual) actual = ds.where(ds.a > 1, drop=True) self.assertDatasetIdentical(expected, actual) with self.assertRaisesRegexp(TypeError, 'must be a'): ds.where(np.arange(5) > 1, drop=True) # 1d with odd coordinates array = DataArray(np.array([2, 7, 1, 8, 3]), coords=[np.array([3, 1, 4, 5, 9])], dims=['x']) expected = DataArray(np.array([7, 8, 3]), coords=[np.array([1, 5, 9])], dims=['x']) actual = array.where(array > 2, drop=True) self.assertDatasetIdentical(expected, actual) # 1d multiple variables ds = Dataset({'a': (('x'), [0, 1, 2, 3]), 'b': (('x'), [4, 5, 6, 7])}) expected = Dataset({'a': (('x'), [np.nan, 1, 2, 3]), 'b': (('x'), [4, 5, 6, np.nan])}) actual = ds.where((ds > 0) & (ds < 7), drop=True) self.assertDatasetIdentical(expected, actual) # 2d ds = Dataset({'a': (('x', 'y'), [[0, 1], [2, 3]])}) expected = Dataset({'a': (('x', 'y'), [[np.nan, 1], [2, 3]])}) actual = ds.where(ds > 0, drop=True) self.assertDatasetIdentical(expected, actual) # 2d with odd coordinates ds = Dataset({'a': (('x', 'y'), [[0, 1], [2, 3]])}, coords={'x': [4, 3], 'y': [1, 2], 'z' : (['x','y'], [[np.e, np.pi], [np.pi*np.e, np.pi*3]])}) expected = Dataset({'a': (('x', 'y'), [[3]])}, coords={'x': [3], 'y': [2], 'z' : (['x','y'], [[np.pi*3]])}) actual = ds.where(ds > 2, drop=True) self.assertDatasetIdentical(expected, actual) # 2d multiple variables ds = Dataset({'a': (('x', 'y'), [[0, 1], [2, 3]]), 'b': (('x','y'), [[4, 5], [6, 7]])}) expected = Dataset({'a': (('x', 'y'), [[np.nan, 1], [2, 3]]), 'b': (('x', 'y'), [[4, 5], [6,7]])}) actual = ds.where(ds > 0, drop=True) self.assertDatasetIdentical(expected, actual) def test_reduce(self): data = create_test_data() self.assertEqual(len(data.mean().coords), 0) actual = data.max() expected = Dataset(dict((k, v.max()) for k, v in iteritems(data.data_vars))) self.assertDatasetEqual(expected, actual) self.assertDatasetEqual(data.min(dim=['dim1']), data.min(dim='dim1')) for reduct, expected in [('dim2', ['dim1', 'dim3', 'time']), (['dim2', 'time'], ['dim1', 'dim3']), (('dim2', 'time'), ['dim1', 'dim3']), ((), ['dim1', 'dim2', 'dim3', 'time'])]: actual = data.min(dim=reduct).dims print(reduct, actual, expected) self.assertItemsEqual(actual, expected) self.assertDatasetEqual(data.mean(dim=[]), data) def test_reduce_bad_dim(self): data = create_test_data() with self.assertRaisesRegexp(ValueError, 'Dataset does not contain'): ds = data.mean(dim='bad_dim') def test_reduce_cumsum_test_dims(self): data = create_test_data() for cumfunc in ['cumsum', 'cumprod']: with self.assertRaisesRegexp(ValueError, "must supply either single 'dim' or 'axis'"): ds = getattr(data, cumfunc)() with self.assertRaisesRegexp(ValueError, "must supply either single 'dim' or 'axis'"): ds = getattr(data, cumfunc)(dim=['dim1', 'dim2']) with self.assertRaisesRegexp(ValueError, 'Dataset does not contain'): ds = getattr(data, cumfunc)(dim='bad_dim') # ensure dimensions are correct for reduct, expected in [('dim1', ['dim1', 'dim2', 'dim3', 'time']), ('dim2', ['dim1', 'dim2', 'dim3', 'time']), ('dim3', ['dim1', 'dim2', 'dim3', 'time']), ('time', ['dim1', 'dim2', 'dim3'])]: actual = getattr(data, cumfunc)(dim=reduct).dims print(reduct, actual, expected) self.assertItemsEqual(actual, expected) def test_reduce_non_numeric(self): data1 = create_test_data(seed=44) data2 = create_test_data(seed=44) add_vars = {'var4': ['dim1', 'dim2']} for v, dims in sorted(add_vars.items()): size = tuple(data1.dims[d] for d in dims) data = np.random.random_integers(0, 100, size=size).astype(np.str_) data1[v] = (dims, data, {'foo': 'variable'}) self.assertTrue('var4' not in data1.mean()) self.assertDatasetEqual(data1.mean(), data2.mean()) self.assertDatasetEqual(data1.mean(dim='dim1'), data2.mean(dim='dim1')) def test_reduce_strings(self): expected = Dataset({'x': 'a'}) ds = Dataset({'x': ('y', ['a', 'b'])}) actual = ds.min() self.assertDatasetIdentical(expected, actual) expected = Dataset({'x': 'b'}) actual = ds.max() self.assertDatasetIdentical(expected, actual) expected = Dataset({'x': 0}) actual = ds.argmin() self.assertDatasetIdentical(expected, actual) expected = Dataset({'x': 1}) actual = ds.argmax() self.assertDatasetIdentical(expected, actual) expected = Dataset({'x': b'a'}) ds = Dataset({'x': ('y', np.array(['a', 'b'], 'S1'))}) actual = ds.min() self.assertDatasetIdentical(expected, actual) expected = Dataset({'x': u'a'}) ds = Dataset({'x': ('y', np.array(['a', 'b'], 'U1'))}) actual = ds.min() self.assertDatasetIdentical(expected, actual) def test_reduce_dtypes(self): # regression test for GH342 expected = Dataset({'x': 1}) actual = Dataset({'x': True}).sum() self.assertDatasetIdentical(expected, actual) # regression test for GH505 expected = Dataset({'x': 3}) actual = Dataset({'x': ('y', np.array([1, 2], 'uint16'))}).sum() self.assertDatasetIdentical(expected, actual) expected = Dataset({'x': 1 + 1j}) actual = Dataset({'x': ('y', [1, 1j])}).sum() self.assertDatasetIdentical(expected, actual) def test_reduce_keep_attrs(self): data = create_test_data() _attrs = {'attr1': 'value1', 'attr2': 2929} attrs = OrderedDict(_attrs) data.attrs = attrs # Test dropped attrs ds = data.mean() self.assertEqual(ds.attrs, {}) for v in ds.data_vars.values(): self.assertEqual(v.attrs, {}) # Test kept attrs ds = data.mean(keep_attrs=True) self.assertEqual(ds.attrs, attrs) for k, v in ds.data_vars.items(): self.assertEqual(v.attrs, data[k].attrs) def test_reduce_argmin(self): # regression test for #205 ds = Dataset({'a': ('x', [0, 1])}) expected = Dataset({'a': ([], 0)}) actual = ds.argmin() self.assertDatasetIdentical(expected, actual) actual = ds.argmin('x') self.assertDatasetIdentical(expected, actual) def test_reduce_scalars(self): ds = Dataset({'x': ('a', [2, 2]), 'y': 2, 'z': ('b', [2])}) expected = Dataset({'x': 0, 'y': 0, 'z': 0}) actual = ds.var() self.assertDatasetIdentical(expected, actual) def test_reduce_only_one_axis(self): def mean_only_one_axis(x, axis): if not isinstance(axis, (int, np.integer)): raise TypeError('non-integer axis') return x.mean(axis) ds = Dataset({'a': (['x', 'y'], [[0, 1, 2, 3, 4]])}) expected = Dataset({'a': ('x', [2])}) actual = ds.reduce(mean_only_one_axis, 'y') self.assertDatasetIdentical(expected, actual) with self.assertRaisesRegexp(TypeError, 'non-integer axis'): ds.reduce(mean_only_one_axis) with self.assertRaisesRegexp(TypeError, 'non-integer axis'): ds.reduce(mean_only_one_axis, ['x', 'y']) @pytest.mark.skipif(LooseVersion(np.__version__) < LooseVersion('1.10.0'), reason='requires numpy version 1.10.0 or later') def test_quantile(self): ds = create_test_data(seed=123) for q in [0.25, [0.50], [0.25, 0.75]]: for dim in [None, 'dim1', ['dim1']]: ds_quantile = ds.quantile(q, dim=dim) assert 'quantile' in ds_quantile for var, dar in ds.data_vars.items(): assert var in ds_quantile self.assertDataArrayIdentical( ds_quantile[var], dar.quantile(q, dim=dim)) dim = ['dim1', 'dim2'] ds_quantile = ds.quantile(q, dim=dim) assert 'dim3' in ds_quantile.dims assert all(d not in ds_quantile.dims for d in dim) def test_count(self): ds = Dataset({'x': ('a', [np.nan, 1]), 'y': 0, 'z': np.nan}) expected = Dataset({'x': 1, 'y': 1, 'z': 0}) actual = ds.count() self.assertDatasetIdentical(expected, actual) def test_apply(self): data = create_test_data() data.attrs['foo'] = 'bar' self.assertDatasetIdentical(data.apply(np.mean), data.mean()) expected = data.mean(keep_attrs=True) actual = data.apply(lambda x: x.mean(keep_attrs=True), keep_attrs=True) self.assertDatasetIdentical(expected, actual) self.assertDatasetIdentical(data.apply(lambda x: x, keep_attrs=True), data.drop('time')) def scale(x, multiple=1): return multiple * x actual = data.apply(scale, multiple=2) self.assertDataArrayEqual(actual['var1'], 2 * data['var1']) self.assertDataArrayIdentical(actual['numbers'], data['numbers']) actual = data.apply(np.asarray) expected = data.drop('time') # time is not used on a data var self.assertDatasetEqual(expected, actual) def make_example_math_dataset(self): variables = OrderedDict( [('bar', ('x', np.arange(100, 400, 100))), ('foo', (('x', 'y'), 1.0 * np.arange(12).reshape(3, 4)))]) coords = {'abc': ('x', ['a', 'b', 'c']), 'y': 10 * np.arange(4)} ds = Dataset(variables, coords) ds['foo'][0, 0] = np.nan return ds def test_dataset_number_math(self): ds = self.make_example_math_dataset() self.assertDatasetIdentical(ds, +ds) self.assertDatasetIdentical(ds, ds + 0) self.assertDatasetIdentical(ds, 0 + ds) self.assertDatasetIdentical(ds, ds + np.array(0)) self.assertDatasetIdentical(ds, np.array(0) + ds) actual = ds.copy(deep=True) actual += 0 self.assertDatasetIdentical(ds, actual) def test_unary_ops(self): ds = self.make_example_math_dataset() self.assertDatasetIdentical(ds.apply(abs), abs(ds)) self.assertDatasetIdentical(ds.apply(lambda x: x + 4), ds + 4) for func in [lambda x: x.isnull(), lambda x: x.round(), lambda x: x.astype(int)]: self.assertDatasetIdentical(ds.apply(func), func(ds)) self.assertDatasetIdentical(ds.isnull(), ~ds.notnull()) # don't actually patch these methods in with self.assertRaises(AttributeError): ds.item with self.assertRaises(AttributeError): ds.searchsorted def test_dataset_array_math(self): ds = self.make_example_math_dataset() expected = ds.apply(lambda x: x - ds['foo']) self.assertDatasetIdentical(expected, ds - ds['foo']) self.assertDatasetIdentical(expected, -ds['foo'] + ds) self.assertDatasetIdentical(expected, ds - ds['foo'].variable) self.assertDatasetIdentical(expected, -ds['foo'].variable + ds) actual = ds.copy(deep=True) actual -= ds['foo'] self.assertDatasetIdentical(expected, actual) expected = ds.apply(lambda x: x + ds['bar']) self.assertDatasetIdentical(expected, ds + ds['bar']) actual = ds.copy(deep=True) actual += ds['bar'] self.assertDatasetIdentical(expected, actual) expected = Dataset({'bar': ds['bar'] + np.arange(3)}) self.assertDatasetIdentical(expected, ds[['bar']] + np.arange(3)) self.assertDatasetIdentical(expected, np.arange(3) + ds[['bar']]) def test_dataset_dataset_math(self): ds = self.make_example_math_dataset() self.assertDatasetIdentical(ds, ds + 0 * ds) self.assertDatasetIdentical(ds, ds + {'foo': 0, 'bar': 0}) expected = ds.apply(lambda x: 2 * x) self.assertDatasetIdentical(expected, 2 * ds) self.assertDatasetIdentical(expected, ds + ds) self.assertDatasetIdentical(expected, ds + ds.data_vars) self.assertDatasetIdentical(expected, ds + dict(ds.data_vars)) actual = ds.copy(deep=True) expected_id = id(actual) actual += ds self.assertDatasetIdentical(expected, actual) self.assertEqual(expected_id, id(actual)) self.assertDatasetIdentical(ds == ds, ds.notnull()) subsampled = ds.isel(y=slice(2)) expected = 2 * subsampled self.assertDatasetIdentical(expected, subsampled + ds) self.assertDatasetIdentical(expected, ds + subsampled) def test_dataset_math_auto_align(self): ds = self.make_example_math_dataset() subset = ds.isel(y=[1, 3]) expected = 2 * subset actual = ds + subset self.assertDatasetIdentical(expected, actual) actual = ds.isel(y=slice(1)) + ds.isel(y=slice(1, None)) expected = 2 * ds.drop(ds.y, dim='y') self.assertDatasetEqual(actual, expected) actual = ds + ds[['bar']] expected = (2 * ds[['bar']]).merge(ds.coords) self.assertDatasetIdentical(expected, actual) self.assertDatasetIdentical(ds + Dataset(), ds.coords.to_dataset()) self.assertDatasetIdentical(Dataset() + Dataset(), Dataset()) ds2 = Dataset(coords={'bar': 42}) self.assertDatasetIdentical(ds + ds2, ds.coords.merge(ds2)) # maybe unary arithmetic with empty datasets should raise instead? self.assertDatasetIdentical(Dataset() + 1, Dataset()) actual = ds.copy(deep=True) other = ds.isel(y=slice(2)) actual += other expected = ds + other.reindex_like(ds) self.assertDatasetIdentical(expected, actual) def test_dataset_math_errors(self): ds = self.make_example_math_dataset() with self.assertRaises(TypeError): ds['foo'] += ds with self.assertRaises(TypeError): ds['foo'].variable += ds with self.assertRaisesRegexp(ValueError, 'must have the same'): ds += ds[['bar']] # verify we can rollback in-place operations if something goes wrong # nb. inplace datetime64 math actually will work with an integer array # but not floats thanks to numpy's inconsistent handling other = DataArray(np.datetime64('2000-01-01T12'), coords={'c': 2}) actual = ds.copy(deep=True) with self.assertRaises(TypeError): actual += other self.assertDatasetIdentical(actual, ds) def test_dataset_transpose(self): ds = Dataset({'a': (('x', 'y'), np.random.randn(3, 4)), 'b': (('y', 'x'), np.random.randn(4, 3))}) actual = ds.transpose() expected = ds.apply(lambda x: x.transpose()) self.assertDatasetIdentical(expected, actual) actual = ds.T self.assertDatasetIdentical(expected, actual) actual = ds.transpose('x', 'y') expected = ds.apply(lambda x: x.transpose('x', 'y')) self.assertDatasetIdentical(expected, actual) ds = create_test_data() actual = ds.transpose() for k in ds: self.assertEqual(actual[k].dims[::-1], ds[k].dims) new_order = ('dim2', 'dim3', 'dim1', 'time') actual = ds.transpose(*new_order) for k in ds: expected_dims = tuple(d for d in new_order if d in ds[k].dims) self.assertEqual(actual[k].dims, expected_dims) with self.assertRaisesRegexp(ValueError, 'arguments to transpose'): ds.transpose('dim1', 'dim2', 'dim3') with self.assertRaisesRegexp(ValueError, 'arguments to transpose'): ds.transpose('dim1', 'dim2', 'dim3', 'time', 'extra_dim') def test_dataset_retains_period_index_on_transpose(self): ds = create_test_data() ds['time'] = pd.period_range('2000-01-01', periods=20) transposed = ds.transpose() self.assertIsInstance(transposed.time.to_index(), pd.PeriodIndex) def test_dataset_diff_n1_simple(self): ds = Dataset({'foo': ('x', [5, 5, 6, 6])}) actual = ds.diff('x') expected = Dataset({'foo': ('x', [0, 1, 0])}) self.assertDatasetEqual(expected, actual) def test_dataset_diff_n1_label(self): ds = Dataset({'foo': ('x', [5, 5, 6, 6])}, {'x': [0, 1, 2, 3]}) actual = ds.diff('x', label='lower') expected = Dataset({'foo': ('x', [0, 1, 0])}, {'x': [0, 1, 2]}) self.assertDatasetEqual(expected, actual) actual = ds.diff('x', label='upper') expected = Dataset({'foo': ('x', [0, 1, 0])}, {'x': [1, 2, 3]}) self.assertDatasetEqual(expected, actual) def test_dataset_diff_n1(self): ds = create_test_data(seed=1) actual = ds.diff('dim2') expected = dict() expected['var1'] = DataArray(np.diff(ds['var1'].values, axis=1), {'dim2': ds['dim2'].values[1:]}, ['dim1', 'dim2']) expected['var2'] = DataArray(np.diff(ds['var2'].values, axis=1), {'dim2': ds['dim2'].values[1:]}, ['dim1', 'dim2']) expected['var3'] = ds['var3'] expected = Dataset(expected, coords={'time': ds['time'].values}) expected.coords['numbers'] = ('dim3', ds['numbers'].values) self.assertDatasetEqual(expected, actual) def test_dataset_diff_n2(self): ds = create_test_data(seed=1) actual = ds.diff('dim2', n=2) expected = dict() expected['var1'] = DataArray(np.diff(ds['var1'].values, axis=1, n=2), {'dim2': ds['dim2'].values[2:]}, ['dim1', 'dim2']) expected['var2'] = DataArray(np.diff(ds['var2'].values, axis=1, n=2), {'dim2': ds['dim2'].values[2:]}, ['dim1', 'dim2']) expected['var3'] = ds['var3'] expected = Dataset(expected, coords={'time': ds['time'].values}) expected.coords['numbers'] = ('dim3', ds['numbers'].values) self.assertDatasetEqual(expected, actual) def test_dataset_diff_exception_n_neg(self): ds = create_test_data(seed=1) with self.assertRaisesRegexp(ValueError, 'must be non-negative'): ds.diff('dim2', n=-1) def test_dataset_diff_exception_label_str(self): ds = create_test_data(seed=1) with self.assertRaisesRegexp(ValueError, '\'label\' argument has to'): ds.diff('dim2', label='raise_me') def test_shift(self): coords = {'bar': ('x', list('abc')), 'x': [-4, 3, 2]} attrs = {'meta': 'data'} ds = Dataset({'foo': ('x', [1, 2, 3])}, coords, attrs) actual = ds.shift(x=1) expected = Dataset({'foo': ('x', [np.nan, 1, 2])}, coords, attrs) self.assertDatasetIdentical(expected, actual) with self.assertRaisesRegexp(ValueError, 'dimensions'): ds.shift(foo=123) def test_roll(self): coords = {'bar': ('x', list('abc')), 'x': [-4, 3, 2]} attrs = {'meta': 'data'} ds = Dataset({'foo': ('x', [1, 2, 3])}, coords, attrs) actual = ds.roll(x=1) ex_coords = {'bar': ('x', list('cab')), 'x': [2, -4, 3]} expected = Dataset({'foo': ('x', [3, 1, 2])}, ex_coords, attrs) self.assertDatasetIdentical(expected, actual) with self.assertRaisesRegexp(ValueError, 'dimensions'): ds.roll(foo=123) def test_real_and_imag(self): attrs = {'foo': 'bar'} ds = Dataset({'x': ((), 1 + 2j, attrs)}, attrs=attrs) expected_re = Dataset({'x': ((), 1, attrs)}, attrs=attrs) self.assertDatasetIdentical(ds.real, expected_re) expected_im = Dataset({'x': ((), 2, attrs)}, attrs=attrs) self.assertDatasetIdentical(ds.imag, expected_im) def test_setattr_raises(self): ds = Dataset({}, coords={'scalar': 1}, attrs={'foo': 'bar'}) with self.assertRaisesRegexp(AttributeError, 'cannot set attr'): ds.scalar = 2 with self.assertRaisesRegexp(AttributeError, 'cannot set attr'): ds.foo = 2 with self.assertRaisesRegexp(AttributeError, 'cannot set attr'): ds.other = 2 def test_filter_by_attrs(self): precip = dict(standard_name='convective_precipitation_flux') temp0 = dict(standard_name='air_potential_temperature', height='0 m') temp10 = dict(standard_name='air_potential_temperature', height='10 m') ds = Dataset({'temperature_0': (['t'], [0], temp0), 'temperature_10': (['t'], [0], temp10), 'precipitation': (['t'], [0], precip)}, coords={'time': (['t'], [0], dict(axis='T'))}) # Test return empty Dataset. ds.filter_by_attrs(standard_name='invalid_standard_name') new_ds = ds.filter_by_attrs(standard_name='invalid_standard_name') self.assertFalse(bool(new_ds.data_vars)) # Test return one DataArray. new_ds = ds.filter_by_attrs(standard_name='convective_precipitation_flux') self.assertEqual(new_ds['precipitation'].standard_name, 'convective_precipitation_flux') self.assertDatasetEqual(new_ds['precipitation'], ds['precipitation']) # Test return more than one DataArray. new_ds = ds.filter_by_attrs(standard_name='air_potential_temperature') self.assertEqual(len(new_ds.data_vars), 2) for var in new_ds.data_vars: self.assertEqual(new_ds[var].standard_name, 'air_potential_temperature') # Test callable. new_ds = ds.filter_by_attrs(height=lambda v: v is not None) self.assertEqual(len(new_ds.data_vars), 2) for var in new_ds.data_vars: self.assertEqual(new_ds[var].standard_name, 'air_potential_temperature') new_ds = ds.filter_by_attrs(height='10 m') self.assertEqual(len(new_ds.data_vars), 1) for var in new_ds.data_vars: self.assertEqual(new_ds[var].height, '10 m') def test_binary_op_join_setting(self): # arithmetic_join applies to data array coordinates missing_2 = xr.Dataset({'x': [0, 1]}) missing_0 = xr.Dataset({'x': [1, 2]}) with xr.set_options(arithmetic_join='outer'): actual = missing_2 + missing_0 expected = xr.Dataset({'x': [0, 1, 2]}) self.assertDatasetEqual(actual, expected) # arithmetic join also applies to data_vars ds1 = xr.Dataset({'foo': 1, 'bar': 2}) ds2 = xr.Dataset({'bar': 2, 'baz': 3}) expected = xr.Dataset({'bar': 4}) # default is inner joining actual = ds1 + ds2 self.assertDatasetEqual(actual, expected) with xr.set_options(arithmetic_join='outer'): expected = xr.Dataset({'foo': np.nan, 'bar': 4, 'baz': np.nan}) actual = ds1 + ds2 self.assertDatasetEqual(actual, expected) with xr.set_options(arithmetic_join='left'): expected = xr.Dataset({'foo': np.nan, 'bar': 4}) actual = ds1 + ds2 self.assertDatasetEqual(actual, expected) with xr.set_options(arithmetic_join='right'): expected = xr.Dataset({'bar': 4, 'baz': np.nan}) actual = ds1 + ds2 self.assertDatasetEqual(actual, expected) def