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notebook.tex
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notebook.tex
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% Default to the notebook output style
% Inherit from the specified cell style.
\documentclass[11pt]{article}
\usepackage[T1]{fontenc}
% Nicer default font (+ math font) than Computer Modern for most use cases
\usepackage{mathpazo}
% Basic figure setup, for now with no caption control since it's done
% automatically by Pandoc (which extracts  syntax from Markdown).
\usepackage{graphicx}
% We will generate all images so they have a width \maxwidth. This means
% that they will get their normal width if they fit onto the page, but
% are scaled down if they would overflow the margins.
\makeatletter
\def\maxwidth{\ifdim\Gin@nat@width>\linewidth\linewidth
\else\Gin@nat@width\fi}
\makeatother
\let\Oldincludegraphics\includegraphics
% Set max figure width to be 80% of text width, for now hardcoded.
\renewcommand{\includegraphics}[1]{\Oldincludegraphics[width=.8\maxwidth]{#1}}
% Ensure that by default, figures have no caption (until we provide a
% proper Figure object with a Caption API and a way to capture that
% in the conversion process - todo).
\usepackage{caption}
\DeclareCaptionLabelFormat{nolabel}{}
\captionsetup{labelformat=nolabel}
\usepackage{adjustbox} % Used to constrain images to a maximum size
\usepackage{xcolor} % Allow colors to be defined
\usepackage{enumerate} % Needed for markdown enumerations to work
\usepackage{geometry} % Used to adjust the document margins
\usepackage{amsmath} % Equations
\usepackage{amssymb} % Equations
\usepackage{textcomp} % defines textquotesingle
% Hack from http://tex.stackexchange.com/a/47451/13684:
\AtBeginDocument{%
\def\PYZsq{\textquotesingle}% Upright quotes in Pygmentized code
}
\usepackage{upquote} % Upright quotes for verbatim code
\usepackage{eurosym} % defines \euro
\usepackage[mathletters]{ucs} % Extended unicode (utf-8) support
\usepackage[utf8x]{inputenc} % Allow utf-8 characters in the tex document
\usepackage{fancyvrb} % verbatim replacement that allows latex
\usepackage{grffile} % extends the file name processing of package graphics
% to support a larger range
% The hyperref package gives us a pdf with properly built
% internal navigation ('pdf bookmarks' for the table of contents,
% internal cross-reference links, web links for URLs, etc.)
\usepackage{hyperref}
\usepackage{longtable} % longtable support required by pandoc >1.10
\usepackage{booktabs} % table support for pandoc > 1.12.2
\usepackage[inline]{enumitem} % IRkernel/repr support (it uses the enumerate* environment)
\usepackage[normalem]{ulem} % ulem is needed to support strikethroughs (\sout)
% normalem makes italics be italics, not underlines
% Colors for the hyperref package
\definecolor{urlcolor}{rgb}{0,.145,.698}
\definecolor{linkcolor}{rgb}{.71,0.21,0.01}
\definecolor{citecolor}{rgb}{.12,.54,.11}
% ANSI colors
\definecolor{ansi-black}{HTML}{3E424D}
\definecolor{ansi-black-intense}{HTML}{282C36}
\definecolor{ansi-red}{HTML}{E75C58}
\definecolor{ansi-red-intense}{HTML}{B22B31}
\definecolor{ansi-green}{HTML}{00A250}
\definecolor{ansi-green-intense}{HTML}{007427}
\definecolor{ansi-yellow}{HTML}{DDB62B}
\definecolor{ansi-yellow-intense}{HTML}{B27D12}
\definecolor{ansi-blue}{HTML}{208FFB}
\definecolor{ansi-blue-intense}{HTML}{0065CA}
\definecolor{ansi-magenta}{HTML}{D160C4}
\definecolor{ansi-magenta-intense}{HTML}{A03196}
\definecolor{ansi-cyan}{HTML}{60C6C8}
\definecolor{ansi-cyan-intense}{HTML}{258F8F}
\definecolor{ansi-white}{HTML}{C5C1B4}
\definecolor{ansi-white-intense}{HTML}{A1A6B2}
% commands and environments needed by pandoc snippets
% extracted from the output of `pandoc -s`
\providecommand{\tightlist}{%
\setlength{\itemsep}{0pt}\setlength{\parskip}{0pt}}
\DefineVerbatimEnvironment{Highlighting}{Verbatim}{commandchars=\\\{\}}
% Add ',fontsize=\small' for more characters per line
\newenvironment{Shaded}{}{}
\newcommand{\KeywordTok}[1]{\textcolor[rgb]{0.00,0.44,0.13}{\textbf{{#1}}}}
\newcommand{\DataTypeTok}[1]{\textcolor[rgb]{0.56,0.13,0.00}{{#1}}}
\newcommand{\DecValTok}[1]{\textcolor[rgb]{0.25,0.63,0.44}{{#1}}}
\newcommand{\BaseNTok}[1]{\textcolor[rgb]{0.25,0.63,0.44}{{#1}}}
\newcommand{\FloatTok}[1]{\textcolor[rgb]{0.25,0.63,0.44}{{#1}}}
\newcommand{\CharTok}[1]{\textcolor[rgb]{0.25,0.44,0.63}{{#1}}}