test_full_like(self): # For more thorough tests, see test_variable.py # Note: testing data_vars with mismatched dtypes ds = Dataset({ 'd1': DataArray([1,2,3], dims=['x'], coords={'x': [10, 20, 30]}), 'd2': DataArray([1.1, 2.2, 3.3], dims=['y']) }, attrs={'foo': 'bar'}) actual = full_like(ds, 2) expect = ds.copy(deep=True) expect['d1'].values = [2, 2, 2] expect['d2'].values = [2.0, 2.0, 2.0] self.assertEqual(expect['d1'].dtype, int) self.assertEqual(expect['d2'].dtype, float) self.assertDatasetIdentical(expect, actual) # override dtype actual = full_like(ds, fill_value=True, dtype=bool) expect = ds.copy(deep=True) expect['d1'].values = [True, True, True] expect['d2'].values = [True, True, True] self.assertEqual(expect['d1'].dtype, bool) self.assertEqual(expect['d2'].dtype, bool) self.assertDatasetIdentical(expect, actual) def test_combine_first(self): dsx0 = DataArray([0, 0], [('x', ['a', 'b'])]).to_dataset(name='dsx0') dsx1 = DataArray([1, 1], [('x', ['b', 'c'])]).to_dataset(name='dsx1') actual = dsx0.combine_first(dsx1) expected = Dataset({'dsx0': ('x', [0, 0, np.nan]), 'dsx1': ('x', [np.nan, 1, 1])}, coords={'x': ['a', 'b', 'c']}) self.assertDatasetEqual(actual, expected) self.assertDatasetEqual(actual, xr.merge([dsx0, dsx1])) # works just like xr.merge([self, other]) dsy2 = DataArray([2, 2, 2], [('x', ['b', 'c', 'd'])]).to_dataset(name='dsy2') actual = dsx0.combine_first(dsy2) expected = xr.merge([dsy2, dsx0]) self.assertDatasetEqual(actual, expected) ### Py.test tests @pytest.fixture() def data_set(seed=None): return create_test_data(seed) def test_dir_expected_attrs(data_set): some_expected_attrs = {'pipe', 'mean', 'isnull', 'var1', 'dim2', 'numbers'} result = dir(data_set) assert set(result) >= some_expected_attrs def test_dir_non_string(data_set): # add a numbered key to ensure this doesn't break dir data_set[5] = 'foo' result = dir(data_set) assert not (5 in result) def test_dir_unicode(data_set): data_set[u'unicode'] = 'uni' result = dir(data_set) assert u'unicode' in result ����������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������xarray-0.9.1/xarray/tests/test_distributed.py�������������������������������������������������������0000644�1311115�0011610�00000002163�13043511243�021530� 0����������������������������������������������������������������������������������������������������ustar �shoyer��������������������������eng�����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������import pytest import xarray as xr from xarray.core.pycompat import suppress distributed = pytest.importorskip('distributed') da = pytest.importorskip('dask.array') from distributed.utils_test import cluster, loop from xarray.tests.test_backends import create_tmp_file from xarray.tests.test_dataset import create_test_data from . import assert_allclose, has_scipy, has_netCDF4, has_h5netcdf ENGINES = [] if has_scipy: ENGINES.append('scipy') if has_netCDF4: ENGINES.append('netcdf4') if has_h5netcdf: ENGINES.append('h5netcdf') @pytest.mark.parametrize('engine', ENGINES) def test_dask_distributed_integration_test(loop, engine): with cluster() as (s, _): with distributed.Client(('127.0.0.1', s['port']), loop=loop): original = create_test_data() with create_tmp_file() as filename: original.to_netcdf(filename, engine=engine) restored = xr.open_dataset(filename, chunks=3, engine=engine) assert isinstance(restored.var1.data, da.Array) computed = restored.compute() assert_allclose(original, computed) �������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������xarray-0.9.1/xarray/tests/test_extensions.py��������������������������������������������������������0000644�1311115�0011610�00000005246�13043511243�021412� 0����������������������������������������������������������������������������������������������������ustar �shoyer��������������������������eng�����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������from __future__ import absolute_import from __future__ import division from __future__ import print_function try: import cPickle as pickle except ImportError: import pickle import xarray as xr from . import TestCase @xr.register_dataset_accessor('example_accessor') @xr.register_dataarray_accessor('example_accessor') class ExampleAccessor(object): """For the pickling tests below.""" def __init__(self, xarray_obj): self.obj = xarray_obj class TestAccessor(TestCase): def test_register(self): @xr.register_dataset_accessor('demo') @xr.register_dataarray_accessor('demo') class DemoAccessor(object): """Demo accessor.""" def __init__(self, xarray_obj): self._obj = xarray_obj @property def foo(self): return 'bar' ds = xr.Dataset() assert ds.demo.foo == 'bar' da = xr.DataArray(0) assert da.demo.foo == 'bar' # accessor is cached assert ds.demo is ds.demo # check descriptor assert ds.demo.__doc__ == "Demo accessor." assert xr.Dataset.demo.__doc__ == "Demo accessor." assert isinstance(ds.demo, DemoAccessor) assert xr.Dataset.demo is DemoAccessor # ensure we can remove it del xr.Dataset.demo assert not hasattr(xr.Dataset, 'demo') with self.assertWarns('overriding a preexisting attribute'): @xr.register_dataarray_accessor('demo') class Foo(object): pass # it didn't get registered again assert not hasattr(xr.Dataset, 'demo') def test_pickle_dataset(self): ds = xr.Dataset() ds_restored = pickle.loads(pickle.dumps(ds)) assert ds.identical(ds_restored) # state save on the accessor is restored assert ds.example_accessor is ds.example_accessor ds.example_accessor.value = 'foo' ds_restored = pickle.loads(pickle.dumps(ds)) assert ds.identical(ds_restored) assert ds_restored.example_accessor.value == 'foo' def test_pickle_dataarray(self): array = xr.Dataset() assert array.example_accessor is array.example_accessor array_restored = pickle.loads(pickle.dumps(array)) assert array.identical(array_restored) def test_broken_accessor(self): # regression test for GH933 @xr.register_dataset_accessor('stupid_accessor') class BrokenAccessor(object): def __init__(self, xarray_obj): raise AttributeError('broken') with self.assertRaisesRegexp(RuntimeError, 'error initializing'): xr.Dataset().stupid_accessor ����������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������xarray-0.9.1/xarray/tests/test_formatting.py��������������������������������������������������������0000644�1311115�0011610�00000012700�13043511243�021356� 0����������������������������������������������������������������������������������������������������ustar �shoyer��������������������������eng�����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������# -*- coding: utf-8 -*- from __future__ import absolute_import from __future__ import division from __future__ import print_function import numpy as np import pandas as pd from xarray.core import formatting from xarray.core.pycompat import PY3 from . import TestCase class TestFormatting(TestCase): def test_get_indexer_at_least_n_items(self): cases = [ ((20,), (slice(10),)), ((3, 20,), (0, slice(10))), ((2, 10,), (0, slice(10))), ((2, 5,), (slice(2), slice(None))), ((1, 2, 5,), (0, slice(2), slice(None))), ((2, 3, 5,), (0, slice(2), slice(None))), ((1, 10, 1,), (0, slice(10), slice(None))), ((2, 5, 1,), (slice(2), slice(None), slice(None))), ((2, 5, 3,), (0, slice(4), slice(None))), ((2, 3, 3,), (slice(2), slice(None), slice(None))), ] for shape, expected in cases: actual = formatting._get_indexer_at_least_n_items(shape, 10) self.assertEqual(expected, actual) def test_first_n_items(self): array = np.arange(100).reshape(10, 5, 2) for n in [3, 10, 13, 100, 200]: actual = formatting.first_n_items(array, n) expected = array.flat[:n] self.assertItemsEqual(expected, actual) with self.assertRaisesRegexp(ValueError, 'at least one item'): formatting.first_n_items(array, 0) def test_last_item(self): array = np.arange(100) reshape = ((10, 10), (1, 100), (2, 2, 5, 5)) expected = np.array([99]) for r in reshape: result = formatting.last_item(array.reshape(r)) self.assertEqual(result, expected) def test_format_item(self): cases = [ (pd.Timestamp('2000-01-01T12'), '2000-01-01T12:00:00'), (pd.Timestamp('2000-01-01'), '2000-01-01'), (pd.Timestamp('NaT'), 'NaT'), (pd.Timedelta('10 days 1 hour'), '10 days 01:00:00'), (pd.Timedelta('-3 days'), '-3 days +00:00:00'), (pd.Timedelta('3 hours'), '0 days 03:00:00'), (pd.Timedelta('NaT'), 'NaT'), ('foo', "'foo'"), (u'foo', "'foo'" if PY3 else "u'foo'"), (b'foo', "b'foo'" if PY3 else "'foo'"), (1, '1'), (1.0, '1.0'), ] for item, expected in cases: actual = formatting.format_item(item) self.assertEqual(expected, actual) def test_format_items(self): cases = [ (np.arange(4) * np.timedelta64(1, 'D'), '0 days 1 days 2 days 3 days'), (np.arange(4) * np.timedelta64(3, 'h'), '00:00:00 03:00:00 06:00:00 09:00:00'), (np.arange(4) * np.timedelta64(500, 'ms'), '00:00:00 00:00:00.500000 00:00:01 00:00:01.500000'), (pd.to_timedelta(['NaT', '0s', '1s', 'NaT']), 'NaT 00:00:00 00:00:01 NaT'), (pd.to_timedelta(['1 day 1 hour', '1 day', '0 hours']), '1 days 01:00:00 1 days 00:00:00 0 days 00:00:00'), ([1, 2, 3], '1 2 3'), ] for item, expected in cases: actual = ' '.join(formatting.format_items(item)) self.assertEqual(expected, actual) def test_format_array_flat(self): actual = formatting.format_array_flat(np.arange(100), 13) expected = '0 1 2 3 4 ...' self.assertEqual(expected, actual) actual = formatting.format_array_flat(np.arange(100.0), 11) expected = '0.0 1.0 ...' self.assertEqual(expected, actual) actual = formatting.format_array_flat(np.arange(100.0), 1) expected = '0.0 ...' self.assertEqual(expected, actual) actual = formatting.format_array_flat(np.arange(3), 5) expected = '0 1 2' self.assertEqual(expected, actual) actual = formatting.format_array_flat(np.arange(4.0), 11) expected = '0.0 1.0 ...' self.assertEqual(expected, actual) actual = formatting.format_array_flat(np.arange(4), 0) expected = '0 ...' self.assertEqual(expected, actual) def test_pretty_print(self): self.assertEqual(formatting.pretty_print('abcdefghij', 8), 'abcde...') self.assertEqual(formatting.pretty_print(u'ß', 1), u'ß') def test_maybe_truncate(self): self.assertEqual(formatting.maybe_truncate(u'ß', 10), u'ß') def test_format_timestamp_out_of_bounds(self): from datetime import datetime date = datetime(1300, 12, 1) expected = '1300-12-01' result = formatting.format_timestamp(date) self.assertEqual(result, expected) date = datetime(2300, 12, 1) expected = '2300-12-01' result = formatting.format_timestamp(date) self.assertEqual(result, expected) def test_set_numpy_options(): original_options = np.get_printoptions() with formatting.set_numpy_options(threshold=10): assert len(repr(np.arange(500))) < 200 # original options are restored assert np.get_printoptions() == original_options def test_short_array_repr(): cases = [ np.random.randn(500), np.random.randn(20, 20), np.random.randn(5, 10, 15), np.random.randn(5, 10, 15, 3), ] # number of lines: # for default numpy repr: 167, 140, 254, 248 # for short_array_repr: 1, 7, 24, 19 for array in cases: num_lines = formatting.short_array_repr(array).count('\n') + 1 assert num_lines < 30 ����������������������������������������������������������������xarray-0.9.1/xarray/tests/test_groupby.py�����������������������������������������������������������0000644�1311115�0011610�00000005010�13043543222�020671� 0����������������������������������������������������������������������������������������������������ustar �shoyer��������������������������eng�����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������from __future__ import absolute_import from __future__ import division from __future__ import print_function import numpy as np import pandas as pd import xarray as xr from xarray.core.groupby import _consolidate_slices import pytest def test_consolidate_slices(): assert _consolidate_slices([slice(3), slice(3, 5)]) == [slice(5)] assert _consolidate_slices([slice(2, 3), slice(3, 6)]) == [slice(2, 6)] assert (_consolidate_slices([slice(2, 3, 1), slice(3, 6, 1)]) == [slice(2, 6, 1)]) slices = [slice(2, 3), slice(5, 6)] assert _consolidate_slices(slices) == slices with pytest.raises(ValueError): _consolidate_slices([slice(3), 4]) def test_multi_index_groupby_apply(): # regression test for GH873 ds = xr.Dataset({'foo': (('x', 'y'), np.random.randn(3, 4))}, {'x': ['a', 'b', 'c'], 'y': [1, 2, 3, 4]}) doubled = 2 * ds group_doubled = (ds.stack(space=['x', 'y']) .groupby('space') .apply(lambda x: 2 * x) .unstack('space')) assert doubled.equals(group_doubled) def test_multi_index_groupby_sum(): # regression test for GH873 ds = xr.Dataset({'foo': (('x', 'y', 'z'), np.ones((3, 4, 2)))}, {'x': ['a', 'b', 'c'], 'y': [1, 2, 3, 4]}) expected = ds.sum('z') actual = (ds.stack(space=['x', 'y']) .groupby('space') .sum('z') .unstack('space')) assert expected.equals(actual) def test_groupby_da_datetime(): # test groupby with a DataArray of dtype datetime for GH1132 # create test data times = pd.date_range('2000-01-01', periods=4) foo = xr.DataArray([1,2,3,4], coords=dict(time=times), dims='time') # create test index dd = times.to_datetime() reference_dates = [dd[0], dd[2]] labels = reference_dates[0:1]*2 + reference_dates[1:2]*2 ind = xr.DataArray(labels, coords=dict(time=times), dims='time', name='reference_date') g = foo.groupby(ind) actual = g.sum(dim='time') expected = xr.DataArray([3,7], coords=dict(reference_date=reference_dates), dims='reference_date') assert actual.equals(expected) def test_groupby_duplicate_coordinate_labels(): # fix for http://stackoverflow.com/questions/38065129 array = xr.DataArray([1, 2, 3], [('x', [1, 1, 2])]) expected = xr.DataArray([3, 3], [('x', [1, 2])]) actual = array.groupby('x').sum() assert expected.equals(actual) # TODO: move other groupby tests from test_dataset and test_dataarray over here ������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������xarray-0.9.1/xarray/tests/test_indexing.py����������������������������������������������������������0000644�1311115�0011610�00000026063�13043511243�021020� 0����������������������������������������������������������������������������������������������������ustar �shoyer��������������������������eng�����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������from __future__ import absolute_import from __future__ import division from __future__ import print_function import numpy as np import pandas as pd from xarray import Dataset, DataArray, Variable from xarray.core import indexing from . import TestCase, ReturnItem class TestIndexers(TestCase): def set_to_zero(self, x, i): x = x.copy() x[i] = 0 return x def test_expanded_indexer(self): x = np.random.randn(10, 11, 12, 13, 14) y = np.arange(5) I = ReturnItem() for i in [I[:], I[...], I[0, :, 10], I[..., 10], I[:5, ..., 0], I[..., 0, :], I[y], I[y, y], I[..., y, y], I[..., 0, 1, 2, 3, 4]]: j = indexing.expanded_indexer(i, x.ndim) self.assertArrayEqual(x[i], x[j]) self.assertArrayEqual(self.set_to_zero(x, i), self.set_to_zero(x, j)) with self.assertRaisesRegexp(IndexError, 'too many indices'): indexing.expanded_indexer(I[1, 2, 3], 2) def test_orthogonal_indexer(self): x = np.random.randn(10, 11, 12, 13, 14) y = np.arange(5) I = ReturnItem() # orthogonal and numpy indexing should be equivalent, because we only # use at most one array and it never in between two slice objects # (i.e., we try to avoid numpy's mind-boggling "partial indexing" # http://docs.scipy.org/doc/numpy/reference/arrays.indexing.html) for i in [I[:], I[0], I[0, 0], I[:5], I[5:], I[2:5], I[3:-3], I[::-1], I[::-2], I[5::-2], I[:3:-2], I[2:5:-1], I[7:3:-2], I[:3, :4], I[:3, 0, :4], I[:3, 0, :4, 0], I[y], I[:, y], I[0, y], I[:2, :3, y], I[0, y, :, :4, 0]]: j = indexing.orthogonal_indexer(i, x.shape) self.assertArrayEqual(x[i], x[j]) self.assertArrayEqual(self.set_to_zero(x, i), self.set_to_zero(x, j)) # for more complicated cases, check orthogonal indexing is still # equivalent to slicing z = np.arange(2, 8, 2) for i, j, shape in [ (I[y, y], I[:5, :5], (5, 5, 12, 13, 14)), (I[y, z], I[:5, 2:8:2], (5, 3, 12, 13, 14)), (I[0, y, y], I[0, :5, :5], (5, 5, 13, 14)), (I[y, 0, z], I[:5, 0, 2:8:2], (5, 3, 13, 14)), (I[y, :, z], I[:5, :, 2:8:2], (5, 11, 3, 13, 14)), (I[0, :, z], I[0, :, 2:8:2], (11, 3, 13, 14)), (I[0, :2, y, y, 0], I[0, :2, :5, :5, 0], (2, 5, 5)), (I[0, :, y, :, 0], I[0, :, :5, :, 0], (11, 5, 13)), (I[:, :, y, :, 0], I[:, :, :5, :, 0], (10, 11, 5, 13)), (I[:, :, y, z, :], I[:, :, :5, 2:8:2], (10, 11, 5, 3, 14))]: k = indexing.orthogonal_indexer(i, x.shape) self.assertEqual(shape, x[k].shape) self.assertArrayEqual(x[j], x[k]) self.assertArrayEqual(self.set_to_zero(x, j), self.set_to_zero(x, k)) # standard numpy (non-orthogonal) indexing doesn't work anymore with self.assertRaisesRegexp(ValueError, 'only supports 1d'): indexing.orthogonal_indexer(x > 0, x.shape) with self.assertRaisesRegexp(ValueError, 'invalid subkey'): print(indexing.orthogonal_indexer((1.5 * y, 1.5 * y), x.shape)) def test_asarray_tuplesafe(self): res = indexing._asarray_tuplesafe(('a', 1)) assert isinstance(res, np.ndarray) assert res.ndim == 0 assert res.item() == ('a', 1) res = indexing._asarray_tuplesafe([(0,), (1,)]) assert res.shape == (2,) assert res[0] == (0,) assert res[1] == (1,) def test_convert_label_indexer(self): # TODO: add tests that aren't just for edge cases index = pd.Index([1, 2, 3]) with self.assertRaisesRegexp(KeyError, 'not all values found'): indexing.convert_label_indexer(index, [0]) with self.assertRaises(KeyError): indexing.convert_label_indexer(index, 0) with self.assertRaisesRegexp(ValueError, 'does not have a MultiIndex'): indexing.convert_label_indexer(index, {'one': 0}) mindex = pd.MultiIndex.from_product([['a', 'b'], [1, 2]], names=('one', 'two')) with self.assertRaisesRegexp(KeyError, 'not all values found'): indexing.convert_label_indexer(mindex, [0]) with self.assertRaises(KeyError): indexing.convert_label_indexer(mindex, 0) with self.assertRaises(ValueError): indexing.convert_label_indexer(index, {'three': 0}) with self.assertRaisesRegexp(KeyError, 'index to be fully lexsorted'): indexing.convert_label_indexer(mindex, (slice(None), 1, 'no_level')) def test_convert_unsorted_datetime_index_raises(self): index = pd.to_datetime(['2001', '2000', '2002']) with self.assertRaises(KeyError): # pandas will try to convert this into an array indexer. We should # raise instead, so we can be sure the result of indexing with a # slice is always a view. indexing.convert_label_indexer(index, slice('2001', '2002')) def test_get_dim_indexers(self): mindex = pd.MultiIndex.from_product([['a', 'b'], [1, 2]], names=('one', 'two')) mdata = DataArray(range(4), [('x', mindex)]) dim_indexers = indexing.get_dim_indexers(mdata, {'one': 'a', 'two': 1}) self.assertEqual(dim_indexers, {'x': {'one': 'a', 'two': 1}}) with self.assertRaisesRegexp(ValueError, 'cannot combine'): indexing.get_dim_indexers(mdata, {'x': 'a', 'two': 1}) with self.assertRaisesRegexp(ValueError, 'do not exist'): indexing.get_dim_indexers(mdata, {'y': 'a'}) with self.assertRaisesRegexp(ValueError, 'do not exist'): indexing.get_dim_indexers(mdata, {'four': 1}) def test_remap_label_indexers(self): def test_indexer(data, x, expected_pos, expected_idx=None): pos, idx = indexing.remap_label_indexers(data, {'x': x}) self.assertArrayEqual(pos.get('x'), expected_pos) self.assertArrayEqual(idx.get('x'), expected_idx) data = Dataset({'x': ('x', [1, 2, 3])}) mindex = pd.MultiIndex.from_product([['a', 'b'], [1, 2], [-1, -2]], names=('one', 'two', 'three')) mdata = DataArray(range(8), [('x', mindex)]) test_indexer(data, 1, 0) test_indexer(data, np.int32(1), 0) test_indexer(data, Variable([], 1), 0) test_indexer(mdata, ('a', 1, -1), 0) test_indexer(mdata, ('a', 1), [True, True, False, False, False, False, False, False], [-1, -2]) test_indexer(mdata, 'a', slice(0, 4, None), pd.MultiIndex.from_product([[1, 2], [-1, -2]])) test_indexer(mdata, ('a',), [True, True, True, True, False, False, False, False], pd.MultiIndex.from_product([[1, 2], [-1, -2]])) test_indexer(mdata, [('a', 1, -1), ('b', 2, -2)], [0, 7]) test_indexer(mdata, slice('a', 'b'), slice(0, 8, None)) test_indexer(mdata, slice(('a', 1), ('b', 1)), slice(0, 6, None)) test_indexer(mdata, {'one': 'a', 'two': 1, 'three': -1}, 0) test_indexer(mdata, {'one': 'a', 'two': 1}, [True, True, False, False, False, False, False, False], [-1, -2]) test_indexer(mdata, {'one': 'a', 'three': -1}, [True, False, True, False, False, False, False, False], [1, 2]) test_indexer(mdata, {'one': 'a'}, [True, True, True, True, False, False, False, False], pd.MultiIndex.from_product([[1, 2], [-1, -2]])) class TestLazyArray(TestCase): def test_slice_slice(self): I = ReturnItem() x = np.arange(100) slices = [I[:3], I[:4], I[2:4], I[:1], I[:-1], I[5:-1], I[-5:-1], I[::-1], I[5::-1], I[:3:-1], I[:30:-1], I[10:4:], I[::4], I[4:4:4], I[:4:-4]] for i in slices: for j in slices: expected = x[i][j] new_slice = indexing.slice_slice(i, j, size=100) actual = x[new_slice] self.assertArrayEqual(expected, actual) def test_lazily_indexed_array(self): x = indexing.NumpyIndexingAdapter(np.random.rand(10, 20, 30)) lazy = indexing.LazilyIndexedArray(x) I = ReturnItem() # test orthogonally applied indexers indexers = [I[:], 0, -2, I[:3], [0, 1, 2, 3], np.arange(10) < 5] for i in indexers: for j in indexers: for k in indexers: expected = np.asarray(x[i, j, k]) for actual in [lazy[i, j, k], lazy[:, j, k][i], lazy[:, :, k][:, j][i]]: self.assertEqual(expected.shape, actual.shape) self.assertArrayEqual(expected, actual) # test sequentially applied indexers indexers = [(3, 2), (I[:], 0), (I[:2], -1), (I[:4], [0]), ([4, 5], 0), ([0, 1, 2], [0, 1]), ([0, 3, 5], I[:2])] for i, j in indexers: expected = np.asarray(x[i][j]) actual = lazy[i][j] self.assertEqual(expected.shape, actual.shape) self.assertArrayEqual(expected, actual) class TestCopyOnWriteArray(TestCase): def test_setitem(self): original = np.arange(10) wrapped = indexing.CopyOnWriteArray(original) wrapped[:] = 0 self.assertArrayEqual(original, np.arange(10)) self.assertArrayEqual(wrapped, np.zeros(10)) def test_sub_array(self): original = np.arange(10) wrapped = indexing.CopyOnWriteArray(original) child = wrapped[:5] self.assertIsInstance(child, indexing.CopyOnWriteArray) child[:] = 0 self.assertArrayEqual(original, np.arange(10)) self.assertArrayEqual(wrapped, np.arange(10)) self.assertArrayEqual(child, np.zeros(5)) class TestMemoryCachedArray(TestCase): def test_wrapper(self): original = indexing.LazilyIndexedArray(np.arange(10)) wrapped = indexing.MemoryCachedArray(original) self.assertArrayEqual(wrapped, np.arange(10)) self.assertIsInstance(wrapped.array, np.ndarray) def test_sub_array(self): original = indexing.LazilyIndexedArray(np.arange(10)) wrapped = indexing.MemoryCachedArray(original) child = wrapped[:5] self.assertIsInstance(child, indexing.MemoryCachedArray) self.assertArrayEqual(child, np.arange(5)) self.assertIsInstance(child.array, np.ndarray) self.assertIsInstance(wrapped.array, indexing.LazilyIndexedArray) def test_setitem(self): original = np.arange(10) wrapped = indexing.MemoryCachedArray(original) wrapped[:] = 0 self.assertArrayEqual(original, np.zeros(10)) �����������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������xarray-0.9.1/xarray/tests/test_merge.py�������������������������������������������������������������0000644�1311115�0011610�00000021415�13043511243�020306� 0����������������������������������������������������������������������������������������������������ustar �shoyer��������������������������eng�����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������from __future__ import absolute_import from __future__ import division from __future__ import print_function import numpy as np import xarray as xr from . import TestCase from .test_dataset import create_test_data from xarray.core import merge class TestMergeInternals(TestCase): def test_broadcast_dimension_size(self): actual = merge.broadcast_dimension_size( [xr.Variable('x', [1]), xr.Variable('y', [2, 1])]) assert actual == {'x': 1, 'y': 2} actual = merge.broadcast_dimension_size( [xr.Variable(('x', 'y'), [[1, 2]]), xr.Variable('y', [2, 1])]) assert actual == {'x': 1, 'y': 2} with self.assertRaises(ValueError): actual = merge.broadcast_dimension_size( [xr.Variable(('x', 'y'), [[1, 2]]), xr.Variable('y', [2])]) class TestMergeFunction(TestCase): def test_merge_arrays(self): data = create_test_data() actual = xr.merge([data.var1, data.var2]) expected = data[['var1', 'var2']] assert actual.identical(expected) def test_merge_datasets(self): data = create_test_data() actual = xr.merge([data[['var1']], data[['var2']]]) expected = data[['var1', 'var2']] assert actual.identical(expected) actual = xr.merge([data, data]) assert actual.identical(data) def test_merge_dataarray_unnamed(self): data = xr.DataArray([1, 2], dims='x') with self.assertRaisesRegexp( ValueError, 'without providing an explicit name'): xr.merge([data]) def test_merge_dicts_simple(self): actual = xr.merge([{'foo': 0}, {'bar': 'one'}, {'baz': 3.5}]) expected = xr.Dataset({'foo': 0, 'bar': 'one', 'baz': 3.5}) assert actual.identical(expected) def test_merge_dicts_dims(self): actual = xr.merge([{'y': ('x', [13])}, {'x': [12]}]) expected = xr.Dataset({'x': [12], 'y': ('x', [13])}) assert actual.identical(expected) def test_merge_error(self): ds = xr.Dataset({'x': 0}) with self.assertRaises(xr.MergeError): xr.merge([ds, ds + 1]) def test_merge_no_conflicts_single_var(self): ds1 = xr.Dataset({'a': ('x', [1, 2]), 'x': [0, 1]}) ds2 = xr.Dataset({'a': ('x', [2, 3]), 'x': [1, 2]}) expected = xr.Dataset({'a': ('x', [1, 2, 3]), 'x': [0, 1, 2]}) assert expected.identical(xr.merge([ds1, ds2], compat='no_conflicts')) assert expected.identical(xr.merge([ds2, ds1], compat='no_conflicts')) assert ds1.identical(xr.merge([ds1, ds2], compat='no_conflicts', join='left')) assert ds2.identical(xr.merge([ds1, ds2], compat='no_conflicts', join='right')) expected = xr.Dataset({'a': ('x', [2]), 'x': [1]}) assert expected.identical(xr.merge([ds1, ds2], compat='no_conflicts', join='inner')) with self.assertRaises(xr.MergeError): ds3 = xr.Dataset({'a': ('x', [99, 3]), 'x': [1, 2]}) xr.merge([ds1, ds3], compat='no_conflicts') with self.assertRaises(xr.MergeError): ds3 = xr.Dataset({'a': ('y', [2, 3]), 'y': [1, 2]}) xr.merge([ds1, ds3], compat='no_conflicts') def test_merge_no_conflicts_multi_var(self): data = create_test_data() data1 = data.copy(deep=True) data2 = data.copy(deep=True) expected = data[['var1', 'var2']] actual = xr.merge([data1.var1, data2.var2], compat='no_conflicts') assert expected.identical(actual) data1['var1'][:, :5] = np.nan data2['var1'][:, 5:] = np.nan data1['var2'][:4, :] = np.nan data2['var2'][4:, :] = np.nan del data2['var3'] actual = xr.merge([data1, data2], compat='no_conflicts') assert data.equals(actual) def test_merge_no_conflicts_preserve_attrs(self): data = xr.Dataset({'x': ([], 0, {'foo': 'bar'})}) actual = xr.merge([data, data]) assert data.identical(actual) def test_merge_no_conflicts_broadcast(self): datasets = [xr.Dataset({'x': ('y', [0])}), xr.Dataset({'x': np.nan})] actual = xr.merge(datasets) expected = xr.Dataset({'x': ('y', [0])}) assert expected.identical(actual) datasets = [xr.Dataset({'x': ('y', [np.nan])}), xr.Dataset({'x': 0})] actual = xr.merge(datasets) assert expected.identical(actual) class TestMergeMethod(TestCase): def test_merge(self): data = create_test_data() ds1 = data[['var1']] ds2 = data[['var3']] expected = data[['var1', 'var3']] actual = ds1.merge(ds2) assert expected.identical(actual) actual = ds2.merge(ds1) assert expected.identical(actual) actual = data.merge(data) assert data.identical(actual) actual = data.reset_coords(drop=True).merge(data) assert data.identical(actual) actual = data.merge(data.reset_coords(drop=True)) assert data.identical(actual) with self.assertRaises(ValueError): ds1.merge(ds2.rename({'var3': 'var1'})) with self.assertRaisesRegexp( ValueError, 'should be coordinates or not'): data.reset_coords().merge(data) with self.assertRaisesRegexp( ValueError, 'should be coordinates or not'): data.merge(data.reset_coords()) def test_merge_broadcast_equals(self): ds1 = xr.Dataset({'x': 0}) ds2 = xr.Dataset({'x': ('y', [0, 0])}) actual = ds1.merge(ds2) assert ds2.identical(actual) actual = ds2.merge(ds1) assert ds2.identical(actual) actual = ds1.copy() actual.update(ds2) assert ds2.identical(actual) ds1 = xr.Dataset({'x': np.nan}) ds2 = xr.Dataset({'x': ('y', [np.nan, np.nan])}) actual = ds1.merge(ds2) assert ds2.identical(actual) def test_merge_compat(self): ds1 = xr.Dataset({'x': 0}) ds2 = xr.Dataset({'x': 1}) for compat in ['broadcast_equals', 'equals', 'identical', 'no_conflicts']: with self.assertRaises(xr.MergeError): ds1.merge(ds2, compat=compat) ds2 = xr.Dataset({'x': [0, 0]}) for compat in ['equals', 'identical']: with self.assertRaisesRegexp( ValueError, 'should be coordinates or not'): ds1.merge(ds2, compat=compat) ds2 = xr.Dataset({'x': ((), 0, {'foo': 'bar'})}) with self.assertRaises(xr.MergeError): ds1.merge(ds2, compat='identical') with self.assertRaisesRegexp(ValueError, 'compat=\S+ invalid'): ds1.merge(ds2, compat='foobar') def test_merge_auto_align(self): ds1 = xr.Dataset({'a': ('x', [1, 2]), 'x': [0, 1]}) ds2 = xr.Dataset({'b': ('x', [3, 4]), 'x': [1, 2]}) expected = xr.Dataset({'a': ('x', [1, 2, np.nan]), 'b': ('x', [np.nan, 3, 4])}, {'x': [0, 1, 2]}) assert expected.identical(ds1.merge(ds2)) assert expected.identical(ds2.merge(ds1)) expected = expected.isel(x=slice(2)) assert expected.identical(ds1.merge(ds2, join='left')) assert expected.identical(ds2.merge(ds1, join='right')) expected = expected.isel(x=slice(1, 2)) assert expected.identical(ds1.merge(ds2, join='inner')) assert expected.identical(ds2.merge(ds1, join='inner')) def test_merge_no_conflicts(self): ds1 = xr.Dataset({'a': ('x', [1, 2]), 'x': [0, 1]}) ds2 = xr.Dataset({'a': ('x', [2, 3]), 'x': [1, 2]}) expected = xr.Dataset({'a': ('x', [1, 2, 3]), 'x': [0, 1, 2]}) assert expected.identical(ds1.merge(ds2, compat='no_conflicts')) assert expected.identical(ds2.merge(ds1, compat='no_conflicts')) assert ds1.identical(ds1.merge(ds2, compat='no_conflicts', join='left')) assert ds2.identical(ds1.merge(ds2, compat='no_conflicts', join='right')) expected2 = xr.Dataset({'a': ('x', [2]), 'x': [1]}) assert expected2.identical(ds1.merge(ds2, compat='no_conflicts', join='inner')) with self.assertRaises(xr.MergeError): ds3 = xr.Dataset({'a': ('x', [99, 3]), 'x': [1, 2]}) ds1.merge(ds3, compat='no_conflicts') with self.assertRaises(xr.MergeError): ds3 = xr.Dataset({'a': ('y', [2, 3]), 'y': [1, 2]}) ds1.merge(ds3, compat='no_conflicts') ���������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������xarray-0.9.1/xarray/tests/test_ops.py���������������������������������������������������������������0000644�1311115�0011610�00000007450�13043511243�020013� 0����������������������������������������������������������������������������������������������������ustar �shoyer��������������������������eng�����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������from __future__ import absolute_import from __future__ import division from __future__ import print_function from pytest import mark import numpy as np from numpy import array, nan from xarray.core.ops import ( first, last, count, mean, array_notnull_equiv, ) from . import TestCase class TestOps(TestCase): def setUp(self): self.x = array([[[nan, nan, 2., nan], [nan, 5., 6., nan], [8., 9., 10., nan]], [[nan, 13., 14., 15.], [nan, 17., 18., nan], [nan, 21., nan, nan]]]) def test_first(self): expected_results = [array([[nan, 13, 2, 15], [nan, 5, 6, nan], [8, 9, 10, nan]]), array([[8, 5, 2, nan], [nan, 13, 14, 15]]), array([[2, 5, 8], [13, 17, 21]])] for axis, expected in zip([0, 1, 2, -3, -2, -1], 2 * expected_results): actual = first(self.x, axis) self.assertArrayEqual(expected, actual) expected = self.x[0] actual = first(self.x, axis=0, skipna=False) self.assertArrayEqual(expected, actual) expected = self.x[..., 0] actual = first(self.x, axis=-1, skipna=False) self.assertArrayEqual(expected, actual) with self.assertRaisesRegexp(IndexError, 'out of bounds'): first(self.x, 3) def test_last(self): expected_results = [array([[nan, 13, 14, 15], [nan, 17, 18, nan], [8, 21, 10, nan]]), array([[8, 9, 10, nan], [nan, 21, 18, 15]]), array([[2, 6, 10], [15, 18, 21]])] for axis, expected in zip([0, 1, 2, -3, -2, -1], 2 * expected_results): actual = last(self.x, axis) self.assertArrayEqual(expected, actual) expected = self.x[-1] actual = last(self.x, axis=0, skipna=False) self.assertArrayEqual(expected, actual) expected = self.x[..., -1] actual = last(self.x, axis=-1, skipna=False) self.assertArrayEqual(expected, actual) with self.assertRaisesRegexp(IndexError, 'out of bounds'): last(self.x, 3) def test_count(self): self.assertEqual(12, count(self.x)) expected = array([[1, 2, 3], [3, 2, 1]]) self.assertArrayEqual(expected, count(self.x, axis=-1)) def test_all_nan_arrays(self): assert np.isnan(mean([np.nan, np.nan])) class TestArrayNotNullEquiv(): @mark.parametrize("arr1, arr2", [ (np.array([1, 2, 3]), np.array([1, 2, 3])), (np.array([1, 2, np.nan]), np.array([1, np.nan, 3])), (np.array([np.nan, 2, np.nan]), np.array([1, np.nan, np.nan])), ]) def test_equal(self, arr1, arr2): assert array_notnull_equiv(arr1, arr2) def test_some_not_equal(self): a = np.array([1, 2, 4]) b = np.array([1, np.nan, 3]) assert not array_notnull_equiv(a, b) def test_wrong_shape(self): a = np.array([[1, np.nan, np.nan, 4]]) b = np.array([[1, 2], [np.nan, 4]]) assert not array_notnull_equiv(a, b) @mark.parametrize("val1, val2, val3, null", [ (1, 2, 3, None), (1., 2., 3., np.nan), (1., 2., 3., None), ('foo', 'bar', 'baz', None), ]) def test_types(self, val1, val2, val3, null): arr1 = np.array([val1, null, val3, null]) arr2 = np.array([val1, val2, null, null]) assert array_notnull_equiv(arr1, arr2) ������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������xarray-0.9.1/xarray/tests/test_options.py�����������������������������������������������������������0000644�1311115�0011610�00000001217�13043511243�020700� 0����������������������������������������������������������������������������������������������������ustar �shoyer��������������������������eng�����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������from __future__ import absolute_import from __future__ import division from __future__ import print_function import xarray import pytest from xarray.core.options import OPTIONS def test_invalid_option_raises(): with pytest.raises(ValueError): xarray.set_options(not_a_valid_options=True) def test_nested_options(): original = OPTIONS['display_width'] with xarray.set_options(display_width=1): assert OPTIONS['display_width'] == 1 with xarray.set_options(display_width=2): assert OPTIONS['display_width'] == 2 assert OPTIONS['display_width'] == 1 assert OPTIONS['display_width'] == original ���������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������xarray-0.9.1/xarray/tests/test_plot.py��������������������������������������������������������������0000644�1311115�0011610�00000131573�13043511243�020174� 0����������������������������������������������������������������������������������������������������ustar �shoyer��������������������������eng�����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������from __future__ import absolute_import from __future__ import division from __future__ import print_function # import mpl and change the backend before other mpl imports try: import matplotlib as mpl # Using a different backend makes Travis CI work mpl.use('Agg') # Order of imports is important here. import matplotlib.pyplot as plt except ImportError: pass import inspect import numpy as np import pandas as pd from xarray import DataArray import xarray.plot as xplt from xarray.plot.plot import _infer_interval_breaks from xarray.plot.utils import (_determine_cmap_params, _build_discrete_cmap, _color_palette) from . import TestCase, requires_matplotlib def text_in_fig(): ''' Return the set of all text in the figure ''' return {t.get_text() for t in plt.gcf().findobj(mpl.text.Text)} def find_possible_colorbars(): # nb. this function also matches meshes from pcolormesh return plt.gcf().findobj(mpl.collections.QuadMesh) def substring_in_axes(substring, ax): ''' Return True if a substring is found anywhere in an axes ''' alltxt = set([t.get_text() for t in ax.findobj(mpl.text.Text)]) for txt in alltxt: if substring in txt: return True return False def easy_array(shape, start=0, stop=1): ''' Make an array with desired shape using np.linspace shape is a tuple like (2, 3) ''' a = np.linspace(start, stop, num=np.prod(shape)) return a.reshape(shape) @requires_matplotlib class PlotTestCase(TestCase): def tearDown(self): # Remove all matplotlib figures plt.close('all') def pass_in_axis(self, plotmethod): fig, axes = plt.subplots(ncols=2) plotmethod(ax=axes[0]) self.assertTrue(axes[0].has_data()) def imshow_called(self, plotmethod): plotmethod() images = plt.gca().findobj(mpl.image.AxesImage) return len(images) > 0 def contourf_called(self, plotmethod): plotmethod() paths = plt.gca().findobj(mpl.collections.PathCollection) return len(paths) > 0 class TestPlot(PlotTestCase): def setUp(self): self.darray = DataArray(easy_array((2, 3, 4))) def test1d(self): self.darray[:, 0, 0].plot() def test_2d_before_squeeze(self): a = DataArray(easy_array((1, 5))) a.plot() def test2d_uniform_calls_imshow(self): self.assertTrue(self.imshow_called(self.darray[:, :, 0].plot.imshow)) def test2d_nonuniform_calls_contourf(self): a = self.darray[:, :, 0] a.coords['dim_1'] = [2, 1, 89] self.assertTrue(self.contourf_called(a.plot.contourf)) def test3d(self): self.darray.plot() def test_can_pass_in_axis(self): self.pass_in_axis(self.darray.plot) def test__infer_interval_breaks(self): self.assertArrayEqual([-0.5, 0.5, 1.5], _infer_interval_breaks([0, 1])) self.assertArrayEqual([-0.5, 0.5, 5.0, 9.5, 10.5], _infer_interval_breaks([0, 1, 9, 10])) self.assertArrayEqual(pd.date_range('20000101', periods=4) - np.timedelta64(12, 'h'), _infer_interval_breaks(pd.date_range('20000101', periods=3))) # make a bounded 2D array that we will center and re-infer xref, yref = np.meshgrid(np.arange(6), np.arange(5)) cx = (xref[1:, 1:] + xref[:-1, :-1]) / 2 cy = (yref[1:, 1:] + yref[:-1, :-1]) / 2 x = _infer_interval_breaks(cx, axis=1) x = _infer_interval_breaks(x, axis=0) y = _infer_interval_breaks(cy, axis=1) y = _infer_interval_breaks(y, axis=0) np.testing.assert_allclose(xref, x) np.testing.assert_allclose(yref, y) def test_datetime_dimension(self): nrow = 3 ncol = 4 time = pd.date_range('2000-01-01', periods=nrow) a = DataArray(easy_array((nrow, ncol)), coords=[('time', time), ('y', range(ncol))]) a.plot() ax = plt.gca() self.assertTrue(ax.has_data()) def test_convenient_facetgrid(self): a = easy_array((10, 15, 4)) d = DataArray(a, dims=['y', 'x', 'z']) d.coords['z'] = list('abcd') g = d.plot(x='x', y='y', col='z', col_wrap=2, cmap='cool') self.assertArrayEqual(g.axes.shape, [2, 2]) for ax in g.axes.flat: self.assertTrue(ax.has_data()) with self.assertRaisesRegexp(ValueError, '[Ff]acet'): d.plot(x='x', y='y', col='z', ax=plt.gca()) with self.assertRaisesRegexp(ValueError, '[Ff]acet'): d[0].plot(x='x', y='y', col='z', ax=plt.gca()) def