\newcommand{\StringTok}[1]{\textcolor[rgb]{0.25,0.44,0.63}{{#1}}}
\newcommand{\CommentTok}[1]{\textcolor[rgb]{0.38,0.63,0.69}{\textit{{#1}}}}
\newcommand{\OtherTok}[1]{\textcolor[rgb]{0.00,0.44,0.13}{{#1}}}
\newcommand{\AlertTok}[1]{\textcolor[rgb]{1.00,0.00,0.00}{\textbf{{#1}}}}
\newcommand{\FunctionTok}[1]{\textcolor[rgb]{0.02,0.16,0.49}{{#1}}}
\newcommand{\RegionMarkerTok}[1]{{#1}}
\newcommand{\ErrorTok}[1]{\textcolor[rgb]{1.00,0.00,0.00}{\textbf{{#1}}}}
\newcommand{\NormalTok}[1]{{#1}}
% Additional commands for more recent versions of Pandoc
\newcommand{\ConstantTok}[1]{\textcolor[rgb]{0.53,0.00,0.00}{{#1}}}
\newcommand{\SpecialCharTok}[1]{\textcolor[rgb]{0.25,0.44,0.63}{{#1}}}
\newcommand{\VerbatimStringTok}[1]{\textcolor[rgb]{0.25,0.44,0.63}{{#1}}}
\newcommand{\SpecialStringTok}[1]{\textcolor[rgb]{0.73,0.40,0.53}{{#1}}}
\newcommand{\ImportTok}[1]{{#1}}
\newcommand{\DocumentationTok}[1]{\textcolor[rgb]{0.73,0.13,0.13}{\textit{{#1}}}}
\newcommand{\AnnotationTok}[1]{\textcolor[rgb]{0.38,0.63,0.69}{\textbf{\textit{{#1}}}}}
\newcommand{\CommentVarTok}[1]{\textcolor[rgb]{0.38,0.63,0.69}{\textbf{\textit{{#1}}}}}
\newcommand{\VariableTok}[1]{\textcolor[rgb]{0.10,0.09,0.49}{{#1}}}
\newcommand{\ControlFlowTok}[1]{\textcolor[rgb]{0.00,0.44,0.13}{\textbf{{#1}}}}
\newcommand{\OperatorTok}[1]{\textcolor[rgb]{0.40,0.40,0.40}{{#1}}}
\newcommand{\BuiltInTok}[1]{{#1}}
\newcommand{\ExtensionTok}[1]{{#1}}
\newcommand{\PreprocessorTok}[1]{\textcolor[rgb]{0.74,0.48,0.00}{{#1}}}
\newcommand{\AttributeTok}[1]{\textcolor[rgb]{0.49,0.56,0.16}{{#1}}}
\newcommand{\InformationTok}[1]{\textcolor[rgb]{0.38,0.63,0.69}{\textbf{\textit{{#1}}}}}
\newcommand{\WarningTok}[1]{\textcolor[rgb]{0.38,0.63,0.69}{\textbf{\textit{{#1}}}}}
% Define a nice break command that doesn't care if a line doesn't already
% exist.
\def\br{\hspace*{\fill} \\* }
% Math Jax compatability definitions
\def\gt{>}
\def\lt{<}
% Document parameters
\title{golive3}
% Pygments definitions
\makeatletter
\def\PY@reset{\let\PY@it=\relax \let\PY@bf=\relax%
\let\PY@ul=\relax \let\PY@tc=\relax%
\let\PY@bc=\relax \let\PY@ff=\relax}
\def\PY@tok#1{\csname PY@tok@#1\endcsname}
\def\PY@toks#1+{\ifx\relax#1\empty\else%
\PY@tok{#1}\expandafter\PY@toks\fi}
\def\PY@do#1{\PY@bc{\PY@tc{\PY@ul{%
\PY@it{\PY@bf{\PY@ff{#1}}}}}}}
\def\PY#1#2{\PY@reset\PY@toks#1+\relax+\PY@do{#2}}
\expandafter\def\csname PY@tok@w\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.73,0.73,0.73}{##1}}}
\expandafter\def\csname PY@tok@c\endcsname{\let\PY@it=\textit\def\PY@tc##1{\textcolor[rgb]{0.25,0.50,0.50}{##1}}}
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\expandafter\def\csname PY@tok@sd\endcsname{\let\PY@it=\textit\def\PY@tc##1{\textcolor[rgb]{0.73,0.13,0.13}{##1}}}
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\expandafter\def\csname PY@tok@se\endcsname{\let\PY@bf=\textbf\def\PY@tc##1{\textcolor[rgb]{0.73,0.40,0.13}{##1}}}
\expandafter\def\csname PY@tok@sr\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.73,0.40,0.53}{##1}}}
\expandafter\def\csname PY@tok@ss\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.10,0.09,0.49}{##1}}}
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\expandafter\def\csname PY@tok@gi\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.00,0.63,0.00}{##1}}}
\expandafter\def\csname PY@tok@gr\endcsname{\def\PY@tc##1{\textcolor[rgb]{1.00,0.00,0.00}{##1}}}
\expandafter\def\csname PY@tok@ge\endcsname{\let\PY@it=\textit}
\expandafter\def\csname PY@tok@gs\endcsname{\let\PY@bf=\textbf}
\expandafter\def\csname PY@tok@gp\endcsname{\let\PY@bf=\textbf\def\PY@tc##1{\textcolor[rgb]{0.00,0.00,0.50}{##1}}}
\expandafter\def\csname PY@tok@go\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.53,0.53,0.53}{##1}}}
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\expandafter\def\csname PY@tok@vg\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.10,0.09,0.49}{##1}}}
\expandafter\def\csname PY@tok@vi\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.10,0.09,0.49}{##1}}}
\expandafter\def\csname PY@tok@vm\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.10,0.09,0.49}{##1}}}
\expandafter\def\csname PY@tok@sa\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.73,0.13,0.13}{##1}}}
\expandafter\def\csname PY@tok@sb\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.73,0.13,0.13}{##1}}}
\expandafter\def\csname PY@tok@sc\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.73,0.13,0.13}{##1}}}