test_subplot_kws(self): a = easy_array((10, 15, 4)) d = DataArray(a, dims=['y', 'x', 'z']) d.coords['z'] = list('abcd') g = d.plot(x='x', y='y', col='z', col_wrap=2, cmap='cool', subplot_kws=dict(axisbg='r')) for ax in g.axes.flat: try: # mpl V2 self.assertEqual(ax.get_facecolor()[0:3], mpl.colors.to_rgb('r')) except AttributeError: self.assertEqual(ax.get_axis_bgcolor(), 'r') def test_plot_size(self): self.darray[:, 0, 0].plot(figsize=(13, 5)) assert tuple(plt.gcf().get_size_inches()) == (13, 5) self.darray.plot(figsize=(13, 5)) assert tuple(plt.gcf().get_size_inches()) == (13, 5) self.darray.plot(size=5) assert plt.gcf().get_size_inches()[1] == 5 self.darray.plot(size=5, aspect=2) assert tuple(plt.gcf().get_size_inches()) == (10, 5) with self.assertRaisesRegexp(ValueError, 'cannot provide both'): self.darray.plot(ax=plt.gca(), figsize=(3, 4)) with self.assertRaisesRegexp(ValueError, 'cannot provide both'): self.darray.plot(size=5, figsize=(3, 4)) with self.assertRaisesRegexp(ValueError, 'cannot provide both'): self.darray.plot(size=5, ax=plt.gca()) with self.assertRaisesRegexp(ValueError, 'cannot provide `aspect`'): self.darray.plot(aspect=1) def test_convenient_facetgrid_4d(self): a = easy_array((10, 15, 2, 3)) d = DataArray(a, dims=['y', 'x', 'columns', 'rows']) g = d.plot(x='x', y='y', col='columns', row='rows') self.assertArrayEqual(g.axes.shape, [3, 2]) for ax in g.axes.flat: self.assertTrue(ax.has_data()) with self.assertRaisesRegexp(ValueError, '[Ff]acet'): d.plot(x='x', y='y', col='columns', ax=plt.gca()) class TestPlot1D(PlotTestCase): def setUp(self): d = [0, 1.1, 0, 2] self.darray = DataArray(d, coords={'period': range(len(d))}, dims='period') def test_xlabel_is_index_name(self): self.darray.plot() self.assertEqual('period', plt.gca().get_xlabel()) def test_no_label_name_on_y_axis(self): self.darray.plot() self.assertEqual('', plt.gca().get_ylabel()) def test_ylabel_is_data_name(self): self.darray.name = 'temperature' self.darray.plot() self.assertEqual(self.darray.name, plt.gca().get_ylabel()) def test_wrong_dims_raises_valueerror(self): twodims = DataArray(easy_array((2, 5))) with self.assertRaises(ValueError): twodims.plot.line() def test_format_string(self): self.darray.plot.line('ro') def test_can_pass_in_axis(self): self.pass_in_axis(self.darray.plot.line) def test_nonnumeric_index_raises_typeerror(self): a = DataArray([1, 2, 3], {'letter': ['a', 'b', 'c']}, dims='letter') with self.assertRaisesRegexp(TypeError, r'[Pp]lot'): a.plot.line() def test_primitive_returned(self): p = self.darray.plot.line() self.assertTrue(isinstance(p[0], mpl.lines.Line2D)) def test_plot_nans(self): self.darray[1] = np.nan self.darray.plot.line() def test_x_ticks_are_rotated_for_time(self): time = pd.date_range('2000-01-01', '2000-01-10') a = DataArray(np.arange(len(time)), [('t', time)]) a.plot.line() rotation = plt.gca().get_xticklabels()[0].get_rotation() self.assertFalse(rotation == 0) def test_slice_in_title(self): self.darray.coords['d'] = 10 self.darray.plot.line() title = plt.gca().get_title() self.assertEqual('d = 10', title) class TestPlotHistogram(PlotTestCase): def setUp(self): self.darray = DataArray(easy_array((2, 3, 4))) def test_3d_array(self): self.darray.plot.hist() def test_title_no_name(self): self.darray.plot.hist() self.assertEqual('', plt.gca().get_title()) def test_title_uses_name(self): self.darray.name = 'testpoints' self.darray.plot.hist() self.assertIn(self.darray.name, plt.gca().get_title()) def test_ylabel_is_count(self): self.darray.plot.hist() self.assertEqual('Count', plt.gca().get_ylabel()) def test_can_pass_in_kwargs(self): nbins = 5 self.darray.plot.hist(bins=nbins) self.assertEqual(nbins, len(plt.gca().patches)) def test_can_pass_in_axis(self): self.pass_in_axis(self.darray.plot.hist) def test_primitive_returned(self): h = self.darray.plot.hist() self.assertTrue(isinstance(h[-1][0], mpl.patches.Rectangle)) def test_plot_nans(self): self.darray[0, 0, 0] = np.nan self.darray.plot.hist() @requires_matplotlib class TestDetermineCmapParams(TestCase): def setUp(self): self.data = np.linspace(0, 1, num=100) def test_robust(self): cmap_params = _determine_cmap_params(self.data, robust=True) self.assertEqual(cmap_params['vmin'], np.percentile(self.data, 2)) self.assertEqual(cmap_params['vmax'], np.percentile(self.data, 98)) self.assertEqual(cmap_params['cmap'].name, 'viridis') self.assertEqual(cmap_params['extend'], 'both') self.assertIsNone(cmap_params['levels']) self.assertIsNone(cmap_params['norm']) def test_center(self): cmap_params = _determine_cmap_params(self.data, center=0.5) self.assertEqual(cmap_params['vmax'] - 0.5, 0.5 - cmap_params['vmin']) self.assertEqual(cmap_params['cmap'], 'RdBu_r') self.assertEqual(cmap_params['extend'], 'neither') self.assertIsNone(cmap_params['levels']) self.assertIsNone(cmap_params['norm']) def test_integer_levels(self): data = self.data + 1 # default is to cover full data range but with no guarantee on Nlevels for level in np.arange(2, 10, dtype=int): cmap_params = _determine_cmap_params(data, levels=level) self.assertEqual(cmap_params['vmin'], cmap_params['levels'][0]) self.assertEqual(cmap_params['vmax'], cmap_params['levels'][-1]) self.assertEqual(cmap_params['extend'], 'neither') # with min max we are more strict cmap_params = _determine_cmap_params(data, levels=5, vmin=0, vmax=5, cmap='Blues') self.assertEqual(cmap_params['vmin'], 0) self.assertEqual(cmap_params['vmax'], 5) self.assertEqual(cmap_params['vmin'], cmap_params['levels'][0]) self.assertEqual(cmap_params['vmax'], cmap_params['levels'][-1]) self.assertEqual(cmap_params['cmap'].name, 'Blues') self.assertEqual(cmap_params['extend'], 'neither') self.assertEqual(cmap_params['cmap'].N, 4) self.assertEqual(cmap_params['norm'].N, 5) cmap_params = _determine_cmap_params(data, levels=5, vmin=0.5, vmax=1.5) self.assertEqual(cmap_params['cmap'].name, 'viridis') self.assertEqual(cmap_params['extend'], 'max') cmap_params = _determine_cmap_params(data, levels=5, vmin=1.5) self.assertEqual(cmap_params['cmap'].name, 'viridis') self.assertEqual(cmap_params['extend'], 'min') cmap_params = _determine_cmap_params(data, levels=5, vmin=1.3, vmax=1.5) self.assertEqual(cmap_params['cmap'].name, 'viridis') self.assertEqual(cmap_params['extend'], 'both') def test_list_levels(self): data = self.data + 1 orig_levels = [0, 1, 2, 3, 4, 5] # vmin and vmax should be ignored if levels are explicitly provided cmap_params = _determine_cmap_params(data, levels=orig_levels, vmin=0, vmax=3) self.assertEqual(cmap_params['vmin'], 0) self.assertEqual(cmap_params['vmax'], 5) self.assertEqual(cmap_params['cmap'].N, 5) self.assertEqual(cmap_params['norm'].N, 6) for wrap_levels in [list, np.array, pd.Index, DataArray]: cmap_params = _determine_cmap_params( data, levels=wrap_levels(orig_levels)) self.assertArrayEqual(cmap_params['levels'], orig_levels) def test_divergentcontrol(self): neg = self.data - 0.1 pos = self.data # Default with positive data will be a normal cmap cmap_params = _determine_cmap_params(pos) self.assertEqual(cmap_params['vmin'], 0) self.assertEqual(cmap_params['vmax'], 1) self.assertEqual(cmap_params['cmap'].name, "viridis") # Default with negative data will be a divergent cmap cmap_params = _determine_cmap_params(neg) self.assertEqual(cmap_params['vmin'], -0.9) self.assertEqual(cmap_params['vmax'], 0.9) self.assertEqual(cmap_params['cmap'], "RdBu_r") # Setting vmin or vmax should prevent this only if center is false cmap_params = _determine_cmap_params(neg, vmin=-0.1, center=False) self.assertEqual(cmap_params['vmin'], -0.1) self.assertEqual(cmap_params['vmax'], 0.9) self.assertEqual(cmap_params['cmap'].name, "viridis") cmap_params = _determine_cmap_params(neg, vmax=0.5, center=False) self.assertEqual(cmap_params['vmin'], -0.1) self.assertEqual(cmap_params['vmax'], 0.5) self.assertEqual(cmap_params['cmap'].name, "viridis") # Setting center=False too cmap_params = _determine_cmap_params(neg, center=False) self.assertEqual(cmap_params['vmin'], -0.1) self.assertEqual(cmap_params['vmax'], 0.9) self.assertEqual(cmap_params['cmap'].name, "viridis") # However, I should still be able to set center and have a div cmap cmap_params = _determine_cmap_params(neg, center=0) self.assertEqual(cmap_params['vmin'], -0.9) self.assertEqual(cmap_params['vmax'], 0.9) self.assertEqual(cmap_params['cmap'], "RdBu_r") # Setting vmin or vmax alone will force symmetric bounds around center cmap_params = _determine_cmap_params(neg, vmin=-0.1) self.assertEqual(cmap_params['vmin'], -0.1) self.assertEqual(cmap_params['vmax'], 0.1) self.assertEqual(cmap_params['cmap'], "RdBu_r") cmap_params = _determine_cmap_params(neg, vmax=0.5) self.assertEqual(cmap_params['vmin'], -0.5) self.assertEqual(cmap_params['vmax'], 0.5) self.assertEqual(cmap_params['cmap'], "RdBu_r") cmap_params = _determine_cmap_params(neg, vmax=0.6, center=0.1) self.assertEqual(cmap_params['vmin'], -0.4) self.assertEqual(cmap_params['vmax'], 0.6) self.assertEqual(cmap_params['cmap'], "RdBu_r") # But this is only true if vmin or vmax are negative cmap_params = _determine_cmap_params(pos, vmin=-0.1) self.assertEqual(cmap_params['vmin'], -0.1) self.assertEqual(cmap_params['vmax'], 0.1) self.assertEqual(cmap_params['cmap'], "RdBu_r") cmap_params = _determine_cmap_params(pos, vmin=0.1) self.assertEqual(cmap_params['vmin'], 0.1) self.assertEqual(cmap_params['vmax'], 1) self.assertEqual(cmap_params['cmap'].name, "viridis") cmap_params = _determine_cmap_params(pos, vmax=0.5) self.assertEqual(cmap_params['vmin'], 0) self.assertEqual(cmap_params['vmax'], 0.5) self.assertEqual(cmap_params['cmap'].name, "viridis") # If both vmin and vmax are provided, output is non-divergent cmap_params = _determine_cmap_params(neg, vmin=-0.2, vmax=0.6) self.assertEqual(cmap_params['vmin'], -0.2) self.assertEqual(cmap_params['vmax'], 0.6) self.assertEqual(cmap_params['cmap'].name, "viridis") @requires_matplotlib class TestDiscreteColorMap(TestCase): def setUp(self): x = np.arange(start=0, stop=10, step=2) y = np.arange(start=9, stop=-7, step=-3) xy = np.dstack(np.meshgrid(x, y)) distance = np.linalg.norm(xy, axis=2) self.darray = DataArray(distance, list(zip(('y', 'x'), (y, x)))) self.data_min = distance.min() self.data_max = distance.max() def test_recover_from_seaborn_jet_exception(self): pal = _color_palette('jet', 4) self.assertTrue(type(pal) == np.ndarray) self.assertEqual(len(pal), 4) def test_build_discrete_cmap(self): for (cmap, levels, extend, filled) in [('jet', [0, 1], 'both', False), ('hot', [-4, 4], 'max', True)]: ncmap, cnorm = _build_discrete_cmap(cmap, levels, extend, filled) self.assertEqual(ncmap.N, len(levels) - 1) self.assertEqual(len(ncmap.colors), len(levels) - 1) self.assertEqual(cnorm.N, len(levels)) self.assertArrayEqual(cnorm.boundaries, levels) self.assertEqual(max(levels), cnorm.vmax) self.assertEqual(min(levels), cnorm.vmin) if filled: self.assertEqual(ncmap.colorbar_extend, extend) else: self.assertEqual(ncmap.colorbar_extend, 'max') def test_discrete_colormap_list_of_levels(self): for extend, levels in [('max', [-1, 2, 4, 8, 10]), ('both', [2, 5, 10, 11]), ('neither', [0, 5, 10, 15]), ('min', [2, 5, 10, 15])]: for kind in ['imshow', 'pcolormesh', 'contourf', 'contour']: primitive = getattr(self.darray.plot, kind)(levels=levels) self.assertArrayEqual(levels, primitive.norm.boundaries) self.assertEqual(max(levels), primitive.norm.vmax) self.assertEqual(min(levels), primitive.norm.vmin) if kind != 'contour': self.assertEqual(extend, primitive.cmap.colorbar_extend) else: self.assertEqual('max', primitive.cmap.colorbar_extend) self.assertEqual(len(levels) - 1, len(primitive.cmap.colors)) def test_discrete_colormap_int_levels(self): for extend, levels, vmin, vmax in [('neither', 7, None, None), ('neither', 7, None, 20), ('both', 7, 4, 8), ('min', 10, 4, 15)]: for kind in ['imshow', 'pcolormesh', 'contourf', 'contour']: primitive = getattr(self.darray.plot, kind)(levels=levels, vmin=vmin, vmax=vmax) self.assertGreaterEqual(levels, len(primitive.norm.boundaries) - 1) if vmax is None: self.assertGreaterEqual(primitive.norm.vmax, self.data_max) else: self.assertGreaterEqual(primitive.norm.vmax, vmax) if vmin is None: self.assertLessEqual(primitive.norm.vmin, self.data_min) else: self.assertLessEqual(primitive.norm.vmin, vmin) if kind != 'contour': self.assertEqual(extend, primitive.cmap.colorbar_extend) else: self.assertEqual('max', primitive.cmap.colorbar_extend) self.assertGreaterEqual(levels, len(primitive.cmap.colors)) def test_discrete_colormap_list_levels_and_vmin_or_vmax(self): levels = [0, 5, 10, 15] primitive = self.darray.plot(levels=levels, vmin=-3, vmax=20) self.assertEqual(primitive.norm.vmax, max(levels)) self.assertEqual(primitive.norm.vmin, min(levels)) class Common2dMixin: """ Common tests for 2d plotting go here. These tests assume that a staticmethod for `self.plotfunc` exists. Should have the same name as the method. """ def setUp(self): da = DataArray(easy_array( (10, 15), start=-1), dims=['y', 'x']) # add 2d coords ds = da.to_dataset(name='testvar') x, y = np.meshgrid(da.x.values, da.y.values) ds['x2d'] = DataArray(x, dims=['y', 'x']) ds['y2d'] = DataArray(y, dims=['y', 'x']) ds.set_coords(['x2d', 'y2d'], inplace=True) # set darray and plot method self.darray = ds.testvar self.plotmethod = getattr(self.darray.plot, self.plotfunc.__name__) def test_label_names(self): self.plotmethod() self.assertEqual('x', plt.gca().get_xlabel()) self.assertEqual('y', plt.gca().get_ylabel()) def test_1d_raises_valueerror(self): with self.assertRaisesRegexp(ValueError, r'DataArray must be 2d'): self.plotfunc(self.darray[0, :]) def test_3d_raises_valueerror(self): a = DataArray(easy_array((2, 3, 4))) with self.assertRaisesRegexp(ValueError, r'DataArray must be 2d'): self.plotfunc(a) def test_nonnumeric_index_raises_typeerror(self): a = DataArray(easy_array((3, 2)), coords=[['a', 'b', 'c'], ['d', 'e']]) with self.assertRaisesRegexp(TypeError, r'[Pp]lot'): self.plotfunc(a) def test_can_pass_in_axis(self): self.pass_in_axis(self.plotmethod) def test_xyincrease_false_changes_axes(self): self.plotmethod(xincrease=False, yincrease=False) xlim = plt.gca().get_xlim() ylim = plt.gca().get_ylim() diffs = xlim[0] - 14, xlim[1] - 0, ylim[0] - 9, ylim[1] - 0 self.assertTrue(all(abs(x) < 1 for x in diffs)) def test_xyincrease_true_changes_axes(self): self.plotmethod(xincrease=True, yincrease=True) xlim = plt.gca().get_xlim() ylim = plt.gca().get_ylim() diffs = xlim[0] - 0, xlim[1] - 14, ylim[0] - 0, ylim[1] - 9 self.assertTrue(all(abs(x) < 1 for x in diffs)) def test_plot_nans(self): x1 = self.darray[:5] x2 = self.darray.copy() x2[5:] = np.nan clim1 = self.plotfunc(x1).get_clim() clim2 = self.plotfunc(x2).get_clim() self.assertEqual(clim1, clim2) def test_viridis_cmap(self): cmap_name = self.plotmethod(cmap='viridis').get_cmap().name self.assertEqual('viridis', cmap_name) def test_default_cmap(self): cmap_name = self.plotmethod().get_cmap().name self.assertEqual('RdBu_r', cmap_name) cmap_name = self.plotfunc(abs(self.darray)).get_cmap().name self.assertEqual('viridis', cmap_name) def test_seaborn_palette_as_cmap(self): try: import seaborn cmap_name = self.plotmethod( levels=2, cmap='husl').get_cmap().name self.assertEqual('husl', cmap_name) except ImportError: pass def test_can_change_default_cmap(self): cmap_name = self.plotmethod(cmap='Blues').get_cmap().name self.assertEqual('Blues', cmap_name) def test_diverging_color_limits(self): artist = self.plotmethod() vmin, vmax = artist.get_clim() self.assertAlmostEqual(-vmin, vmax) def test_xy_strings(self): self.plotmethod('y', 'x') ax = plt.gca() self.assertEqual('y', ax.get_xlabel()) self.assertEqual('x', ax.get_ylabel()) def test_positional_coord_string(self): with self.assertRaisesRegexp(ValueError, 'cannot supply only one'): self.plotmethod('y') with self.assertRaisesRegexp(ValueError, 'cannot supply only one'): self.plotmethod(y='x') def test_bad_x_string_exception(self): with self.assertRaisesRegexp(ValueError, 'x and y must be coordinate'): self.plotmethod('not_a_real_dim', 'y') self.darray.coords['z'] = 100 with self.assertRaisesRegexp(ValueError, 'cannot supply only one'): self.plotmethod('z') def test_coord_strings(self): # 1d coords (same as dims) self.assertEqual({'x', 'y'}, set(self.darray.dims)) self.plotmethod(y='y', x='x') def test_non_linked_coords(self): # plot with coordinate names that are not dimensions self.darray.coords['newy'] = self.darray.y + 150 # Normal case, without transpose self.plotfunc(self.darray, x='x', y='newy') ax = plt.gca() self.assertEqual('x', ax.get_xlabel()) self.assertEqual('newy', ax.get_ylabel()) # ax limits might change between plotfuncs # simply ensure that these high coords were passed over self.assertTrue(np.min(ax.get_ylim()) > 100.) def test_non_linked_coords_transpose(self): # plot with coordinate names that are not dimensions, # and with transposed y and x axes # This used to raise an error with pcolormesh and contour # https://github.com/pydata/xarray/issues/788 self.darray.coords['newy'] = self.darray.y + 150 self.plotfunc(self.darray, x='newy', y='x') ax = plt.gca() self.assertEqual('newy', ax.get_xlabel()) self.assertEqual('x', ax.get_ylabel()) # ax limits might change between plotfuncs # simply ensure that these high coords were passed over self.assertTrue(np.min(ax.get_xlim()) > 100.) def test_default_title(self): a = DataArray(easy_array((4, 3, 2)), dims=['a', 'b', 'c']) a.coords['c'] = [0, 1] a.coords['d'] = u'foo' self.plotfunc(a.isel(c=1)) title = plt.gca().get_title() self.assertTrue('c = 1, d = foo' == title or 'd = foo, c = 1' == title) def test_colorbar_default_label(self): self.darray.name = 'testvar' self.plotmethod(add_colorbar=True) self.assertIn(self.darray.name, text_in_fig()) def test_no_labels(self): self.darray.name = 'testvar' self.plotmethod(add_labels=False) alltxt = text_in_fig() for string in ['x', 'y', 'testvar']: self.assertNotIn(string, alltxt) def test_colorbar_kwargs(self): # replace label self.darray.name = 'testvar' self.plotmethod(add_colorbar=True, cbar_kwargs={'label':'MyLabel'}) alltxt = text_in_fig() self.assertIn('MyLabel', alltxt) self.assertNotIn('testvar', alltxt) # you can use mapping types as well self.plotmethod(add_colorbar=True, cbar_kwargs=(('label', 'MyLabel'),)) alltxt = text_in_fig() self.assertIn('MyLabel', alltxt) self.assertNotIn('testvar', alltxt) # change cbar ax fig, (ax, cax) = plt.subplots(1, 2) self.plotmethod(ax=ax, cbar_ax=cax, add_colorbar=True, cbar_kwargs={'label':'MyBar'}) self.assertTrue(ax.has_data()) self.assertTrue(cax.has_data()) alltxt = text_in_fig() self.assertIn('MyBar', alltxt) self.assertNotIn('testvar', alltxt) # note that there are two ways to achieve this fig, (ax, cax) = plt.subplots(1, 2) self.plotmethod(ax=ax, add_colorbar=True, cbar_kwargs={'label':'MyBar', 'cax':cax}) self.assertTrue(ax.has_data()) self.assertTrue(cax.has_data()) alltxt = text_in_fig() self.assertIn('MyBar', alltxt) self.assertNotIn('testvar', alltxt) # see that no colorbar is respected self.plotmethod(add_colorbar=False) self.assertNotIn('testvar', text_in_fig()) # check that error is raised self.assertRaises(ValueError, self.plotmethod, add_colorbar=False, cbar_kwargs= {'label':'label'}) def test_verbose_facetgrid(self): a = easy_array((10, 15, 3)) d = DataArray(a, dims=['y', 'x', 'z']) g = xplt.FacetGrid(d, col='z') g.map_dataarray(self.plotfunc, 'x', 'y') for ax in g.axes.flat: self.assertTrue(ax.has_data()) def test_2d_function_and_method_signature_same(self): func_sig = inspect.getcallargs(self.plotfunc, self.darray) method_sig = inspect.getcallargs(self.plotmethod) del method_sig['_PlotMethods_obj'] del func_sig['darray'] self.assertEqual(func_sig, method_sig) def test_convenient_facetgrid(self): a = easy_array((10, 15, 4)) d = DataArray(a, dims=['y', 'x', 'z']) g = self.plotfunc(d, x='x', y='y', col='z', col_wrap=2) self.assertArrayEqual(g.axes.shape, [2, 2]) for (y, x), ax in np.ndenumerate(g.axes): self.assertTrue(ax.has_data()) if x == 0: self.assertEqual('y', ax.get_ylabel()) else: self.assertEqual('', ax.get_ylabel()) if y == 1: self.assertEqual('x', ax.get_xlabel()) else: self.assertEqual('', ax.get_xlabel()) # Infering labels g = self.plotfunc(d, col='z', col_wrap=2) self.assertArrayEqual(g.axes.shape, [2, 2]) for (y, x), ax in np.ndenumerate(g.axes): self.assertTrue(ax.has_data()) if x == 0: self.assertEqual('y', ax.get_ylabel()) else: self.assertEqual('', ax.get_ylabel()) if y == 1: self.assertEqual('x', ax.get_xlabel()) else: self.assertEqual('', ax.get_xlabel()) def test_convenient_facetgrid_4d(self): a = easy_array((10, 15, 2, 3)) d = DataArray(a, dims=['y', 'x', 'columns', 'rows']) g = self.plotfunc(d, x='x', y='y', col='columns', row='rows') self.assertArrayEqual(g.axes.shape, [3, 2]) for ax in g.axes.flat: self.assertTrue(ax.has_data()) def test_facetgrid_cmap(self): # Regression test for GH592 data = (np.random.random(size=(20, 25, 12)) + np.linspace(-3, 3, 12)) d = DataArray(data, dims=['x', 'y', 'time']) fg = d.plot.pcolormesh(col='time') # check that all color limits are the same self.assertTrue(len(set(m.get_clim() for m in fg._mappables)) == 1) # check that all colormaps are the same self.assertTrue(len(set(m.get_cmap().name for m in fg._mappables)) == 1) class TestContourf(Common2dMixin, PlotTestCase): plotfunc = staticmethod(xplt.contourf) def test_contourf_called(self): # Having both statements ensures the test works properly self.assertFalse(self.contourf_called(self.darray.plot.imshow)) self.assertTrue(self.contourf_called(self.darray.plot.contourf)) def test_primitive_artist_returned(self): artist = self.plotmethod() self.assertTrue(isinstance(artist, mpl.contour.QuadContourSet)) def test_extend(self): artist = self.plotmethod() self.assertEqual(artist.extend, 'neither') self.darray[0, 0] = -100 self.darray[-1, -1] = 100 artist = self.plotmethod(robust=True) self.assertEqual(artist.extend, 'both') self.darray[0, 0] = 0 self.darray[-1, -1] = 0 artist = self.plotmethod(vmin=-0, vmax=10) self.assertEqual(artist.extend, 'min') artist = self.plotmethod(vmin=-10, vmax=0) self.assertEqual(artist.extend, 'max') def test_2d_coord_names(self): self.plotmethod(x='x2d', y='y2d') # make sure labels came out ok ax = plt.gca() self.assertEqual('x2d', ax.get_xlabel()) self.assertEqual('y2d', ax.get_ylabel()) def test_levels(self): artist = self.plotmethod(levels=[-0.5, -0.4, 0.1]) self.assertEqual(artist.extend, 'both') artist = self.plotmethod(levels=3) self.assertEqual(artist.extend, 'neither') class TestContour(Common2dMixin, PlotTestCase): plotfunc = staticmethod(xplt.contour) def test_colors(self): # matplotlib cmap.colors gives an rgbA ndarray # when seaborn is used, instead we get an rgb tuple def _color_as_tuple(c): return tuple(c[:3]) artist = self.plotmethod(colors='k') self.assertEqual( _color_as_tuple(artist.cmap.colors[0]), (0.0, 0.0, 0.0)) artist = self.plotmethod(colors=['k', 'b']) self.assertEqual( _color_as_tuple(artist.cmap.colors[1]), (0.0, 0.0, 1.0)) artist = self.darray.plot.contour(levels=[-0.5, 0., 0.5, 1.], colors=['k', 'r', 'w', 'b']) self.assertEqual( _color_as_tuple(artist.cmap.colors[1]), (1.0, 0.0, 0.0)) self.assertEqual( _color_as_tuple(artist.cmap.colors[2]), (1.0, 1.0, 1.0)) # the last color is now under "over" self.assertEqual( _color_as_tuple(artist.cmap._rgba_over), (0.0, 0.0, 1.0)) def test_cmap_and_color_both(self): with self.assertRaises(ValueError): self.plotmethod(colors='k', cmap='RdBu') def list_of_colors_in_cmap_deprecated(self): with self.assertRaises(Exception): self.plotmethod(cmap=['k', 'b']) def test_2d_coord_names(self): self.plotmethod(x='x2d', y='y2d') # make sure labels came out ok ax = plt.gca() self.assertEqual('x2d', ax.get_xlabel()) self.assertEqual('y2d', ax.get_ylabel()) def test_single_level(self): # this used to raise an error, but not anymore since # add_colorbar defaults to false self.plotmethod(levels=[0.1]) self.plotmethod(levels=1) class TestPcolormesh(Common2dMixin, PlotTestCase): plotfunc = staticmethod(xplt.pcolormesh) def test_primitive_artist_returned(self): artist = self.plotmethod() self.assertTrue(isinstance(artist, mpl.collections.QuadMesh)) def test_everything_plotted(self): artist = self.plotmethod() self.assertEqual(artist.get_array().size, self.darray.size) def test_2d_coord_names(self): self.plotmethod(x='x2d', y='y2d') # make sure labels came out ok ax = plt.gca() self.assertEqual('x2d', ax.get_xlabel()) self.assertEqual('y2d', ax.get_ylabel()) def test_dont_infer_interval_breaks_for_cartopy(self): # Regression for GH 781 ax = plt.gca() # Simulate a Cartopy Axis setattr(ax, 'projection', True) artist = self.plotmethod(x='x2d', y='y2d', ax=ax) self.assertTrue(isinstance(artist, mpl.collections.QuadMesh)) # Let cartopy handle the axis limits and artist size self.assertTrue(artist.get_array().size <= self.darray.size) class TestImshow(Common2dMixin, PlotTestCase): plotfunc = staticmethod(xplt.imshow) def test_imshow_called(self): # Having both statements ensures the test works properly self.assertFalse(self.imshow_called(self.darray.plot.contourf)) self.assertTrue(self.imshow_called(self.darray.plot.imshow)) def test_xy_pixel_centered(self): self.darray.plot.imshow(yincrease=False) self.assertTrue(np.allclose([-0.5, 14.5], plt.gca().get_xlim())) self.assertTrue(np.allclose([9.5, -0.5], plt.gca().get_ylim())) def test_default_aspect_is_auto(self): self.darray.plot.imshow() self.assertEqual('auto', plt.gca().get_aspect()) def test_cannot_change_mpl_aspect(self): with self.assertRaisesRegexp(ValueError, 'not available in xarray'): self.darray.plot.imshow(aspect='equal') # with numbers we fall back to fig control self.darray.plot.imshow(size=5, aspect=2) self.assertEqual('auto', plt.gca().get_aspect()) assert tuple(plt.gcf().get_size_inches()) == (10, 5) def test_primitive_artist_returned(self): artist = self.plotmethod() self.assertTrue(isinstance(artist, mpl.image.AxesImage)) def test_seaborn_palette_needs_levels(self): try: import seaborn with self.assertRaises(ValueError): self.plotmethod(cmap='husl') except ImportError: pass def test_2d_coord_names(self): with self.assertRaisesRegexp(ValueError, 'requires 1D coordinates'): self.plotmethod(x='x2d', y='y2d') class TestFacetGrid(PlotTestCase): def setUp(self): d = easy_array((10, 15, 3)) self.darray = DataArray(d, dims=['y', 'x', 'z'], coords={'z': ['a', 'b', 'c']}) self.g = xplt.FacetGrid(self.darray, col='z') def test_no_args(self): self.g.map_dataarray(xplt.contourf, 'x', 'y') # Don't want colorbar labeled with 'None' alltxt = text_in_fig() self.assertNotIn('None', alltxt) for ax in self.g.axes.flat: self.assertTrue(ax.has_data()) # default font size should be small fontsize = ax.title.get_size() self.assertLessEqual(fontsize, 12) def test_names_appear_somewhere(self): self.darray.name = 'testvar' self.g.map_dataarray(xplt.contourf, 'x', 'y') for k, ax in zip('abc', self.g.axes.flat): self.assertEqual('z = {0}'.format(k), ax.get_title()) alltxt = text_in_fig() self.assertIn(self.darray.name, alltxt) for label in ['x', 'y']: self.assertIn(label, alltxt) def test_text_not_super_long(self): self.darray.coords['z'] = [100 * letter for letter in 'abc'] g = xplt.FacetGrid(self.darray, col='z') g.map_dataarray(xplt.contour, 'x', 'y') alltxt = text_in_fig() maxlen = max(len(txt) for txt in alltxt) self.assertLess(maxlen, 50) t0 = g.axes[0, 0].get_title() self.assertTrue(t0.endswith('...')) def test_colorbar(self): vmin = self.darray.values.min() vmax = self.darray.values.max() expected = np.array((vmin, vmax)) self.g.map_dataarray(xplt.imshow, 'x', 'y') for image in plt.gcf().findobj(mpl.image.AxesImage): clim = np.array(image.get_clim()) self.assertTrue(np.allclose(expected, clim)) self.assertEqual(1, len(find_possible_colorbars())) def test_empty_cell(self): g = xplt.FacetGrid(self.darray, col='z', col_wrap=2) g.map_dataarray(xplt.imshow, 'x', 'y') bottomright = g.axes[-1, -1] self.assertFalse(bottomright.has_data()) self.assertFalse(bottomright.get_visible()) def test_norow_nocol_error(self): with self.assertRaisesRegexp(ValueError, r'[Rr]ow'): xplt.FacetGrid(self.darray) def test_groups(self): self.g.map_dataarray(xplt.imshow, 'x', 'y') upperleft_dict = self.g.name_dicts[0, 0] upperleft_array = self.darray.loc[upperleft_dict] z0 = self.darray.isel(z=0) self.assertDataArrayEqual(upperleft_array, z0) def test_float_index(self): self.darray.coords['z'] = [0.1, 0.2, 0.4] g = xplt.FacetGrid(self.darray, col='z') g.map_dataarray(xplt.imshow, 'x', 'y') def test_nonunique_index_error(self): self.darray.coords['z'] = [0.1, 0.2, 0.2] with self.assertRaisesRegexp(ValueError, r'[Uu]nique'): xplt.FacetGrid(self.darray, col='z') def test_robust(self): z = np.zeros((20, 20, 2)) darray = DataArray(z, dims=['y', 'x', 'z']) darray[:, :, 1] = 1 darray[2, 0, 0] = -1000 darray[3, 0, 0] = 1000 g = xplt.FacetGrid(darray, col='z') g.map_dataarray(xplt.imshow, 'x', 'y', robust=True) # Color limits should be 0, 1 # The largest number displayed in the figure should be less than 21 numbers = set() alltxt = text_in_fig() for txt in alltxt: try: numbers.add(float(txt)) except ValueError: pass largest = max(abs(x) for x in numbers) self.assertLess(largest, 21) def test_can_set_vmin_vmax(self): vmin, vmax = 50.0, 1000.0 expected = np.array((vmin, vmax)) self.g.map_dataarray(xplt.imshow, 'x', 'y', vmin=vmin, vmax=vmax) for image in plt.gcf().findobj(mpl.image.AxesImage): clim = np.array(image.get_clim()) self.assertTrue(np.allclose(expected, clim)) def test_can_set_norm(self): norm = mpl.colors.SymLogNorm(0.1) self.g.map_dataarray(xplt.imshow, 'x', 'y', norm=norm) for image in plt.gcf().findobj(mpl.image.AxesImage): self.assertIs(image.norm, norm) def test_figure_size(self): self.assertArrayEqual(self.g.fig.get_size_inches(), (10, 3)) g = xplt.FacetGrid(self.darray, col='z', size=6) self.assertArrayEqual(g.fig.get_size_inches(), (19, 6)) g = self.darray.plot.imshow(col='z', size=6) self.assertArrayEqual(g.fig.get_size_inches(), (19, 6)) g = xplt.FacetGrid(self.darray, col='z', size=4, aspect=0.5) self.assertArrayEqual(g.fig.get_size_inches(), (7, 4)) g = xplt.FacetGrid(self.darray, col='z', figsize=(9, 4)) self.assertArrayEqual(g.fig.get_size_inches(), (9, 4)) with self.assertRaisesRegexp(ValueError, "cannot provide both"): g = xplt.plot(self.darray, row=2, col='z', figsize=(6, 4), size=6) with self.assertRaisesRegexp(ValueError, "Can't use"): g = xplt.plot(self.darray, row=2, col='z', ax=plt.gca(), size=6) def test_num_ticks(self): nticks = 99 maxticks = nticks + 1 self.g.map_dataarray(xplt.imshow, 'x', 'y') self.g.set_ticks(max_xticks=nticks, max_yticks=nticks) for ax in self.g.axes.flat: xticks = len(ax.get_xticks()) yticks = len(ax.get_yticks()) self.assertLessEqual(xticks, maxticks) self.assertLessEqual(yticks, maxticks) self.assertGreaterEqual(xticks, nticks / 2.0) self.assertGreaterEqual(yticks, nticks / 2.0) def test_map(self): self.g.map(plt.contourf, 'x', 'y', Ellipsis) self.g.map(lambda: None) def test_map_dataset(self): g = xplt.FacetGrid(self.darray.to_dataset(name='foo'), col='z') g.map(plt.contourf, 'x', 'y', 'foo') alltxt = text_in_fig() for label in ['x', 'y']: self.assertIn(label, alltxt) # everything has a label self.assertNotIn('None', alltxt) # colorbar can't be inferred automatically self.assertNotIn('foo', alltxt) self.assertEqual(0, len(find_possible_colorbars())) g.add_colorbar(label='colors!') self.assertIn('colors!', text_in_fig()) self.assertEqual(1, len(find_possible_colorbars())) def test_set_axis_labels(self): g = self.g.map_dataarray(xplt.contourf, 'x', 'y') g.set_axis_labels('longitude', 'latitude') alltxt = text_in_fig() for label in ['longitude', 'latitude']: self.assertIn(label, alltxt) def test_facetgrid_colorbar(self): a = easy_array((10, 15, 4)) d = DataArray(a, dims=['y', 'x', 'z'], name='foo') d.plot.imshow(x='x', y='y', col='z') self.assertEqual(1, len(find_possible_colorbars())) d.plot.imshow(x='x', y='y', col='z', add_colorbar=True) self.assertEqual(1, len(find_possible_colorbars())) d.plot.imshow(x='x', y='y', col='z', add_colorbar=False) self.assertEqual(0, len(find_possible_colorbars())) def test_facetgrid_polar(self): # test if polar projection in FacetGrid does not raise an exception self.darray.plot.pcolormesh(col='z', subplot_kws=dict(projection='polar'), sharex=False, sharey=False) class TestFacetGrid4d(PlotTestCase): def setUp(self): a = easy_array((10, 15, 3, 2)) darray = DataArray(a, dims=['y', 'x', 'col', 'row']) darray.coords['col'] = np.array(['col' + str(x) for x in darray.coords['col'].values]) darray.coords['row'] = np.array(['row' + str(x) for x in darray.coords['row'].values]) self.darray = darray def test_default_labels(self): g = xplt.FacetGrid(self.darray, col='col', row='row') self.assertEqual((2, 3), g.axes.shape) g.map_dataarray(xplt.imshow, 'x', 'y') # Rightmost column should be labeled for label, ax in zip(self.darray.coords['row'].values, g.axes[:, -1]): self.assertTrue(substring_in_axes(label, ax)) # Top row should be labeled for label, ax in zip(self.darray.coords['col'].values, g.axes[0, :]): self.assertTrue(substring_in_axes(label, ax)) �������������������������������������������������������������������������������������������������������������������������������������xarray-0.9.1/xarray/tests/test_tutorial.py����������������������������������������������������������0000644�1311115�0011610�00000001423�13043511243�021047� 0����������������������������������������������������������������������������������������������������ustar �shoyer��������������������������eng�����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������from __future__ import absolute_import from __future__ import division from __future__ import print_function import os from xarray import tutorial, DataArray from xarray.core.pycompat import suppress from . import TestCase, unittest @unittest.skip('TODO: make this conditional on network availability') class Test_load_dataset(TestCase): def setUp(self): self.testfile = 'tiny' self.testfilepath = os.path.expanduser(os.sep.join( ('~', '.xarray_tutorial_data', self.testfile))) with suppress(OSError): os.remove(self.testfilepath) def test_download_from_github(self): ds = tutorial.load_dataset(self.testfile) tiny = DataArray(range(5), name='tiny').to_dataset() self.assertDatasetIdentical(ds, tiny) ���������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������xarray-0.9.1/xarray/tests/test_ufuncs.py������������������������������������������������������������0000644�1311115�0011610�00000004547�13043511243�020521� 0����������������������������������������������������������������������������������������������������ustar �shoyer��������������������������eng�����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������from __future__ import absolute_import from __future__ import division from __future__ import print_function import pickle import numpy as np import xarray.ufuncs as xu import xarray as xr from . import TestCase class TestOps(TestCase): def assertIdentical(self, a, b): assert type(a) is type(b) or (float(a) == float(b)) try: assert a.identical(b), (a, b) except AttributeError: self.assertArrayEqual(a, b) def test_unary(self): args = [0, np.zeros(2), xr.Variable(['x'], [0, 0]), xr.DataArray([0, 0], dims='x'), xr.Dataset({'y': ('x', [0, 0])})] for a in args: self.assertIdentical(a + 1, xu.cos(a)) def test_binary(self): args = [0, np.zeros(2), xr.Variable(['x'], [0, 0]), xr.DataArray([0, 0], dims='x'), xr.Dataset({'y': ('x', [0, 0])})] for n, t1 in enumerate(args): for t2 in args[n:]: self.assertIdentical(t2 + 1, xu.maximum(t1, t2 + 1)) self.assertIdentical(t2 + 1, xu.maximum(t2, t1 + 1)) self.assertIdentical(t2 + 1, xu.maximum(t1 + 1, t2)) self.assertIdentical(t2 + 1, xu.maximum(t2 + 1, t1)) def test_groupby(self): ds = xr.Dataset({'a': ('x', [0, 0, 0])}, {'c': ('x', [0, 0, 1])}) ds_grouped = ds.groupby('c') group_mean = ds_grouped.mean('x') arr_grouped = ds['a'].groupby('c') self.assertIdentical(ds, xu.maximum(ds_grouped, group_mean)) self.assertIdentical(ds, xu.maximum(group_mean, ds_grouped)) self.assertIdentical(ds, xu.maximum(arr_grouped, group_mean)) self.assertIdentical(ds, xu.maximum(group_mean, arr_grouped)) self.assertIdentical(ds, xu.maximum(ds_grouped, group_mean['a'])) self.assertIdentical(ds, xu.maximum(group_mean['a'], ds_grouped)) self.assertIdentical(ds.a, xu.maximum(arr_grouped, group_mean.a)) self.assertIdentical(ds.a, xu.maximum(group_mean.a, arr_grouped)) with self.assertRaisesRegexp(TypeError, 'only support binary ops'): xu.maximum(ds.a.variable, ds_grouped) def test_pickle(self): a = 1.0 cos_pickled = pickle.loads(pickle.dumps(xu.cos)) self.assertIdentical(cos_pickled(a), xu.cos(a)) ���������������������������������������������������������������������������������������������������������������������������������������������������������xarray-0.9.1/xarray/tests/test_utils.py�������������������������������������������������������������0000644�1311115�0011610�00000014212�13043511243�020344� 0����������������������������������������������������������������������������������������������������ustar �shoyer��������������������������eng�����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������from __future__ import absolute_import from __future__ import division from __future__ import print_function import pickle import pytest import numpy as np import pandas as pd from xarray.core import ops, utils from xarray.core.pycompat import OrderedDict from . import TestCase class TestAlias(TestCase): def test(self): def new_method(): pass old_method = utils.alias(new_method, 'old_method') assert 'deprecated' in old_method.