\expandafter\def\csname PY@tok@dl\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.73,0.13,0.13}{##1}}}
\expandafter\def\csname PY@tok@s2\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.73,0.13,0.13}{##1}}}
\expandafter\def\csname PY@tok@sh\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.73,0.13,0.13}{##1}}}
\expandafter\def\csname PY@tok@s1\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.73,0.13,0.13}{##1}}}
\expandafter\def\csname PY@tok@mb\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.40,0.40,0.40}{##1}}}
\expandafter\def\csname PY@tok@mf\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.40,0.40,0.40}{##1}}}
\expandafter\def\csname PY@tok@mh\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.40,0.40,0.40}{##1}}}
\expandafter\def\csname PY@tok@mi\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.40,0.40,0.40}{##1}}}
\expandafter\def\csname PY@tok@il\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.40,0.40,0.40}{##1}}}
\expandafter\def\csname PY@tok@mo\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.40,0.40,0.40}{##1}}}
\expandafter\def\csname PY@tok@ch\endcsname{\let\PY@it=\textit\def\PY@tc##1{\textcolor[rgb]{0.25,0.50,0.50}{##1}}}
\expandafter\def\csname PY@tok@cm\endcsname{\let\PY@it=\textit\def\PY@tc##1{\textcolor[rgb]{0.25,0.50,0.50}{##1}}}
\expandafter\def\csname PY@tok@cpf\endcsname{\let\PY@it=\textit\def\PY@tc##1{\textcolor[rgb]{0.25,0.50,0.50}{##1}}}
\expandafter\def\csname PY@tok@c1\endcsname{\let\PY@it=\textit\def\PY@tc##1{\textcolor[rgb]{0.25,0.50,0.50}{##1}}}
\expandafter\def\csname PY@tok@cs\endcsname{\let\PY@it=\textit\def\PY@tc##1{\textcolor[rgb]{0.25,0.50,0.50}{##1}}}
\def\PYZbs{\char`\\}
\def\PYZus{\char`\_}
\def\PYZob{\char`\{}
\def\PYZcb{\char`\}}
\def\PYZca{\char`\^}
\def\PYZam{\char`\&}
\def\PYZlt{\char`\<}
\def\PYZgt{\char`\>}
\def\PYZsh{\char`\#}
\def\PYZpc{\char`\%}
\def\PYZdl{\char`\$}
\def\PYZhy{\char`\-}
\def\PYZsq{\char`\'}
\def\PYZdq{\char`\"}
\def\PYZti{\char`\~}
% for compatibility with earlier versions
\def\PYZat{@}
\def\PYZlb{[}
\def\PYZrb{]}
\makeatother
% Exact colors from NB
\definecolor{incolor}{rgb}{0.0, 0.0, 0.5}
\definecolor{outcolor}{rgb}{0.545, 0.0, 0.0}
% Prevent overflowing lines due to hard-to-break entities
\sloppy
% Setup hyperref package
\hypersetup{
breaklinks=true, % so long urls are correctly broken across lines
colorlinks=true,
urlcolor=urlcolor,
linkcolor=linkcolor,
citecolor=citecolor,
}
% Slightly bigger margins than the latex defaults
\geometry{verbose,tmargin=1in,bmargin=1in,lmargin=1in,rmargin=1in}
\begin{document}
\maketitle
\hypertarget{golive3}{%
\section{golive3}\label{golive3}}
Extend the number of path/row elements out to eight, all with the same
UTM lat/lon zone. (Otherwise coordinates will start getting crossed.)
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}60}]:} \PY{o}{\PYZpc{}}\PY{k}{matplotlib} inline
\PY{o}{!}pip install utm
\PY{o}{!}pip install pygeotools
\PY{k+kn}{import} \PY{n+nn}{timeit}
\PY{k+kn}{from} \PY{n+nn}{datetime} \PY{k}{import} \PY{n}{timedelta}\PY{p}{,} \PY{n}{datetime}
\PY{k+kn}{from} \PY{n+nn}{matplotlib} \PY{k}{import} \PY{n}{pyplot} \PY{k}{as} \PY{n}{plt}
\PY{k+kn}{import} \PY{n+nn}{pandas} \PY{k}{as} \PY{n+nn}{pd}
\PY{k+kn}{import} \PY{n+nn}{netCDF4}
\PY{k+kn}{import} \PY{n+nn}{xarray} \PY{k}{as} \PY{n+nn}{xr}
\PY{k+kn}{import} \PY{n+nn}{dask}
\PY{k+kn}{import} \PY{n+nn}{numpy} \PY{k}{as} \PY{n+nn}{n}
\PY{k+kn}{from} \PY{n+nn}{scipy} \PY{k}{import} \PY{n}{stats}
\PY{k+kn}{from} \PY{n+nn}{scipy}\PY{n+nn}{.}\PY{n+nn}{signal} \PY{k}{import} \PY{n}{medfilt} \PY{k}{as} \PY{n}{mf}
\PY{k+kn}{from} \PY{n+nn}{random} \PY{k}{import} \PY{n}{randint} \PY{k}{as} \PY{n}{ri}
\PY{k+kn}{from} \PY{n+nn}{ipywidgets} \PY{k}{import} \PY{o}{*} \PY{c+c1}{\PYZsh{} interactivity}
\PY{k+kn}{from} \PY{n+nn}{traitlets} \PY{k}{import} \PY{n}{dlink} \PY{c+c1}{\PYZsh{} interactivity}
\PY{k+kn}{import} \PY{n+nn}{sys}
\PY{k+kn}{import} \PY{n+nn}{os}
\PY{k+kn}{import} \PY{n+nn}{utm}
\PY{c+c1}{\PYZsh{} from osgeo import gdal }
\PY{c+c1}{\PYZsh{} from pygeotools.lib import iolib,timelib}
\PY{k+kn}{import} \PY{n+nn}{golive\PYZus{}utility} \PY{k}{as} \PY{n+nn}{g}
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
Requirement already satisfied: utm in /opt/conda/lib/python3.6/site-packages
\textcolor{ansi-yellow}{You are using pip version 9.0.1, however version 19.0.3 is available.