__doc__ with self.assertWarns('deprecated'): old_method() class TestSafeCastToIndex(TestCase): def test(self): dates = pd.date_range('2000-01-01', periods=10) x = np.arange(5) td = x * np.timedelta64(1, 'D') for expected, array in [ (dates, dates.values), (pd.Index(x, dtype=object), x.astype(object)), (pd.Index(td), td), (pd.Index(td, dtype=object), td.astype(object)), ]: actual = utils.safe_cast_to_index(array) self.assertArrayEqual(expected, actual) self.assertEqual(expected.dtype, actual.dtype) def test_multiindex_from_product_levels(): result = utils.multiindex_from_product_levels([['b', 'a'], [1, 3, 2]]) np.testing.assert_array_equal( result.labels, [[0, 0, 0, 1, 1, 1], [0, 1, 2, 0, 1, 2]]) np.testing.assert_array_equal(result.levels[0], ['b', 'a']) np.testing.assert_array_equal(result.levels[1], [1, 3, 2]) other = pd.MultiIndex.from_product([['b', 'a'], [1, 3, 2]]) np.testing.assert_array_equal(result.values, other.values) class TestArrayEquiv(TestCase): def test_0d(self): # verify our work around for pd.isnull not working for 0-dimensional # object arrays self.assertTrue(ops.array_equiv(0, np.array(0, dtype=object))) self.assertTrue( ops.array_equiv(np.nan, np.array(np.nan, dtype=object))) self.assertFalse( ops.array_equiv(0, np.array(1, dtype=object))) class TestDictionaries(TestCase): def setUp(self): self.x = {'a': 'A', 'b': 'B'} self.y = {'c': 'C', 'b': 'B'} self.z = {'a': 'Z'} def test_equivalent(self): self.assertTrue(utils.equivalent(0, 0)) self.assertTrue(utils.equivalent(np.nan, np.nan)) self.assertTrue(utils.equivalent(0, np.array(0.0))) self.assertTrue(utils.equivalent([0], np.array([0]))) self.assertTrue(utils.equivalent(np.array([0]), [0])) self.assertTrue(utils.equivalent(np.arange(3), 1.0 * np.arange(3))) self.assertFalse(utils.equivalent(0, np.zeros(3))) def test_safe(self): # should not raise exception: utils.update_safety_check(self.x, self.y) def test_unsafe(self): with self.assertRaises(ValueError): utils.update_safety_check(self.x, self.z) def test_ordered_dict_intersection(self): self.assertEqual({'b': 'B'}, utils.ordered_dict_intersection(self.x, self.y)) self.assertEqual({}, utils.ordered_dict_intersection(self.x, self.z)) def test_dict_equiv(self): x = OrderedDict() x['a'] = 3 x['b'] = np.array([1, 2, 3]) y = OrderedDict() y['b'] = np.array([1.0, 2.0, 3.0]) y['a'] = 3 self.assertTrue(utils.dict_equiv(x, y)) # two nparrays are equal y['b'] = [1, 2, 3] # np.array not the same as a list self.assertTrue(utils.dict_equiv(x, y)) # nparray == list x['b'] = [1.0, 2.0, 3.0] self.assertTrue(utils.dict_equiv(x, y)) # list vs. list x['c'] = None self.assertFalse(utils.dict_equiv(x, y)) # new key in x x['c'] = np.nan y['c'] = np.nan self.assertTrue(utils.dict_equiv(x, y)) # as intended, nan is nan x['c'] = np.inf y['c'] = np.inf self.assertTrue(utils.dict_equiv(x, y)) # inf == inf y = dict(y) self.assertTrue(utils.dict_equiv(x, y)) # different dictionary types are fine y['b'] = 3 * np.arange(3) self.assertFalse(utils.dict_equiv(x, y)) # not equal when arrays differ def test_frozen(self): x = utils.Frozen(self.x) with self.assertRaises(TypeError): x['foo'] = 'bar' with self.assertRaises(TypeError): del x['a'] with self.assertRaises(AttributeError): x.update(self.y) self.assertEqual(x.mapping, self.x) self.assertIn(repr(x), ("Frozen({'a': 'A', 'b': 'B'})", "Frozen({'b': 'B', 'a': 'A'})")) def test_sorted_keys_dict(self): x = {'a': 1, 'b': 2, 'c': 3} y = utils.SortedKeysDict(x) self.assertItemsEqual(y, ['a', 'b', 'c']) self.assertEqual(repr(utils.SortedKeysDict()), "SortedKeysDict({})") def test_chain_map(self): m = utils.ChainMap({'x': 0, 'y': 1}, {'x': -100, 'z': 2}) self.assertIn('x', m) self.assertIn('y', m) self.assertIn('z', m) self.assertEqual(m['x'], 0) self.assertEqual(m['y'], 1) self.assertEqual(m['z'], 2) m['x'] = 100 self.assertEqual(m['x'], 100) self.assertEqual(m.maps[0]['x'], 100) self.assertItemsEqual(['x', 'y', 'z'], m) class Test_is_uniform_and_sorted(TestCase): def test_sorted_uniform(self): self.assertTrue(utils.is_uniform_spaced(np.arange(5))) def test_sorted_not_uniform(self): self.assertEqual(False, utils.is_uniform_spaced([-2, 1, 89])) def test_not_sorted_uniform(self): self.assertEqual(False, utils.is_uniform_spaced([1, -1, 3])) def test_not_sorted_not_uniform(self): self.assertEqual(False, utils.is_uniform_spaced([4, 1, 89])) def test_two_numbers(self): self.assertTrue(utils.is_uniform_spaced([0, 1.7])) def test_relative_tolerance(self): self.assertTrue(utils.is_uniform_spaced([0, 0.97, 2], rtol=0.1)) class Test_hashable(TestCase): def test_hashable(self): for v in [False, 1, (2, ), (3, 4), 'four']: self.assertTrue(utils.hashable(v)) for v in [[5, 6], ['seven', '8'], {9: 'ten'}]: self.assertFalse(utils.hashable(v)) ��������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������xarray-0.9.1/xarray/tests/test_variable.py����������������������������������������������������������0000644�1311115�0011610�00000146546�13043511243�021011� 0����������������������������������������������������������������������������������������������������ustar �shoyer��������������������������eng�����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������from __future__ import absolute_import from __future__ import division from __future__ import print_function from collections import namedtuple from copy import copy, deepcopy from datetime import datetime, timedelta from textwrap import dedent import pytest from distutils.version import LooseVersion import numpy as np import pytz import pandas as pd from xarray import Variable, IndexVariable, Coordinate, Dataset from xarray.core import indexing from xarray.core.variable import as_variable, as_compatible_data from xarray.core.indexing import PandasIndexAdapter, LazilyIndexedArray from xarray.core.pycompat import PY3, OrderedDict from xarray.core.common import full_like, zeros_like, ones_like from . import TestCase, source_ndarray, requires_dask class VariableSubclassTestCases(object): def test_properties(self): data = 0.5 * np.arange(10) v = self.cls(['time'], data, {'foo': 'bar'}) self.assertEqual(v.dims, ('time',)) self.assertArrayEqual(v.values, data) self.assertEqual(v.dtype, float) self.assertEqual(v.shape, (10,)) self.assertEqual(v.size, 10) self.assertEqual(v.sizes, {'time': 10}) self.assertEqual(v.nbytes, 80) self.assertEqual(v.ndim, 1) self.assertEqual(len(v), 10) self.assertEqual(v.attrs, {'foo': u'bar'}) def test_attrs(self): v = self.cls(['time'], 0.5 * np.arange(10)) self.assertEqual(v.attrs, {}) attrs = {'foo': 'bar'} v.attrs = attrs self.assertEqual(v.attrs, attrs) self.assertIsInstance(v.attrs, OrderedDict) v.attrs['foo'] = 'baz' self.assertEqual(v.attrs['foo'], 'baz') def test_getitem_dict(self): v = self.cls(['x'], np.random.randn(5)) actual = v[{'x': 0}] expected = v[0] self.assertVariableIdentical(expected, actual) def _assertIndexedLikeNDArray(self, variable, expected_value0, expected_dtype=None): """Given a 1-dimensional variable, verify that the variable is indexed like a numpy.ndarray. """ self.assertEqual(variable[0].shape, ()) self.assertEqual(variable[0].ndim, 0) self.assertEqual(variable[0].size, 1) # test identity self.assertTrue(variable.equals(variable.copy())) self.assertTrue(variable.identical(variable.copy())) # check value is equal for both ndarray and Variable self.assertEqual(variable.values[0], expected_value0) self.assertEqual(variable[0].values, expected_value0) # check type or dtype is consistent for both ndarray and Variable if expected_dtype is None: # check output type instead of array dtype self.assertEqual(type(variable.values[0]), type(expected_value0)) self.assertEqual(type(variable[0].values), type(expected_value0)) elif expected_dtype is not False: self.assertEqual(variable.values[0].dtype, expected_dtype) self.assertEqual(variable[0].values.dtype, expected_dtype) def test_index_0d_int(self): for value, dtype in [(0, np.int_), (np.int32(0), np.int32)]: x = self.cls(['x'], [value]) self._assertIndexedLikeNDArray(x, value, dtype) def test_index_0d_float(self): for value, dtype in [(0.5, np.float_), (np.float32(0.5), np.float32)]: x = self.cls(['x'], [value]) self._assertIndexedLikeNDArray(x, value, dtype) def test_index_0d_string(self): for value, dtype in [('foo', np.dtype('U3' if PY3 else 'S3')), (u'foo', np.dtype('U3'))]: x = self.cls(['x'], [value]) self._assertIndexedLikeNDArray(x, value, dtype) def test_index_0d_datetime(self): d = datetime(2000, 1, 1) x = self.cls(['x'], [d]) self._assertIndexedLikeNDArray(x, np.datetime64(d)) x = self.cls(['x'], [np.datetime64(d)]) self._assertIndexedLikeNDArray(x, np.datetime64(d), 'datetime64[ns]') x = self.cls(['x'], pd.DatetimeIndex([d])) self._assertIndexedLikeNDArray(x, np.datetime64(d), 'datetime64[ns]') def test_index_0d_timedelta64(self): td = timedelta(hours=1) x = self.cls(['x'], [np.timedelta64(td)]) self._assertIndexedLikeNDArray(x, np.timedelta64(td), 'timedelta64[ns]') x = self.cls(['x'], pd.to_timedelta([td])) self._assertIndexedLikeNDArray(x, np.timedelta64(td), 'timedelta64[ns]') def test_index_0d_not_a_time(self): d = np.datetime64('NaT', 'ns') x = self.cls(['x'], [d]) self._assertIndexedLikeNDArray(x, d) def test_index_0d_object(self): class HashableItemWrapper(object): def __init__(self, item): self.item = item def __eq__(self, other): return self.item == other.item def __hash__(self): return hash(self.item) def __repr__(self): return '%s(item=%r)' % (type(self).__name__, self.item) item = HashableItemWrapper((1, 2, 3)) x = self.cls('x', [item]) self._assertIndexedLikeNDArray(x, item, expected_dtype=False) def test_0d_object_array_with_list(self): listarray = np.empty((1,), dtype=object) listarray[0] = [1, 2, 3] x = self.cls('x', listarray) assert x.data == listarray assert x[0].data == listarray.squeeze() assert x.squeeze().data == listarray.squeeze() def test_index_and_concat_datetime(self): # regression test for #125 date_range = pd.date_range('2011-09-01', periods=10) for dates in [date_range, date_range.values, date_range.to_pydatetime()]: expected = self.cls('t', dates) for times in [[expected[i] for i in range(10)], [expected[i:(i + 1)] for i in range(10)], [expected[[i]] for i in range(10)]]: actual = Variable.concat(times, 't') self.assertEqual(expected.dtype, actual.dtype) self.assertArrayEqual(expected, actual) def test_0d_time_data(self): # regression test for #105 x = self.cls('time', pd.date_range('2000-01-01', periods=5)) expected = np.datetime64('2000-01-01T00Z', 'ns') self.assertEqual(x[0].values, expected) def test_datetime64_conversion(self): times = pd.date_range('2000-01-01', periods=3) for values, preserve_source in [ (times, True), (times.values, True), (times.values.astype('datetime64[s]'), False), (times.to_pydatetime(), False), ]: v = self.cls(['t'], values) self.assertEqual(v.dtype, np.dtype('datetime64[ns]')) self.assertArrayEqual(v.values, times.values) self.assertEqual(v.values.dtype, np.dtype('datetime64[ns]')) same_source = source_ndarray(v.values) is source_ndarray(values) assert preserve_source == same_source def test_timedelta64_conversion(self): times = pd.timedelta_range(start=0, periods=3) for values, preserve_source in [ (times, True), (times.values, True), (times.values.astype('timedelta64[s]'), False), (times.to_pytimedelta(), False), ]: v = self.cls(['t'], values) self.assertEqual(v.dtype, np.dtype('timedelta64[ns]')) self.assertArrayEqual(v.values, times.values) self.assertEqual(v.values.dtype, np.dtype('timedelta64[ns]')) same_source = source_ndarray(v.values) is source_ndarray(values) assert preserve_source == same_source def test_object_conversion(self): data = np.arange(5).astype(str).astype(object) actual = self.cls('x', data) self.assertEqual(actual.dtype, data.dtype) def test_pandas_data(self): v = self.cls(['x'], pd.Series([0, 1, 2], index=[3, 2, 1])) self.assertVariableIdentical(v, v[[0, 1, 2]]) v = self.cls(['x'], pd.Index([0, 1, 2])) self.assertEqual(v[0].values, v.values[0]) def test_pandas_period_index(self): v = self.cls(['x'], pd.period_range(start='2000', periods=20, freq='B')) self.assertEqual(v[0], pd.Period('2000', freq='B')) assert "Period('2000-01-03', 'B')" in repr(v) def test_1d_math(self): x = 1.0 * np.arange(5) y = np.ones(5) # should we need `.to_base_variable()`? # probably a break that `+v` changes type? v = self.cls(['x'], x) base_v = v.to_base_variable() # unary ops self.assertVariableIdentical(base_v, +v) self.assertVariableIdentical(base_v, abs(v)) self.assertArrayEqual((-v).values, -x) # binary ops with numbers self.assertVariableIdentical(base_v, v + 0) self.assertVariableIdentical(base_v, 0 + v) self.assertVariableIdentical(base_v, v * 1) self.assertArrayEqual((v > 2).values, x > 2) self.assertArrayEqual((0 == v).values, 0 == x) self.assertArrayEqual((v - 1).values, x - 1) self.assertArrayEqual((1 - v).values, 1 - x) # binary ops with numpy arrays self.assertArrayEqual((v * x).values, x ** 2) self.assertArrayEqual((x * v).values, x ** 2) self.assertArrayEqual(v - y, v - 1) self.assertArrayEqual(y - v, 1 - v) # verify attributes are dropped v2 = self.cls(['x'], x, {'units': 'meters'}) self.assertVariableIdentical(base_v, +v2) # binary ops with all variables self.assertArrayEqual(v + v, 2 * v) w = self.cls(['x'], y, {'foo': 'bar'}) self.assertVariableIdentical(v + w, self.cls(['x'], x + y).to_base_variable()) self.assertArrayEqual((v * w).values, x * y) # something complicated self.assertArrayEqual((v ** 2 * w - 1 + x).values, x ** 2 * y - 1 + x) # make sure dtype is preserved (for Index objects) self.assertEqual(float, (+v).dtype) self.assertEqual(float, (+v).values.dtype) self.assertEqual(float, (0 + v).dtype) self.assertEqual(float, (0 + v).values.dtype) # check types of returned data self.assertIsInstance(+v, Variable) self.assertNotIsInstance(+v, IndexVariable) self.assertIsInstance(0 + v, Variable) self.assertNotIsInstance(0 + v, IndexVariable) def test_1d_reduce(self): x = np.arange(5) v = self.cls(['x'], x) actual = v.sum() expected = Variable((), 10) self.assertVariableIdentical(expected, actual) self.assertIs(type(actual), Variable) def test_array_interface(self): x = np.arange(5) v = self.cls(['x'], x) self.assertArrayEqual(np.asarray(v), x) # test patched in methods self.assertArrayEqual(v.astype(float), x.astype(float)) # think this is a break, that argsort changes the type self.assertVariableIdentical(v.argsort(), v.to_base_variable()) self.assertVariableIdentical(v.clip(2, 3), self.cls('x', x.clip(2, 3)).to_base_variable()) # test ufuncs self.assertVariableIdentical(np.sin(v), self.cls(['x'], np.sin(x)).to_base_variable()) self.assertIsInstance(np.sin(v), Variable) self.assertNotIsInstance(np.sin(v), IndexVariable) def example_1d_objects(self): for data in [range(3), 0.5 * np.arange(3), 0.5 * np.arange(3, dtype=np.float32), pd.date_range('2000-01-01', periods=3), np.array(['a', 'b', 'c'], dtype=object)]: yield (self.cls('x', data), data) def test___array__(self): for v, data in self.example_1d_objects(): self.assertArrayEqual(v.values, np.asarray(data)) self.assertArrayEqual(np.asarray(v), np.asarray(data)) self.assertEqual(v[0].values, np.asarray(data)[0]) self.assertEqual(np.asarray(v[0]), np.asarray(data)[0]) def test_equals_all_dtypes(self): for v, _ in self.example_1d_objects(): v2 = v.copy() self.assertTrue(v.equals(v2)) self.assertTrue(v.identical(v2)) self.assertTrue(v.no_conflicts(v2)) self.assertTrue(v[0].equals(v2[0])) self.assertTrue(v[0].identical(v2[0])) self.assertTrue(v[0].no_conflicts(v2[0])) self.assertTrue(v[:2].equals(v2[:2])) self.assertTrue(v[:2].identical(v2[:2])) self.assertTrue(v[:2].no_conflicts(v2[:2])) def test_eq_all_dtypes(self): # ensure that we don't choke on comparisons for which numpy returns # scalars expected = Variable('x', 3 * [False]) for v, _ in self.example_1d_objects(): actual = 'z' == v self.assertVariableIdentical(expected, actual) actual = ~('z' != v) self.assertVariableIdentical(expected, actual) def test_encoding_preserved(self): expected = self.cls('x', range(3), {'foo': 1}, {'bar': 2}) for actual in [expected.T, expected[...], expected.squeeze(), expected.isel(x=slice(None)), expected.expand_dims({'x': 3}), expected.copy(deep=True), expected.copy(deep=False)]: self.assertVariableIdentical(expected.to_base_variable(), actual.to_base_variable()) self.assertEqual(expected.encoding, actual.encoding) def test_concat(self): x = np.arange(5) y = np.arange(5, 10) v = self.cls(['a'], x) w = self.cls(['a'], y) self.assertVariableIdentical(Variable(['b', 'a'], np.array([x, y])), Variable.concat([v, w], 'b')) self.assertVariableIdentical(Variable(['b', 'a'], np.array([x, y])), Variable.concat((v, w), 'b')) self.assertVariableIdentical(Variable(['b', 'a'], np.array([x, y])), Variable.concat((v, w), 'b')) with self.assertRaisesRegexp(ValueError, 'inconsistent dimensions'): Variable.concat([v, Variable(['c'], y)], 'b') # test indexers actual = Variable.concat( [v, w], positions=[np.arange(0, 10, 2), np.arange(1, 10, 2)], dim='a') expected = Variable('a', np.array([x, y]).ravel(order='F')) self.assertVariableIdentical(expected, actual) # test concatenating along a dimension v = Variable(['time', 'x'], np.random.random((10, 8))) self.assertVariableIdentical(v, Variable.concat([v[:5], v[5:]], 'time')) self.assertVariableIdentical(v, Variable.concat([v[:5], v[5:6], v[6:]], 'time')) self.assertVariableIdentical(v, Variable.concat([v[:1], v[1:]], 'time')) # test dimension order self.assertVariableIdentical(v, Variable.concat([v[:, :5], v[:, 5:]], 'x')) with self.assertRaisesRegexp(ValueError, 'all input arrays must have'): Variable.concat([v[:, 0], v[:, 1:]], 'x') def test_concat_attrs(self): # different or conflicting attributes should be removed v = self.cls('a', np.arange(5), {'foo': 'bar'}) w = self.cls('a', np.ones(5)) expected = self.cls('a', np.concatenate([np.arange(5), np.ones(5)])).to_base_variable() self.assertVariableIdentical(expected, Variable.concat([v, w], 'a')) w.attrs['foo'] = 2 self.assertVariableIdentical(expected, Variable.concat([v, w], 'a')) w.attrs['foo'] = 'bar' expected.attrs['foo'] = 'bar' self.assertVariableIdentical(expected, Variable.concat([v, w], 'a')) def test_concat_fixed_len_str(self): # regression test for #217 for kind in ['S', 'U']: x = self.cls('animal', np.array(['horse'], dtype=kind)) y = self.cls('animal', np.array(['aardvark'], dtype=kind)) actual = Variable.concat([x, y], 'animal') expected = Variable( 'animal', np.array(['horse', 'aardvark'], dtype=kind)) self.assertVariableEqual(expected, actual) def test_concat_number_strings(self): # regression test for #305 a = self.cls('x', ['0', '1', '2']) b = self.cls('x', ['3', '4']) actual = Variable.concat([a, b], dim='x') expected = Variable('x', np.arange(5).astype(str).astype(object)) self.assertVariableIdentical(expected, actual) self.assertEqual(expected.dtype, object) self.assertEqual(type(expected.values[0]), str) def test_copy(self): v = self.cls('x', 0.5 * np.arange(10), {'foo': 'bar'}) for deep in [True, False]: w = v.copy(deep=deep) self.assertIs(type(v), type(w)) self.assertVariableIdentical(v, w) self.assertEqual(v.dtype, w.dtype) if self.cls is Variable: if deep: self.assertIsNot(source_ndarray(v.values), source_ndarray(w.values)) else: self.assertIs(source_ndarray(v.values), source_ndarray(w.values)) self.assertVariableIdentical(v, copy(v)) def test_copy_index(self): midx = pd.MultiIndex.from_product([['a', 'b'], [1, 2], [-1, -2]], names=('one', 'two', 'three')) v = self.cls('x', midx) for deep in [True, False]: w = v.copy(deep=deep) self.assertIsInstance(w._data, PandasIndexAdapter) self.assertIsInstance(w.to_index(), pd.MultiIndex) self.assertArrayEqual(v._data.array, w._data.array) def test_real_and_imag(self): v = self.cls('x', np.arange(3) - 1j * np.arange(3), {'foo': 'bar'}) expected_re = self.cls('x', np.arange(3), {'foo': 'bar'}) self.assertVariableIdentical(v.real, expected_re) expected_im = self.cls('x', -np.arange(3), {'foo': 'bar'}) self.assertVariableIdentical(v.imag, expected_im) expected_abs = self.cls('x', np.sqrt(2 * np.arange(3) ** 2)).to_base_variable() self.assertVariableAllClose(abs(v), expected_abs) def test_aggregate_complex(self): # should skip NaNs v = self.cls('x', [1, 2j, np.nan]) expected = Variable((), 0.5 + 1j) self.assertVariableAllClose(v.mean(), expected) def test_pandas_cateogrical_dtype(self): data = pd.Categorical(np.arange(10, dtype='int64')) v = self.cls('x', data) print(v) # should not error assert v.dtype == 'int64' def test_pandas_datetime64_with_tz(self): data = pd.date_range(start='2000-01-01', tz=pytz.timezone('America/New_York'), periods=10, freq='1h') v = self.cls('x', data) print(v) # should not error if 'America/New_York' in str(data.dtype): # pandas is new enough that it has datetime64 with timezone dtype assert v.dtype == 'object' def test_multiindex(self): idx = pd.MultiIndex.from_product([list('abc'), [0, 1]]) v = self.cls('x', idx) self.assertVariableIdentical(Variable((), ('a', 0)), v[0]) self.assertVariableIdentical(v, v[:]) def test_load(self): array = self.cls('x', np.arange(5)) orig_data = array._data copied = array.copy(deep=True) array.load() assert type(array._data) is type(orig_data) assert type(copied._data) is type(orig_data) self.assertVariableIdentical(array, copied) class TestVariable(TestCase, VariableSubclassTestCases): cls = staticmethod(Variable) def setUp(self): self.d = np.random.random((10, 3)).astype(np.float64) def test_data_and_values(self): v = Variable(['time', 'x'], self.d) self.assertArrayEqual(v.data, self.d) self.assertArrayEqual(v.values, self.d) self.assertIs(source_ndarray(v.values), self.d) with self.assertRaises(ValueError): # wrong size v.values = np.random.random(5) d2 = np.random.random((10, 3)) v.values = d2 self.assertIs(source_ndarray(v.values), d2) d3 = np.random.random((10, 3)) v.data = d3 self.assertIs(source_ndarray(v.data), d3) def test_numpy_same_methods(self): v = Variable([], np.float32(0.0)) self.assertEqual(v.item(), 0) self.assertIs(type(v.item()), float) v = IndexVariable('x', np.arange(5)) self.assertEqual(2, v.searchsorted(2)) def test_datetime64_conversion_scalar(self): expected = np.datetime64('2000-01-01T00:00:00Z', 'ns') for values in [ np.datetime64('2000-01-01T00Z'), pd.Timestamp('2000-01-01T00'), datetime(2000, 1, 1), ]: v = Variable([], values) self.assertEqual(v.dtype, np.dtype('datetime64[ns]')) self.assertEqual(v.values, expected) self.assertEqual(v.values.dtype, np.dtype('datetime64[ns]')) def test_timedelta64_conversion_scalar(self): expected = np.timedelta64(24 * 60 * 60 * 10 ** 9, 'ns') for values in [ np.timedelta64(1, 'D'), pd.Timedelta('1 day'), timedelta(days=1), ]: v = Variable([], values) self.assertEqual(v.dtype, np.dtype('timedelta64[ns]')) self.assertEqual(v.values, expected) self.assertEqual(v.values.dtype, np.dtype('timedelta64[ns]')) def test_0d_str(self): v = Variable([], u'foo') self.assertEqual(v.dtype, np.dtype('U3')) self.assertEqual(v.values, 'foo') v = Variable([], np.string_('foo')) self.assertEqual(v.dtype, np.dtype('S3')) self.assertEqual(v.values, bytes('foo', 'ascii') if PY3 else 'foo') def test_0d_datetime(self): v = Variable([], pd.Timestamp('2000-01-01')) self.assertEqual(v.dtype, np.dtype('datetime64[ns]')) self.assertEqual(v.values, np.datetime64('2000-01-01T00Z', 'ns')) def test_0d_timedelta(self): for td in [pd.to_timedelta('1s'), np.timedelta64(1, 's')]: v = Variable([], td) self.assertEqual(v.dtype, np.dtype('timedelta64[ns]')) self.assertEqual(v.values, np.timedelta64(10 ** 9, 'ns')) def test_equals_and_identical(self): d = np.random.rand(10, 3) d[0, 0] = np.nan v1 = Variable(('dim1', 'dim2'), data=d, attrs={'att1': 3, 'att2': [1, 2, 3]}) v2 = Variable(('dim1', 'dim2'), data=d, attrs={'att1': 3, 'att2': [1, 2, 3]}) self.assertTrue(v1.equals(v2)) self.assertTrue(v1.identical(v2)) v3 = Variable(('dim1', 'dim3'), data=d) self.assertFalse(v1.equals(v3)) v4 = Variable(('dim1', 'dim2'), data=d) self.assertTrue(v1.equals(v4)) self.assertFalse(v1.identical(v4)) v5 = deepcopy(v1) v5.values[:] = np.random.rand(10, 3) self.assertFalse(v1.equals(v5)) self.assertFalse(v1.equals(None)) self.assertFalse(v1.equals(d)) self.assertFalse(v1.identical(None)) self.assertFalse(v1.identical(d)) def test_broadcast_equals(self): v1 = Variable((), np.nan) v2 = Variable(('x'), [np.nan, np.nan]) self.assertTrue(v1.broadcast_equals(v2)) self.assertFalse(v1.equals(v2)) self.assertFalse(v1.identical(v2)) v3 = Variable(('x'), [np.nan]) self.assertTrue(v1.broadcast_equals(v3)) self.assertFalse(v1.equals(v3)) self.assertFalse(v1.identical(v3)) self.assertFalse(v1.broadcast_equals(None)) v4 = Variable(('x'), [np.nan] * 3) self.assertFalse(v2.broadcast_equals(v4)) def test_no_conflicts(self): v1 = Variable(('x'), [1, 2, np.nan, np.nan]) v2 = Variable(('x'), [np.nan, 2, 3, np.nan]) self.assertTrue(v1.no_conflicts(v2)) self.assertFalse(v1.equals(v2)) self.assertFalse(v1.broadcast_equals(v2)) self.assertFalse(v1.identical(v2)) self.assertFalse(v1.no_conflicts(None)) v3 = Variable(('y'), [np.nan, 2, 3, np.nan]) self.assertFalse(v3.no_conflicts(v1)) d = np.array([1, 2, np.nan, np.nan]) self.assertFalse(v1.no_conflicts(d)) self.assertFalse(v2.no_conflicts(d)) v4 = Variable(('w', 'x'), [d]) self.assertTrue(v1.no_conflicts(v4)) def test_as_variable(self): data = np.arange(10) expected = Variable('x', data) self.assertVariableIdentical(expected, as_variable(expected)) ds = Dataset({'x': expected}) self.assertVariableIdentical(expected, as_variable(ds['x']).to_base_variable()) self.assertNotIsInstance(ds['x'], Variable) self.assertIsInstance(as_variable(ds['x']), Variable) FakeVariable = namedtuple('FakeVariable', 'values dims') fake_xarray = FakeVariable(expected.values, expected.dims) self.assertVariableIdentical(expected, as_variable(fake_xarray)) xarray_tuple = (expected.dims, expected.values) self.assertVariableIdentical(expected, as_variable(xarray_tuple)) with self.assertRaisesRegexp(TypeError, 'tuples to convert'): as_variable(tuple(data)) with self.assertRaisesRegexp( TypeError, 'without an explicit list of dimensions'): as_variable(data) actual = as_variable(data, name='x') self.assertVariableIdentical(expected.to_index_variable(), actual) actual = as_variable(0) expected = Variable([], 0) self.assertVariableIdentical(expected, actual) def test_repr(self): v = Variable(['time', 'x'], [[1, 2, 3], [4, 5, 6]], {'foo': 'bar'}) expected = dedent(""" array([[1, 2, 3], [4, 5, 6]]) Attributes: foo: bar """).strip() self.assertEqual(expected, repr(v)) def test_repr_lazy_data(self): v = Variable('x', LazilyIndexedArray(np.arange(2e5))) self.assertIn('200000 values with dtype', repr(v)) self.assertIsInstance(v._data, LazilyIndexedArray) def test_items(self): data = np.random.random((10, 11)) v = Variable(['x', 'y'], data) # test slicing self.assertVariableIdentical(v, v[:]) self.assertVariableIdentical(v, v[...]) self.assertVariableIdentical(Variable(['y'], data[0]), v[0]) self.assertVariableIdentical(Variable(['x'], data[:, 0]), v[:, 0]) self.assertVariableIdentical(Variable(['x', 'y'], data[:3, :2]), v[:3, :2]) # test array indexing x = Variable(['x'], np.arange(10)) y = Variable(['y'], np.arange(11)) self.assertVariableIdentical(v, v[x.values]) self.assertVariableIdentical(v, v[x]) self.assertVariableIdentical(v[:3], v[x < 3]) self.assertVariableIdentical(v[:, 3:], v[:, y >= 3]) self.assertVariableIdentical(v[:3, 3:], v[x < 3, y >= 3]) self.assertVariableIdentical(v[:3, :2], v[x[:3], y[:2]]) self.assertVariableIdentical(v[:3, :2], v[range(3), range(2)]) # test iteration for n, item in enumerate(v): self.assertVariableIdentical(Variable(['y'], data[n]), item) with self.assertRaisesRegexp(TypeError, 'iteration over a 0-d'): iter(Variable([], 0)) # test setting v.values[:] = 0 self.assertTrue(np.all(v.values == 0)) # test orthogonal setting v[range(10), range(11)] = 1 self.assertArrayEqual(v.values, np.ones((10, 11))) def test_isel(self): v = Variable(['time', 'x'], self.d) self.assertVariableIdentical(v.isel(time=slice(None)), v) self.assertVariableIdentical(v.isel(time=0), v[0]) self.assertVariableIdentical(v.isel(time=slice(0, 3)), v[:3]) self.assertVariableIdentical(v.isel(x=0), v[:, 0]) with self.assertRaisesRegexp(ValueError, 'do not exist'): v.isel(not_a_dim=0) def test_index_0d_numpy_string(self): # regression test to verify our work around for indexing 0d strings v = Variable([], np.string_('asdf')) self.assertVariableIdentical(v[()], v) v = Variable([], np.unicode_(u'asdf')) self.assertVariableIdentical(v[()], v) def test_indexing_0d_unicode(self): # regression test for GH568 actual = Variable(('x'), [u'tmax'])[0][()] expected = Variable((), u'tmax') self.assertVariableIdentical(actual, expected) def test_shift(self): v = Variable('x', [1, 2, 3, 4, 5]) self.assertVariableIdentical(v, v.shift(x=0)) self.assertIsNot(v, v.shift(x=0)) expected = Variable('x', [np.nan, 1, 2, 3, 4]) self.assertVariableIdentical(expected, v.shift(x=1)) expected = Variable('x', [np.nan, np.nan, 1, 2, 3]) self.assertVariableIdentical(expected, v.shift(x=2)) expected = Variable('x', [2, 3, 4, 5, np.nan]) self.assertVariableIdentical(expected, v.shift(x=-1)) expected = Variable('x', [np.nan] * 5) self.assertVariableIdentical(expected, v.shift(x=5)) self.assertVariableIdentical(expected, v.shift(x=6)) with self.assertRaisesRegexp(ValueError, 'dimension'): v.shift(z=0) v = Variable('x', [1, 2, 3, 4, 5], {'foo': 'bar'}) self.assertVariableIdentical(v, v.shift(x=0)) expected = Variable('x', [np.nan, 1, 2, 3, 4], {'foo': 'bar'}) self.assertVariableIdentical(expected, v.shift(x=1)) def test_shift2d(self): v = Variable(('x', 'y'), [[1, 2], [3, 4]]) expected = Variable(('x', 'y'), [[np.nan, np.nan], [np.nan, 1]]) self.assertVariableIdentical(expected, v.shift(x=1, y=1)) def test_roll(self): v = Variable('x', [1, 2, 3, 4, 5]) self.assertVariableIdentical(v, v.roll(x=0)) self.assertIsNot(v, v.roll(x=0)) expected = Variable('x', [5, 1, 2, 3, 4]) self.assertVariableIdentical(expected, v.roll(x=1)) self.assertVariableIdentical(expected, v.roll(x=-4)) self.assertVariableIdentical(expected, v.roll(x=6)) expected = Variable('x', [4, 5, 1, 2, 3]) self.assertVariableIdentical(expected, v.roll(x=2)) self.assertVariableIdentical(expected, v.roll(x=-3)) with self.assertRaisesRegexp(ValueError, 'dimension'): v.roll(z=0) def test_roll_consistency(self): v = Variable(('x', 'y'), np.random.randn(5, 6)) for axis, dim in [(0, 'x'), (1, 'y')]: for shift in [-3, 0, 1, 7, 11]: expected = np.roll(v.values, shift, axis=axis) actual = v.roll(**{dim: shift}).values self.assertArrayEqual(expected, actual) def test_transpose(self): v = Variable(['time', 'x'], self.d) v2 = Variable(['x', 'time'], self.d.T) self.assertVariableIdentical(v, v2.transpose()) self.assertVariableIdentical(v.transpose(), v.T) x = np.random.randn(2, 3, 4, 5) w = Variable(['a', 'b', 'c', 'd'], x) w2 = Variable(['d', 'b', 'c', 'a'], np.einsum('abcd->dbca', x)) self.assertEqual(w2.shape, (5, 3, 4, 2)) self.assertVariableIdentical(w2, w.transpose('d', 'b', 'c', 'a')) self.assertVariableIdentical(w, w2.transpose('a', 'b', 'c', 'd')) w3 = Variable(['b', 'c', 'd', 'a'], np.einsum('abcd->bcda', x)) self.assertVariableIdentical(w, w3.transpose('a', 'b', 'c', 'd')) def test_transpose_0d(self): for value in [ 3.5, ('a', 1), np.datetime64('2000-01-01'), np.timedelta64(1, 'h'), None, object(), ]: variable = Variable([], value) actual = variable.transpose() assert actual.identical(variable) def test_squeeze(self): v = Variable(['x', 'y'], [[1]]) self.assertVariableIdentical(Variable([], 1), v.squeeze()) self.assertVariableIdentical(Variable(['y'], [1]), v.squeeze('x')) self.assertVariableIdentical(Variable(['y'], [1]), v.squeeze(['x'])) self.assertVariableIdentical(Variable(['x'], [1]), v.squeeze('y')) self.assertVariableIdentical(Variable([], 1), v.squeeze(['x', 'y'])) v = Variable(['x', 'y'], [[1, 2]]) self.assertVariableIdentical(Variable(['y'], [1, 2]), v.squeeze()) self.assertVariableIdentical(Variable(['y'], [1, 2]), v.squeeze('x')) with self.assertRaisesRegexp(ValueError, 'cannot select a dimension'): v.squeeze('y') def test_get_axis_num(self): v = Variable(['x', 'y', 'z'], np.random.randn(2, 3, 4)) self.assertEqual(v.get_axis_num('x'), 0) self.assertEqual(v.get_axis_num(['x']), (0,)) self.assertEqual(v.get_axis_num(['x', 'y']), (0, 1)) self.assertEqual(v.get_axis_num(['z', 'y', 'x']), (2, 1, 0)) with self.assertRaisesRegexp(ValueError, 'not found in array dim'): v.get_axis_num('foobar') def test_expand_dims(self): v = Variable(['x'], [0, 1]) actual = v.expand_dims(['x', 'y']) expected = Variable(['x', 'y'], [[0], [1]]) self.assertVariableIdentical(actual, expected) actual = v.expand_dims(['y', 'x']) self.assertVariableIdentical(actual, expected.T) actual = v.expand_dims(OrderedDict([('x', 2), ('y', 2)])) expected = Variable(['x', 'y'], [[0, 0], [1, 1]]) self.assertVariableIdentical(actual, expected) v = Variable(['foo'], [0, 1]) actual = v.expand_dims('foo') expected = v self.assertVariableIdentical(actual, expected) with self.assertRaisesRegexp(ValueError, 'must be a superset'): v.expand_dims(['z']) def test_expand_dims_object_dtype(self): v = Variable([], ('a', 1)) actual = v.expand_dims(('x',), (3,)) exp_values = np.empty((3,), dtype=object) for i in range(3): exp_values[i] = ('a', 1) expected = Variable(['x'], exp_values) assert actual.identical(expected) def test_stack(self): v = Variable(['x', 'y'], [[0, 1], [2, 3]], {'foo': 'bar'}) actual = v.stack(z=('x', 'y')) expected = Variable('z', [0, 1, 2, 3], v.attrs) self.assertVariableIdentical(actual, expected) actual = v.stack(z=('x',)) expected = Variable(('y', 'z'), v.data.T, v.attrs) self.assertVariableIdentical(actual, expected) actual = v.stack(z=(),) self.assertVariableIdentical(actual, v) actual = v.stack(X=('x',), Y=('y',)).transpose('X', 'Y') expected = Variable(('X', 'Y'), v.data, v.attrs) self.assertVariableIdentical(actual, expected) def test_stack_errors(self): v = Variable(['x', 'y'], [[0, 1], [2, 3]], {'foo': 'bar'}) with self.assertRaisesRegexp(ValueError, 'invalid existing dim'): v.stack(z=('x1',)) with self.assertRaisesRegexp(ValueError, 'cannot create a new dim'): v.stack(x=('x',)) def test_unstack(self): v = Variable('z', [0, 1, 2, 3], {'foo': 'bar'}) actual = v.unstack(z=OrderedDict([('x', 2), ('y', 2)])) expected = Variable(('x', 'y'), [[0, 1], [2, 3]], v.attrs) self.assertVariableIdentical(actual, expected) actual = v.unstack(z=OrderedDict([('x', 4), ('y', 1)])) expected = Variable(('x', 'y'), [[0], [1], [2], [3]], v.attrs) self.assertVariableIdentical(actual, expected) actual = v.unstack(z=OrderedDict([('x', 4)])) expected = Variable('x', [0, 1, 2, 3], v.attrs) self.assertVariableIdentical(actual, expected) def test_unstack_errors(self): v = Variable('z', [0, 1, 2, 3]) with self.assertRaisesRegexp(ValueError, 'invalid existing dim'): v.unstack(foo={'x': 4}) with self.assertRaisesRegexp(ValueError, 'cannot create a new dim'): v.stack(z=('z',)) with self.assertRaisesRegexp(ValueError, 'the product of the new dim'): v.unstack(z={'x': 5}) def test_unstack_2d(self): v = Variable(['x', 'y'], [[0, 1], [2, 3]]) actual = v.unstack(y={'z': 2}) expected = Variable(['x', 'z'], v.data) self.assertVariableIdentical(actual, expected) actual = v.unstack(x={'z': 2}) expected = Variable(['y', 'z'], v.data.T) self.assertVariableIdentical(actual, expected) def test_stack_unstack_consistency(self): v = Variable(['x', 'y'], [[0, 1], [2, 3]]) actual = (v.stack(z=('x', 'y')) .unstack(z=OrderedDict([('x', 2), ('y', 2)]))) self.assertVariableIdentical(actual, v) def test_broadcasting_math(self): x = np.random.randn(2, 3) v = Variable(['a', 'b'], x) # 1d to 2d broadcasting self.assertVariableIdentical( v * v, Variable(['a', 'b'], np.einsum('ab,ab->ab', x, x))) self.assertVariableIdentical( v * v[0], Variable(['a', 'b'], np.einsum('ab,b->ab', x, x[0]))) self.assertVariableIdentical( v[0] * v, Variable(['b', 'a'], np.einsum('b,ab->ba', x[0], x))) self.assertVariableIdentical( v[0] * v[:, 0], Variable(['b', 'a'], np.einsum('b,a->ba', x[0], x[:, 0]))) # higher dim broadcasting y = np.random.randn(3, 4, 5) w = Variable(['b', 'c', 'd'], y) self.assertVariableIdentical( v * w, Variable(['a', 'b', 'c', 'd'], np.einsum('ab,bcd->abcd', x, y))) self.assertVariableIdentical( w * v, Variable(['b', 'c', 'd', 'a'], np.einsum('bcd,ab->bcda', y, x))) self.assertVariableIdentical( v * w[0], Variable(['a', 'b', 'c', 'd'], np.einsum('ab,cd->abcd', x, y[0]))) def test_broadcasting_failures(self): a = Variable(['x'], np.arange(10)) b = Variable(['x'], np.arange(5)) c = Variable(['x', 'x'], np.arange(100).reshape(10, 10)) with self.assertRaisesRegexp(ValueError, 'mismatched lengths'): a + b with self.assertRaisesRegexp(ValueError, 'duplicate dimensions'): a + c def test_inplace_math(self): x = np.arange(5) v = Variable(['x'], x) v2 = v v2 += 1 self.assertIs(v, v2) # since we provided an ndarray for data, it is also modified in-place self.assertIs(source_ndarray(v.values), x) self.assertArrayEqual(v.values, np.arange(5) + 1) with self.assertRaisesRegexp(ValueError, 'dimensions cannot change'): v += Variable('y', np.arange(5)) def test_reduce(self): v = Variable(['x', 'y'], self.d, {'ignored': 'attributes'}) self.assertVariableIdentical(v.reduce(np.std, 'x'), Variable(['y'], self.d.std(axis=0))) self.assertVariableIdentical(v.reduce(np.std, axis=0), v.reduce(np.std, dim='x')) self.assertVariableIdentical(v.reduce(np.std, ['y', 'x']), Variable([], self.d.std(axis=(0, 1)))) self.assertVariableIdentical(v.reduce(np.std), Variable([], self.d.std())) self.assertVariableIdentical( v.reduce(np.mean, 'x').reduce(np.std, 'y'), Variable([], self.d.mean(axis=0).std())) self.assertVariableAllClose(v.mean('x'), v.reduce(np.mean, 'x')) with self.assertRaisesRegexp(ValueError, 'cannot supply both'): v.mean(dim='x', axis=0) @pytest.mark.skipif(LooseVersion(np.__version__) < LooseVersion('1.10.0'), reason='requires numpy version 1.10.0 or later') def test_quantile(self): v = Variable(['x', 'y'], self.d) for q in [0.25, [0.50], [0.25, 0.75]]: for axis, dim in zip([None, 0, [0], [0, 1]], [None, 'x', ['x'], ['x', 'y']]): actual = v.quantile(q, dim=dim) expected = np.nanpercentile(self.d, np.array(q) * 100, axis=axis) np.testing.assert_allclose(actual.values, expected) def test_big_endian_reduce(self): # regression test for GH489 data = np.ones(5, dtype='>f4') v = Variable(['x'], data) expected = Variable([], 5) self.assertVariableIdentical(expected, v.sum()) def test_reduce_funcs(self): v = Variable('x', np.array([1, np.nan, 2, 3])) self.assertVariableIdentical(v.mean(), Variable([], 2)) self.assertVariableIdentical(v.mean(skipna=True), Variable([], 2)) self.assertVariableIdentical(v.mean(skipna=False), Variable([], np.nan)) self.assertVariableIdentical(np.mean(v), Variable([], 2)) self.assertVariableIdentical(v.prod(), Variable([], 6)) self.assertVariableIdentical(v.cumsum(axis=0), Variable('x', np.array([1, 1, 3, 6]))) self.assertVariableIdentical(v.cumprod(axis=0), Variable('x', np.array([1, 1, 2, 6]))) self.assertVariableIdentical(v.var(), Variable([], 2.0 / 3)) if LooseVersion(np.