You should consider upgrading via the 'pip install --upgrade pip' command.}
Requirement already satisfied: pygeotools in /opt/conda/lib/python3.6/site-packages
Requirement already satisfied: scipy in /opt/conda/lib/python3.6/site-packages (from pygeotools)
Requirement already satisfied: gdal in /opt/conda/lib/python3.6/site-packages (from pygeotools)
Requirement already satisfied: matplotlib in /opt/conda/lib/python3.6/site-packages (from pygeotools)
Requirement already satisfied: numpy in /opt/conda/lib/python3.6/site-packages (from pygeotools)
Requirement already satisfied: cycler>=0.10 in /opt/conda/lib/python3.6/site-packages (from matplotlib->pygeotools)
Requirement already satisfied: kiwisolver>=1.0.1 in /opt/conda/lib/python3.6/site-packages (from matplotlib->pygeotools)
Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /opt/conda/lib/python3.6/site-packages (from matplotlib->pygeotools)
Requirement already satisfied: python-dateutil>=2.1 in /opt/conda/lib/python3.6/site-packages (from matplotlib->pygeotools)
Requirement already satisfied: six in /opt/conda/lib/python3.6/site-packages (from cycler>=0.10->matplotlib->pygeotools)
Requirement already satisfied: setuptools in /opt/conda/lib/python3.6/site-packages (from kiwisolver>=1.0.1->matplotlib->pygeotools)
\textcolor{ansi-yellow}{You are using pip version 9.0.1, however version 19.0.3 is available.
You should consider upgrading via the 'pip install --upgrade pip' command.}
\end{Verbatim}
\hypertarget{utm-zone-stuff}{%
\subsubsection{UTM zone stuff}\label{utm-zone-stuff}}
Coordinates are Paths 60, 61, 62, 63, 64, 65 x Rows 17 and 18. However
the \texttt{x} and \texttt{y} coordinates are degenerate from one to
another when the UTM zone changes, specifically in longitude. This we
can pull from a dataset using this function:
\begin{verbatim}
def GetMeridian(ds):
pstring = ds.input_image_details.attrs['image1_proj_WKT']
locale = pstring.find('central_meridian')
return int(pstring[locale+18:locale+22])
print(GetMeridian(dataset))
\end{verbatim}
Using this on some exemplary data files shows that a block of eight
path-row frames lie within the same UTM zone (meridian -141).
\begin{verbatim}
65 17 64 17 63 17 62 17
64 18 63 18 62 18 61 18
\end{verbatim}
This extends the \textbf{golive2} notebook by a factor of 8 without
running afoul of coordinate ambiguity. The following cell uses
\texttt{wget} to pull the respective data.
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}2}]:} \PY{o}{\PYZpc{}\PYZpc{}}\PY{k}{time}
if False:
\PYZsh{} This takes about three minutes per path\PYZhy{}row frame
\PYZsh{} Pull NetCDF files from the GoLIVE ftp server; by path\PYZhy{}row frames
!wget \PYZhy{}P /home/jovyan/data/golive3 ftp://dtn.rc.colorado.edu/work/nsidc0710/nsidc0710\PYZus{}landsat8\PYZus{}golive\PYZus{}ice\PYZus{}velocity\PYZus{}v1.1/p065\PYZus{}r017/*.nc
!wget \PYZhy{}P /home/jovyan/data/golive3 ftp://dtn.rc.colorado.edu/work/nsidc0710/nsidc0710\PYZus{}landsat8\PYZus{}golive\PYZus{}ice\PYZus{}velocity\PYZus{}v1.1/p064\PYZus{}r018/*.nc
!wget \PYZhy{}P /home/jovyan/data/golive3 ftp://dtn.rc.colorado.edu/work/nsidc0710/nsidc0710\PYZus{}landsat8\PYZus{}golive\PYZus{}ice\PYZus{}velocity\PYZus{}v1.1/p064\PYZus{}r017/*.nc
!wget \PYZhy{}P /home/jovyan/data/golive3 ftp://dtn.rc.colorado.edu/work/nsidc0710/nsidc0710\PYZus{}landsat8\PYZus{}golive\PYZus{}ice\PYZus{}velocity\PYZus{}v1.1/p063\PYZus{}r018/*.nc
!wget \PYZhy{}P /home/jovyan/data/golive3 ftp://dtn.rc.colorado.edu/work/nsidc0710/nsidc0710\PYZus{}landsat8\PYZus{}golive\PYZus{}ice\PYZus{}velocity\PYZus{}v1.1/p063\PYZus{}r017/*.nc
!wget \PYZhy{}P /home/jovyan/data/golive3 ftp://dtn.rc.colorado.edu/work/nsidc0710/nsidc0710\PYZus{}landsat8\PYZus{}golive\PYZus{}ice\PYZus{}velocity\PYZus{}v1.1/p062\PYZus{}r018/*.nc
!wget \PYZhy{}P /home/jovyan/data/golive3 ftp://dtn.rc.colorado.edu/work/nsidc0710/nsidc0710\PYZus{}landsat8\PYZus{}golive\PYZus{}ice\PYZus{}velocity\PYZus{}v1.1/p062\PYZus{}r017/*.nc
!wget \PYZhy{}P /home/jovyan/data/golive3 ftp://dtn.rc.colorado.edu/work/nsidc0710/nsidc0710\PYZus{}landsat8\PYZus{}golive\PYZus{}ice\PYZus{}velocity\PYZus{}v1.1/p061\PYZus{}r018/*.nc
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
CPU times: user 3 µs, sys: 0 ns, total: 3 µs
Wall time: 6.44 µs
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}44}]:} \PY{o}{\PYZpc{}\PYZpc{}}\PY{k}{time}
\PYZsh{} because the files are redundant: Use the data directory for the golive3 as the source
golive3\PYZus{}d = g.SetDataDirectory(\PYZsq{}golive3\PYZsq{})
tslist = []
sourceFiles = golive3\PYZus{}d + \PYZsq{}L8*016\PYZus{}2014*.nc\PYZsq{}
\PYZsh{} sourceFiles = golive3\PYZus{}d + \PYZsq{}L8*.nc\PYZsq{}
def pp(d):
global tslist
s = d[\PYZsq{}image\PYZus{}pair\PYZus{}times\PYZsq{}].attrs[\PYZsq{}mid\PYZus{}date\PYZsq{}]
ts = pd.Timestamp(s)
while ts in tslist: ts += timedelta(hours = 1)
tslist.append(ts)
d[\PYZsq{}time\PYZsq{}] = xr.Variable(\PYZsq{}time\PYZsq{}, [ts])
return d
\PYZsh{} This next line \PYZsq{}works\PYZsq{} but breaks the kernel when using the entire source dataset. My guess is that
\PYZsh{} 2000\PYZhy{}plus files are too much metadata for xarray to deal with in memory (let alone the data of course).