__version__) < '1.9': with self.assertRaises(NotImplementedError): v.median() else: self.assertVariableIdentical(v.median(), Variable([], 2)) v = Variable('x', [True, False, False]) self.assertVariableIdentical(v.any(), Variable([], True)) self.assertVariableIdentical(v.all(dim='x'), Variable([], False)) v = Variable('t', pd.date_range('2000-01-01', periods=3)) with self.assertRaises(NotImplementedError): v.max(skipna=True) self.assertVariableIdentical( v.max(), Variable([], pd.Timestamp('2000-01-03'))) def test_reduce_keep_attrs(self): _attrs = {'units': 'test', 'long_name': 'testing'} v = Variable(['x', 'y'], self.d, _attrs) # Test dropped attrs vm = v.mean() self.assertEqual(len(vm.attrs), 0) self.assertEqual(vm.attrs, OrderedDict()) # Test kept attrs vm = v.mean(keep_attrs=True) self.assertEqual(len(vm.attrs), len(_attrs)) self.assertEqual(vm.attrs, _attrs) def test_count(self): expected = Variable([], 3) actual = Variable(['x'], [1, 2, 3, np.nan]).count() self.assertVariableIdentical(expected, actual) v = Variable(['x'], np.array(['1', '2', '3', np.nan], dtype=object)) actual = v.count() self.assertVariableIdentical(expected, actual) actual = Variable(['x'], [True, False, True]).count() self.assertVariableIdentical(expected, actual) self.assertEqual(actual.dtype, int) expected = Variable(['x'], [2, 3]) actual = Variable(['x', 'y'], [[1, 0, np.nan], [1, 1, 1]]).count('y') self.assertVariableIdentical(expected, actual) class TestIndexVariable(TestCase, VariableSubclassTestCases): cls = staticmethod(IndexVariable) def test_init(self): with self.assertRaisesRegexp(ValueError, 'must be 1-dimensional'): IndexVariable((), 0) def test_to_index(self): data = 0.5 * np.arange(10) v = IndexVariable(['time'], data, {'foo': 'bar'}) self.assertTrue(pd.Index(data, name='time').identical(v.to_index())) def test_multiindex_default_level_names(self): midx = pd.MultiIndex.from_product([['a', 'b'], [1, 2]]) v = IndexVariable(['x'], midx, {'foo': 'bar'}) self.assertEqual(v.to_index().names, ('x_level_0', 'x_level_1')) def test_data(self): x = IndexVariable('x', np.arange(3.0)) self.assertIsInstance(x._data, PandasIndexAdapter) self.assertIsInstance(x.data, np.ndarray) self.assertEqual(float, x.dtype) self.assertArrayEqual(np.arange(3), x) self.assertEqual(float, x.values.dtype) with self.assertRaisesRegexp(TypeError, 'cannot be modified'): x[:] = 0 def test_name(self): coord = IndexVariable('x', [10.0]) self.assertEqual(coord.name, 'x') with self.assertRaises(AttributeError): coord.name = 'y' def test_level_names(self): midx = pd.MultiIndex.from_product([['a', 'b'], [1, 2]], names=['level_1', 'level_2']) x = IndexVariable('x', midx) self.assertEqual(x.level_names, midx.names) self.assertIsNone(IndexVariable('y', [10.0]).level_names) def test_get_level_variable(self): midx = pd.MultiIndex.from_product([['a', 'b'], [1, 2]], names=['level_1', 'level_2']) x = IndexVariable('x', midx) level_1 = IndexVariable('x', midx.get_level_values('level_1')) self.assertVariableIdentical(x.get_level_variable('level_1'), level_1) with self.assertRaisesRegexp(ValueError, 'has no MultiIndex'): IndexVariable('y', [10.0]).get_level_variable('level') def test_concat_periods(self): periods = pd.period_range('2000-01-01', periods=10) coords = [IndexVariable('t', periods[:5]), IndexVariable('t', periods[5:])] expected = IndexVariable('t', periods) actual = IndexVariable.concat(coords, dim='t') assert actual.identical(expected) assert isinstance(actual.to_index(), pd.PeriodIndex) positions = [list(range(5)), list(range(5, 10))] actual = IndexVariable.concat(coords, dim='t', positions=positions) assert actual.identical(expected) assert isinstance(actual.to_index(), pd.PeriodIndex) def test_concat_multiindex(self): idx = pd.MultiIndex.from_product([[0, 1, 2], ['a', 'b']]) coords = [IndexVariable('x', idx[:2]), IndexVariable('x', idx[2:])] expected = IndexVariable('x', idx) actual = IndexVariable.concat(coords, dim='x') assert actual.identical(expected) assert isinstance(actual.to_index(), pd.MultiIndex) def test_coordinate_alias(self): with self.assertWarns('deprecated'): x = Coordinate('x', [1, 2, 3]) self.assertIsInstance(x, IndexVariable) class TestAsCompatibleData(TestCase): def test_unchanged_types(self): types = (np.asarray, PandasIndexAdapter, indexing.LazilyIndexedArray) for t in types: for data in [np.arange(3), pd.date_range('2000-01-01', periods=3), pd.date_range('2000-01-01', periods=3).values]: x = t(data) self.assertIs(source_ndarray(x), source_ndarray(as_compatible_data(x))) def test_converted_types(self): for input_array in [[[0, 1, 2]], pd.DataFrame([[0, 1, 2]])]: actual = as_compatible_data(input_array) self.assertArrayEqual(np.asarray(input_array), actual) self.assertEqual(np.ndarray, type(actual)) self.assertEqual(np.asarray(input_array).dtype, actual.dtype) def test_masked_array(self): original = np.ma.MaskedArray(np.arange(5)) expected = np.arange(5) actual = as_compatible_data(original) self.assertArrayEqual(expected, actual) self.assertEqual(np.dtype(int), actual.dtype) original = np.ma.MaskedArray(np.arange(5), mask=4 * [False] + [True]) expected = np.arange(5.0) expected[-1] = np.nan actual = as_compatible_data(original) self.assertArrayEqual(expected, actual) self.assertEqual(np.dtype(float), actual.dtype) def test_datetime(self): expected = np.datetime64('2000-01-01T00Z') actual = as_compatible_data(expected) self.assertEqual(expected, actual) self.assertEqual(np.ndarray, type(actual)) self.assertEqual(np.dtype('datetime64[ns]'), actual.dtype) expected = np.array([np.datetime64('2000-01-01T00Z')]) actual = as_compatible_data(expected) self.assertEqual(np.asarray(expected), actual) self.assertEqual(np.ndarray, type(actual)) self.assertEqual(np.dtype('datetime64[ns]'), actual.dtype) expected = np.array([np.datetime64('2000-01-01T00Z', 'ns')]) actual = as_compatible_data(expected) self.assertEqual(np.asarray(expected), actual) self.assertEqual(np.ndarray, type(actual)) self.assertEqual(np.dtype('datetime64[ns]'), actual.dtype) self.assertIs(expected, source_ndarray(np.asarray(actual))) expected = np.datetime64('2000-01-01T00Z', 'ns') actual = as_compatible_data(datetime(2000, 1, 1)) self.assertEqual(np.asarray(expected), actual) self.assertEqual(np.ndarray, type(actual)) self.assertEqual(np.dtype('datetime64[ns]'), actual.dtype) def test_full_like(self): # For more thorough tests, see test_variable.py orig = Variable(dims=('x', 'y'), data=[[1.5 ,2.0], [3.1, 4.3]], attrs={'foo': 'bar'}) expect = orig.copy(deep=True) expect.values = [[2.0, 2.0], [2.0, 2.0]] self.assertVariableIdentical(expect, full_like(orig, 2)) # override dtype expect.values = [[True, True], [True, True]] self.assertEquals(expect.dtype, bool) self.assertVariableIdentical(expect, full_like(orig, True, dtype=bool)) @requires_dask def test_full_like_dask(self): orig = Variable(dims=('x', 'y'), data=[[1.5 ,2.0], [3.1, 4.3]], attrs={'foo': 'bar'}).chunk(((1, 1), (2,))) def check(actual, expect_dtype, expect_values): self.assertEqual(actual.dtype, expect_dtype) self.assertEqual(actual.shape, orig.shape) self.assertEqual(actual.dims, orig.dims) self.assertEqual(actual.attrs, orig.attrs) self.assertEqual(actual.chunks, orig.chunks) self.assertArrayEqual(actual.values, expect_values) check(full_like(orig, 2), orig.dtype, np.full_like(orig.values, 2)) # override dtype check(full_like(orig, True, dtype=bool), bool, np.full_like(orig.values, True, dtype=bool)) # Check that there's no array stored inside dask # (e.g. we didn't create a numpy array and then we chunked it!) dsk = full_like(orig, 1).data.dask for v in dsk.values(): if isinstance(v, tuple): for vi in v: assert not isinstance(vi, np.ndarray) else: assert not isinstance(v, np.ndarray) def test_zeros_like(self): orig = Variable(dims=('x', 'y'), data=[[1.5 ,2.0], [3.1, 4.3]], attrs={'foo': 'bar'}) self.assertVariableIdentical(zeros_like(orig), full_like(orig, 0)) self.assertVariableIdentical(zeros_like(orig, dtype=int), full_like(orig, 0, dtype=int)) def test_ones_like(self): orig = Variable(dims=('x', 'y'), data=[[1.5 ,2.0], [3.1, 4.3]], attrs={'foo': 'bar'}) self.assertVariableIdentical(ones_like(orig), full_like(orig, 1)) self.assertVariableIdentical(ones_like(orig, dtype=int), full_like(orig, 1, dtype=int)) ����������������������������������������������������������������������������������������������������������������������������������������������������������xarray-0.9.1/xarray/tutorial.py���������������������������������������������������������������������0000644�1311115�0011610�00000003335�13043511243�016652� 0����������������������������������������������������������������������������������������������������ustar �shoyer��������������������������eng�����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������''' Useful for: * users learning xarray * building tutorials in the documentation. ''' from __future__ import absolute_import from __future__ import division from __future__ import print_function import os as _os from .backends.api import open_dataset as _open_dataset from .core.pycompat import urlretrieve as _urlretrieve _default_cache_dir = _os.sep.join(('~', '.xarray_tutorial_data')) # idea borrowed from Seaborn def load_dataset(name, cache=True, cache_dir=_default_cache_dir, github_url='https://github.com/pydata/xarray-data', **kws): """ Load a dataset from the online repository (requires internet). If a local copy is found then always use that to avoid network traffic. Parameters ---------- name : str Name of the netcdf file containing the dataset ie. 'air_temperature' cache_dir : string, optional The directory in which to search for and write cached data. cache : boolean, optional If True, then cache data locally for use on subsequent calls github_url : string Github repository where the data is stored kws : dict, optional Passed to xarray.open_dataset """ longdir = _os.path.expanduser(cache_dir) fullname = name + '.nc' localfile = _os.sep.join((longdir, fullname)) if not _os.path.exists(localfile): # This will always leave this directory on disk. # May want to add an option to remove it. if not _os.path.isdir(longdir): _os.mkdir(longdir) url = '/'.join((github_url, 'raw', 'master', fullname)) _urlretrieve(url, localfile) ds = _open_dataset(localfile, **kws).load() if not cache: _os.remove(localfile) return ds ���������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������xarray-0.9.1/xarray/ufuncs.py�����������������������������������������������������������������������0000644�1311115�0011610�00000006376�13043511243�016322� 0����������������������������������������������������������������������������������������������������ustar �shoyer��������������������������eng�����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������"""xarray specific universal functions Handles unary and binary operations for the following types, in ascending priority order: - scalars - numpy.ndarray - dask.array.Array - xarray.Variable - xarray.DataArray - xarray.Dataset - xarray.core.groupby.GroupBy Once NumPy 1.10 comes out with support for overriding ufuncs, this module will hopefully no longer be necessary. """ from __future__ import absolute_import from __future__ import division from __future__ import print_function import numpy as _np from .core.variable import Variable as _Variable from .core.dataset import Dataset as _Dataset from .core.dataarray import DataArray as _DataArray from .core.groupby import GroupBy as _GroupBy from .core.pycompat import dask_array_type as _dask_array_type from .core.ops import _dask_or_eager_func _xarray_types = (_Variable, _DataArray, _Dataset, _GroupBy) _dispatch_order = (_np.ndarray, _dask_array_type) + _xarray_types def _dispatch_priority(obj): for priority, cls in enumerate(_dispatch_order): if isinstance(obj, cls): return priority return -1 class _UFuncDispatcher(object): """Wrapper for dispatching ufuncs.""" def __init__(self, name): self._name = name def __call__(self, *args, **kwargs): new_args = args f = _dask_or_eager_func(self._name, n_array_args=len(args)) if len(args) > 2 or len(args) == 0: raise TypeError('cannot handle %s arguments for %r' % (len(args), self._name)) elif len(args) == 1: if isinstance(args[0], _xarray_types): f = args[0]._unary_op(self) else: # len(args) = 2 p1, p2 = map(_dispatch_priority, args) if p1 >= p2: if isinstance(args[0], _xarray_types): f = args[0]._binary_op(self) else: if isinstance(args[1], _xarray_types): f = args[1]._binary_op(self, reflexive=True) new_args = tuple(reversed(args)) res = f(*new_args, **kwargs) if res is NotImplemented: raise TypeError('%r not implemented for types (%r, %r)' % (self._name, type(args[0]), type(args[1]))) return res def _create_op(name): func = _UFuncDispatcher(name) func.__name__ = name doc = getattr(_np, name).__doc__ func.__doc__ = ('xarray specific variant of numpy.%s. Handles ' 'xarray.Dataset, xarray.DataArray, xarray.Variable, ' 'numpy.ndarray and dask.array.Array objects with ' 'automatic dispatching.\n\n' 'Documentation from numpy:\n\n%s' % (name, doc)) return func __all__ = """logaddexp logaddexp2 conj exp log log2 log10 log1p expm1 sqrt square sin cos tan arcsin arccos arctan arctan2 hypot sinh cosh tanh arcsinh arccosh arctanh deg2rad rad2deg logical_and logical_or logical_xor logical_not maximum minimum fmax fmin isreal iscomplex isfinite isinf isnan signbit copysign nextafter ldexp fmod floor ceil trunc degrees radians rint fix angle real imag fabs sign frexp fmod """.split() for name in __all__: globals()[name] = _create_op(name) ������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������xarray-0.9.1/xarray/version.py����������������������������������������������������������������������0000644�1311115�0011610�00000000052�13043675420�016475� 0����������������������������������������������������������������������������������������������������ustar �shoyer��������������������������eng�����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������version = '0.9.1' short_version = '0.9.1' ��������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������xarray-0.9.1/xarray.egg-info/�����������������������������������������������������������������������0000755�1311115�0011610�00000000000�13043675421�016134� 5����������������������������������������������������������������������������������������������������ustar �shoyer��������������������������eng�����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������xarray-0.9.1/xarray.egg-info/dependency_links.txt���������������������������������������������������0000644�1311115�0011610�00000000001�13043675420�022201� 0����������������������������������������������������������������������������������������������������ustar �shoyer��������������������������eng�����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������ �������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������xarray-0.9.1/xarray.egg-info/PKG-INFO���������������������������������������������������������������0000644�1311115�0011610�00000004040�13043675420�017226� 0����������������������������������������������������������������������������������������������������ustar �shoyer��������������������������eng�����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������Metadata-Version: 1.1 Name: xarray Version: 0.9.1 Summary: N-D labeled arrays and datasets in Python Home-page: https://github.com/pydata/xarray Author: xarray Developers Author-email: xarray@googlegroups.com License: Apache Description: **xarray** (formerly **xray**) is an open source project and Python package that aims to bring the labeled data power of pandas_ to the physical sciences, by providing N-dimensional variants of the core pandas data structures. Our goal is to provide a pandas-like and pandas-compatible toolkit for analytics on multi-dimensional arrays, rather than the tabular data for which pandas excels. Our approach adopts the `Common Data Model`_ for self- describing scientific data in widespread use in the Earth sciences: ``xarray.Dataset`` is an in-memory representation of a netCDF file. .. _pandas: http://pandas.pydata.org .. _Common Data Model: http://www.unidata.ucar.edu/software/thredds/current/netcdf-java/CDM .. _netCDF: http://www.unidata.ucar.edu/software/netcdf .. _OPeNDAP: http://www.opendap.org/ Important links --------------- - HTML documentation: http://xarray.pydata.org - Issue tracker: http://github.com/pydata/xarray/issues - Source code: http://github.com/pydata/xarray - SciPy2015 talk: https://www.youtube.com/watch?v=X0pAhJgySxk Platform: UNKNOWN Classifier: Development Status :: 4 - Beta Classifier: License :: OSI Approved :: Apache Software License Classifier: Operating System :: OS Independent Classifier: Intended Audience :: Science/Research Classifier: Programming Language :: Python Classifier: Programming Language :: Python :: 2 Classifier: Programming Language :: Python :: 2.7 Classifier: Programming Language :: Python :: 3 Classifier: Programming Language :: Python :: 3.4 Classifier: Programming Language :: Python :: 3.5 Classifier: Programming Language :: Python :: 3.6 Classifier: Topic :: Scientific/Engineering ������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������xarray-0.9.1/xarray.egg-info/requires.txt�����������������������������������������������������������0000644�1311115�0011610�00000000036�13043675420�020532� 0����������������������������������������������������������������������������������������������������ustar �shoyer��������������������������eng�����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������numpy >= 1.7 pandas >= 0.15.0 ��������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������xarray-0.9.1/xarray.egg-info/SOURCES.txt������������������������������������������������������������0000644�1311115�0011610�00000005734�13043675421�020031� 0����������������������������������������������������������������������������������������������������ustar �shoyer��������������������������eng�����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������LICENSE MANIFEST.in README.rst setup.cfg setup.py doc/.DS_Store doc/Makefile doc/api-hidden.rst doc/api.rst doc/combining.rst doc/computation.rst doc/conf.py doc/dask.rst doc/data-structures.rst doc/environment.yml doc/examples.rst doc/faq.rst doc/groupby.rst doc/index.rst doc/indexing.rst doc/installing.rst doc/internals.rst doc/io.rst doc/pandas.rst doc/plotting.rst doc/reshaping.rst doc/time-series.rst doc/whats-new.rst doc/why-xarray.rst doc/_static/.DS_Store doc/_static/.gitignore doc/_static/dask_array.png doc/_static/dataset-diagram-build.sh doc/_static/dataset-diagram-logo.png doc/_static/dataset-diagram-logo.tex doc/_static/dataset-diagram.png doc/_static/dataset-diagram.tex doc/_static/opendap-prism-tmax.png doc/examples/.DS_Store doc/examples/examples_pairplot.png doc/examples/monthly-means.rst doc/examples/monthly_means_output.png doc/examples/multidimensional-coords.rst doc/examples/quick-overview.rst doc/examples/weather-data.rst doc/examples/_code/accessor_example.py doc/examples/_code/weather_data_setup.py licenses/NUMPY_LICENSE licenses/PANDAS_LICENSE licenses/PYTHON_LICENSE licenses/SEABORN_LICENSE xarray/__init__.py xarray/conventions.py xarray/convert.py xarray/testing.py xarray/tutorial.py xarray/ufuncs.py xarray/version.py xarray.egg-info/PKG-INFO xarray.egg-info/SOURCES.txt xarray.egg-info/dependency_links.txt xarray.egg-info/requires.txt xarray.egg-info/top_level.txt xarray/backends/__init__.py xarray/backends/api.py xarray/backends/common.py xarray/backends/h5netcdf_.py xarray/backends/memory.py xarray/backends/netCDF4_.py xarray/backends/netcdf3.py xarray/backends/pydap_.py xarray/backends/pynio_.py xarray/backends/scipy_.py xarray/core/__init__.py xarray/core/alignment.py xarray/core/apply_dask_ufunc.py xarray/core/combine.py xarray/core/common.py xarray/core/computation.py xarray/core/coordinates.py xarray/core/dataarray.py xarray/core/dataset.py xarray/core/extensions.py xarray/core/formatting.py xarray/core/groupby.py xarray/core/indexing.py xarray/core/merge.py xarray/core/npcompat.py xarray/core/nputils.py xarray/core/ops.py xarray/core/options.py xarray/core/pycompat.py xarray/core/rolling.py xarray/core/utils.py xarray/core/variable.py xarray/plot/__init__.py xarray/plot/default_colormap.csv xarray/plot/facetgrid.py xarray/plot/plot.py xarray/plot/utils.py xarray/tests/__init__.py xarray/tests/test_backends.py xarray/tests/test_combine.py xarray/tests/test_computation.py xarray/tests/test_conventions.py xarray/tests/test_dask.py xarray/tests/test_dataarray.py xarray/tests/test_dataset.py xarray/tests/test_distributed.py xarray/tests/test_extensions.py xarray/tests/test_formatting.py xarray/tests/test_groupby.py xarray/tests/test_indexing.py xarray/tests/test_merge.py xarray/tests/test_ops.py xarray/tests/test_options.py xarray/tests/test_plot.py xarray/tests/test_tutorial.py xarray/tests/test_ufuncs.py xarray/tests/test_utils.py xarray/tests/test_variable.py xarray/tests/data/bears.nc 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