\PYZsh{} For the sake of getting it to work a data subset can be selected for example with
\PYZsh{} sourceFiles = golive3\PYZus{}d + \PYZsq{}L8*016\PYZus{}2014*.nc\PYZsq{}
\PYZsh{} where the wildcards give 75 files, less than the 255 from golive2; then we have
\PYZsh{}
\PYZsh{} ds = xr.open\PYZus{}mfdataset(sourceFiles, preprocess = pp, decode\PYZus{}cf = False, autoclose = True, concat\PYZus{}dim=\PYZsq{}time\PYZsq{})
\PYZsh{}
\PYZsh{} Whereas a more ambitious idea would be to preserve the time axis entire in dask chunks
\PYZsh{} There are 2103 source files and time becomes the first axis. sqrt(1e6/2103) is about 22 so...
if False:
ds = xr.open\PYZus{}mfdataset(sourceFiles, preprocess = pp, decode\PYZus{}cf = False, \PYZbs{}
autoclose = True, concat\PYZus{}dim=\PYZsq{}time\PYZsq{})
\PYZsh{} ds.vv\PYZus{}masked[0].plot(figsize=(12,8), cmap=plt.cm.rainbow, vmin=0., vmax=4.)
ds
\PYZsh{} focus on the interesting part of the image...
\PYZsh{} plt.xlim(480000,570000)
\PYZsh{} plt.ylim(6630000,6694000)
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
CPU times: user 62 µs, sys: 26 µs, total: 88 µs
Wall time: 94.2 µs
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}4}]:} \PY{k}{if} \PY{k+kc}{False}\PY{p}{:}
\PY{c+c1}{\PYZsh{} print(tslist)}
\PY{n+nb}{print}\PY{p}{(}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{open\PYZus{}mfdataset(etc) yielded}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,} \PY{n+nb}{len}\PY{p}{(}\PY{n}{tslist}\PY{p}{)}\PY{p}{,} \PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{files.}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}
\PY{n}{vv}\PY{o}{=}\PY{n}{ds}\PY{o}{.}\PY{n}{vv\PYZus{}masked}\PY{o}{.}\PY{n}{load}\PY{p}{(}\PY{p}{)}
\PY{n}{lgo}\PY{o}{=}\PY{n}{ds}\PY{o}{.}\PY{n}{lgo\PYZus{}mask}\PY{o}{.}\PY{n}{load}\PY{p}{(}\PY{p}{)}
\PY{n}{vv}\PY{o}{.}\PY{n}{to\PYZus{}netcdf}\PY{p}{(}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{../data/golive3/go\PYZus{}vv\PYZus{}16day\PYZus{}2014.nc}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}
\PY{n}{lgo}\PY{o}{.}\PY{n}{to\PYZus{}netcdf}\PY{p}{(}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{../data/golive3/go\PYZus{}lgo\PYZus{}16day\PYZus{}2014.nc}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}5}]:} \PY{k}{if} \PY{k+kc}{True}\PY{p}{:}
\PY{n}{vvds}\PY{o}{=}\PY{n}{xr}\PY{o}{.}\PY{n}{open\PYZus{}dataset}\PY{p}{(}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{../data/golive3/go\PYZus{}vv\PYZus{}16day\PYZus{}2014.nc}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}
\PY{n}{vv}\PY{o}{=}\PY{n}{vvds}\PY{o}{.}\PY{n}{vv\PYZus{}masked}
\PY{n}{lgods}\PY{o}{=}\PY{n}{xr}\PY{o}{.}\PY{n}{open\PYZus{}dataset}\PY{p}{(}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{../data/golive3/go\PYZus{}lgo\PYZus{}16day\PYZus{}2014.nc}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}
\PY{n}{lgo}\PY{o}{=}\PY{n}{lgods}\PY{o}{.}\PY{n}{lgo\PYZus{}mask}
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}6}]:} \PY{k}{if} \PY{k+kc}{True}\PY{p}{:}
\PY{n+nb}{type}\PY{p}{(}\PY{n}{vv}\PY{p}{)}
\PY{n+nb}{print}\PY{p}{(}\PY{n}{vv}\PY{p}{)}
\PY{c+c1}{\PYZsh{} This thread was abandoned}
\PY{c+c1}{\PYZsh{} nvalues = 75*1361*1773}
\PY{c+c1}{\PYZsh{} vvcount = int(vv.count())}
\PY{c+c1}{\PYZsh{} count\PYZus{}nodata = int(vv.where(vv.data == \PYZhy{}9999.0).count())}
\PY{c+c1}{\PYZsh{} print(vvcount, count\PYZus{}nodata, vvcount + count\PYZus{}nodata, nvalues)}
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
<xarray.DataArray 'vv\_masked' (time: 75, y: 1361, x: 1773)>
[180978975 values with dtype=float32]
Coordinates:
* time (time) datetime64[ns] 2014-01-10 2014-01-10T01:00:00 {\ldots} 2015-01-06
* x (x) float64 2.2e+05 2.203e+05 2.206e+05 {\ldots} 7.513e+05 7.516e+05
* y (y) float64 6.537e+06 6.538e+06 6.538e+06 {\ldots} 6.945e+06 6.945e+06
Attributes:
grid\_mapping: transverse\_mercator
standard\_name: speed\_masked
long\_name: magnitude of velocity (masked)
units: m/d
masking\_info: masked\_where(((del\_corr\_arr<0.100)\&(corr\_arr<1.000))|(cor{\ldots}
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}7}]:} \PY{n}{vv\PYZus{}mean1} \PY{o}{=} \PY{n}{vv}\PY{o}{.}\PY{n}{where}\PY{p}{(}\PY{n}{vv}\PY{o}{.}\PY{n}{data} \PY{o}{!=} \PY{o}{\PYZhy{}}\PY{l+m+mf}{9999.}\PY{p}{)}
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}8}]:} \PY{n}{vv\PYZus{}mean\PYZus{}q} \PY{o}{=} \PY{n}{vv}\PY{o}{.}\PY{n}{where}\PY{p}{(}\PY{n}{vv}\PY{o}{.}\PY{n}{data} \PY{o}{!=} \PY{o}{\PYZhy{}}\PY{l+m+mf}{9999.0}\PY{p}{)}
\PY{n}{vv\PYZus{}mean\PYZus{}q1} \PY{o}{=} \PY{n}{vv\PYZus{}mean\PYZus{}q}\PY{o}{.}\PY{n}{dropna}\PY{p}{(}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{time}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}9}]:} \PY{n}{vv\PYZus{}mean\PYZus{}q1}\PY{o}{.}\PY{n}{shape}
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{outcolor}Out[{\color{outcolor}9}]:} (0, 1361, 1773)
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}10}]:} \PY{n}{vv\PYZus{}mean2} \PY{o}{=} \PY{n}{vv\PYZus{}mean1}\PY{o}{.}\PY{n}{mean}\PY{p}{(}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{time}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
/opt/conda/lib/python3.6/site-packages/xarray/core/nanops.py:162: RuntimeWarning: Mean of empty slice
return np.nanmean(a, axis=axis, dtype=dtype)
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}11}]:} \PY{k}{if} \PY{k+kc}{False}\PY{p}{:}
\PY{n}{vv\PYZus{}mean2}\PY{o}{.}\PY{n}{to\PYZus{}netcdf}\PY{p}{(}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{../data/golive3/go\PYZus{}2014\PYZus{}16day\PYZus{}mean.nc}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}12}]:} \PY{n}{vv\PYZus{}mean2}\PY{o}{.}\PY{n}{shape}
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{outcolor}Out[{\color{outcolor}12}]:} (1361, 1773)
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}13}]:} \PY{n}{vv\PYZus{}mean2}\PY{o}{.}\PY{n}{plot}\PY{p}{(}\PY{n}{figsize}\PY{o}{=}\PY{p}{(}\PY{l+m+mi}{16}\PY{p}{,}\PY{l+m+mi}{16}\PY{p}{)}\PY{p}{,} \PY{n}{cmap}\PY{o}{=}\PY{n}{plt}\PY{o}{.}\PY{n}{cm}\PY{o}{.}\PY{n}{rainbow}\PY{p}{,} \PY{n}{vmin}\PY{o}{=}\PY{l+m+mf}{0.}\PY{p}{,} \PY{n}{vmax}\PY{o}{=}\PY{l+m+mf}{0.8}\PY{p}{)}
\PY{c+c1}{\PYZsh{} plt.xlim(220000,490000)}
\PY{c+c1}{\PYZsh{} plt.ylim(6700000,6970000)}
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{outcolor}Out[{\color{outcolor}13}]:} <matplotlib.collections.QuadMesh at 0x7f1391874240>
\end{Verbatim}
\begin{center}
\adjustimage{max size={0.9\linewidth}{0.9\paperheight}}{output_14_1.png}
\end{center}
{ \hspace*{\fill} \\}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}14}]:} \PY{n}{lgo1}\PY{o}{=}\PY{n}{lgo}\PY{o}{.}\PY{n}{where}\PY{p}{(}\PY{n}{lgo}\PY{o}{.}\PY{n}{data} \PY{o}{\PYZlt{}} \PY{l+m+mf}{2.}\PY{p}{)}
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
/opt/conda/lib/python3.6/site-packages/ipykernel/\_\_main\_\_.py:1: RuntimeWarning: invalid value encountered in less
if \_\_name\_\_ == '\_\_main\_\_':
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}15}]:} \PY{n}{lgo2}\PY{o}{=}\PY{n}{lgo1}\PY{o}{.}\PY{n}{mean}\PY{p}{(}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{time}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}
\PY{n}{lgo3}\PY{o}{=}\PY{n}{lgo2}\PY{o}{.}\PY{n}{where}\PY{p}{(}\PY{n}{lgo2}\PY{o}{.}\PY{n}{data} \PY{o}{\PYZlt{}} \PY{l+m+mf}{0.8}\PY{p}{)} \PY{c+c1}{\PYZsh{} becomes a mask: Not ocean, at least 20\PYZpc{} votes for glacier ice}
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
/opt/conda/lib/python3.6/site-packages/xarray/core/nanops.py:162: RuntimeWarning: Mean of empty slice
return np.nanmean(a, axis=axis, dtype=dtype)
/opt/conda/lib/python3.6/site-packages/ipykernel/\_\_main\_\_.py:2: RuntimeWarning: invalid value encountered in less
from ipykernel import kernelapp as app
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}16}]:} \PY{n}{lgo3}\PY{o}{.}\PY{n}{plot}\PY{p}{(}\PY{n}{figsize}\PY{o}{=}\PY{p}{(}\PY{l+m+mi}{16}\PY{p}{,}\PY{l+m+mi}{16}\PY{p}{)}\PY{p}{,} \PY{n}{cmap}\PY{o}{=}\PY{n}{plt}\PY{o}{.}\PY{n}{cm}\PY{o}{.}\PY{n}{rainbow}\PY{p}{,} \PY{n}{vmin}\PY{o}{=}\PY{l+m+mf}{0.}\PY{p}{,} \PY{n}{vmax}\PY{o}{=}\PY{l+m+mf}{1.0}\PY{p}{)}
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{outcolor}Out[{\color{outcolor}16}]:} <matplotlib.collections.QuadMesh at 0x7f138f76f198>
\end{Verbatim}
\begin{center}
\adjustimage{max size={0.9\linewidth}{0.9\paperheight}}{output_17_1.png}
\end{center}
{ \hspace*{\fill} \\}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}17}]:} \PY{n}{vv\PYZus{}mean3}\PY{o}{=}\PY{n}{vv\PYZus{}mean2}\PY{o}{.}\PY{n}{where}\PY{p}{(}\PY{n}{lgo3}\PY{o}{.}\PY{n}{data} \PY{o}{\PYZgt{}}\PY{o}{=} \PY{l+m+mf}{0.}\PY{p}{)}
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
/opt/conda/lib/python3.6/site-packages/ipykernel/\_\_main\_\_.py:1: RuntimeWarning: invalid value encountered in greater\_equal
if \_\_name\_\_ == '\_\_main\_\_':
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}18}]:} \PY{n}{vv\PYZus{}mean3}\PY{o}{.}\PY{n}{plot}\PY{p}{(}\PY{n}{figsize}\PY{o}{=}\PY{p}{(}\PY{l+m+mi}{16}\PY{p}{,}\PY{l+m+mi}{16}\PY{p}{)}\PY{p}{,} \PY{n}{cmap}\PY{o}{=}\PY{n}{plt}\PY{o}{.}\PY{n}{cm}\PY{o}{.}\PY{n}{rainbow}\PY{p}{,} \PY{n}{vmin}\PY{o}{=}\PY{l+m+mf}{0.}\PY{p}{,} \PY{n}{vmax}\PY{o}{=}\PY{l+m+mf}{0.8}\PY{p}{)}
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{outcolor}Out[{\color{outcolor}18}]:} <matplotlib.collections.QuadMesh at 0x7f138f712ac8>
\end{Verbatim}
\begin{center}
\adjustimage{max size={0.9\linewidth}{0.9\paperheight}}{output_19_1.png}
\end{center}
{ \hspace*{\fill} \\}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}19}]:} \PY{k}{if} \PY{k+kc}{False}\PY{p}{:}
\PY{n}{xHi} \PY{o}{=} \PY{n}{ds}\PY{o}{.}\PY{n}{dims}\PY{p}{[}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{x}\PY{l+s+s1}{\PYZsq{}}\PY{p}{]} \PY{o}{\PYZhy{}} \PY{l+m+mi}{1}
\PY{n}{yHi} \PY{o}{=} \PY{n}{ds}\PY{o}{.}\PY{n}{dims}\PY{p}{[}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{y}\PY{l+s+s1}{\PYZsq{}}\PY{p}{]} \PY{o}{\PYZhy{}} \PY{l+m+mi}{1}
\PY{n}{n0} \PY{o}{=} \PY{n}{ds}\PY{o}{.}\PY{n}{y}\PY{p}{[}\PY{l+m+mi}{0}\PY{p}{]}\PY{o}{.}\PY{n}{data}
\PY{n}{n1} \PY{o}{=} \PY{n}{ds}\PY{o}{.}\PY{n}{y}\PY{p}{[}\PY{n}{yHi}\PY{p}{]}\PY{o}{.}\PY{n}{data}
\PY{n}{e0} \PY{o}{=} \PY{n}{ds}\PY{o}{.}\PY{n}{x}\PY{p}{[}\PY{l+m+mi}{0}\PY{p}{]}\PY{o}{.}\PY{n}{data}
\PY{n}{e1} \PY{o}{=} \PY{n}{ds}\PY{o}{.}\PY{n}{x}\PY{p}{[}\PY{n}{xHi}\PY{p}{]}\PY{o}{.}\PY{n}{data}
\PY{n+nb}{print}\PY{p}{(}\PY{n}{e0}\PY{p}{,} \PY{n}{e1}\PY{p}{,} \PY{n}{n0}\PY{p}{,} \PY{n}{n1}\PY{p}{)}
\PY{n+nb}{print}\PY{p}{(}\PY{n}{utm}\PY{o}{.}\PY{n}{to\PYZus{}latlon}\PY{p}{(}\PY{n}{e0}\PY{p}{,} \PY{n}{n0}\PY{p}{,} \PY{l+m+mi}{7}\PY{p}{,} \PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{V}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}\PY{p}{)}
\PY{n+nb}{print}\PY{p}{(}\PY{n}{utm}\PY{o}{.}\PY{n}{to\PYZus{}latlon}\PY{p}{(}\PY{n}{e1}\PY{p}{,} \PY{n}{n0}\PY{p}{,} \PY{l+m+mi}{7}\PY{p}{,} \PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{V}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}\PY{p}{)}
\PY{n+nb}{print}\PY{p}{(}\PY{n}{utm}\PY{o}{.}\PY{n}{to\PYZus{}latlon}\PY{p}{(}\PY{n}{e0}\PY{p}{,} \PY{n}{n1}\PY{p}{,} \PY{l+m+mi}{7}\PY{p}{,} \PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{V}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}\PY{p}{)}
\PY{n+nb}{print}\PY{p}{(}\PY{n}{utm}\PY{o}{.}\PY{n}{to\PYZus{}latlon}\PY{p}{(}\PY{n}{e1}\PY{p}{,} \PY{n}{n1}\PY{p}{,} \PY{l+m+mi}{7}\PY{p}{,} \PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{V}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}\PY{p}{)}
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}20}]:} \PY{k}{def} \PY{n+nf}{ls\PYZus{}slope}\PY{p}{(}\PY{n}{x}\PY{p}{,}\PY{n}{y}\PY{p}{)}\PY{p}{:}
\PY{l+s+sd}{\PYZsq{}\PYZsq{}\PYZsq{} simple least squares fit slope (degree 1)\PYZsq{}\PYZsq{}\PYZsq{}}
\PY{k}{return} \PY{n}{np}\PY{o}{.}\PY{n}{polyfit}\PY{p}{(}\PY{n}{x}\PY{p}{,} \PY{n}{y}\PY{p}{,} \PY{l+m+mi}{1}\PY{p}{)}\PY{p}{[}\PY{l+m+mi}{0}\PY{p}{]}
\PY{k}{def} \PY{n+nf}{ls\PYZus{}offset}\PY{p}{(}\PY{n}{x}\PY{p}{,}\PY{n}{y}\PY{p}{)}\PY{p}{:}
\PY{l+s+sd}{\PYZsq{}\PYZsq{}\PYZsq{} simple least squares fit offset (degree 0)\PYZsq{}\PYZsq{}\PYZsq{}}
\PY{k}{return} \PY{n}{np}\PY{o}{.}\PY{n}{polyfit}\PY{p}{(}\PY{n}{x}\PY{p}{,} \PY{n}{y}\PY{p}{,} \PY{l+m+mi}{1}\PY{p}{)}\PY{p}{[}\PY{l+m+mi}{1}\PY{p}{]}
\PY{k}{def} \PY{n+nf}{get\PYZus{}slope}\PY{p}{(}\PY{n}{x}\PY{p}{,} \PY{n}{y}\PY{p}{,} \PY{n}{dim}\PY{o}{=}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{time}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}\PY{p}{:}
\PY{c+c1}{\PYZsh{} x = Pixel value, y = a vector containing the date, dim == dimension}
\PY{n}{DS1} \PY{o}{=} \PY{n}{xr}\PY{o}{.}\PY{n}{apply\PYZus{}ufunc}\PY{p}{(}
\PY{n}{ls\PYZus{}slope}\PY{p}{,} \PY{n}{x} \PY{p}{,} \PY{n}{y}\PY{p}{,}
\PY{n}{input\PYZus{}core\PYZus{}dims}\PY{o}{=}\PY{p}{[}\PY{p}{[}\PY{n}{dim}\PY{p}{]}\PY{p}{,} \PY{p}{[}\PY{n}{dim}\PY{p}{]}\PY{p}{]}\PY{p}{,}
\PY{n}{vectorize}\PY{o}{=}\PY{k+kc}{True}\PY{p}{,}
\PY{n}{dask}\PY{o}{=}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{parallelized}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,}
\PY{n}{output\PYZus{}dtypes}\PY{o}{=}\PY{p}{[}\PY{n+nb}{float}\PY{p}{]}\PY{p}{,}
\PY{p}{)}
\PY{k}{return} \PY{n}{DS1}\PY{o}{.}\PY{n}{rename}\PY{p}{(}\PY{p}{\PYZob{}}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{speedfield}\PY{l+s+s1}{\PYZsq{}}\PY{p}{:}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{p0}\PY{l+s+s1}{\PYZsq{}}\PY{p}{\PYZcb{}}\PY{p}{)}
\PY{k}{def} \PY{n+nf}{get\PYZus{}intercept}\PY{p}{(}\PY{n}{x}\PY{p}{,} \PY{n}{y}\PY{p}{,} \PY{n}{dim}\PY{o}{=}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{time}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}\PY{p}{:}
\PY{n}{DS2} \PY{o}{=} \PY{n}{xr}\PY{o}{.}\PY{n}{apply\PYZus{}ufunc}\PY{p}{(}
\PY{n}{ls\PYZus{}offset}\PY{p}{,} \PY{n}{x} \PY{p}{,} \PY{n}{y}\PY{p}{,}
\PY{n}{input\PYZus{}core\PYZus{}dims}\PY{o}{=}\PY{p}{[}\PY{p}{[}\PY{n}{dim}\PY{p}{]}\PY{p}{,} \PY{p}{[}\PY{n}{dim}\PY{p}{]}\PY{p}{]}\PY{p}{,}
\PY{n}{vectorize}\PY{o}{=}\PY{k+kc}{True}\PY{p}{,}
\PY{n}{dask}\PY{o}{=}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{parallelized}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,}
\PY{n}{output\PYZus{}dtypes}\PY{o}{=}\PY{p}{[}\PY{n+nb}{float}\PY{p}{]}\PY{p}{,}
\PY{p}{)}
\PY{k}{return} \PY{n}{DS2}\PY{o}{.}\PY{n}{rename}\PY{p}{(}\PY{p}{\PYZob{}}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{speedfield}\PY{l+s+s1}{\PYZsq{}}\PY{p}{:}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{p1}\PY{l+s+s1}{\PYZsq{}}\PY{p}{\PYZcb{}}\PY{p}{)}
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}21}]:} \PY{c+c1}{\PYZsh{} Remove \PYZhy{}9999.0 values from the vv DataArray (here they become nan)}
\PY{n}{vvnan} \PY{o}{=} \PY{n}{vv}\PY{o}{.}\PY{n}{where}\PY{p}{(}\PY{n}{vv}\PY{o}{.}\PY{n}{data} \PY{o}{\PYZgt{}}\PY{o}{=} \PY{l+m+mf}{0.}\PY{p}{)}
\PY{n}{vvds}\PY{o}{=}\PY{n}{vvnan}\PY{o}{.}\PY{n}{to\PYZus{}dataset}\PY{p}{(}\PY{n}{name}\PY{o}{=}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{speedfield}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}
\PY{n+nb}{print}\PY{p}{(}\PY{n}{vvds}\PY{p}{)}
\PY{n}{vvds}\PY{p}{[}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{speedfield}\PY{l+s+s1}{\PYZsq{}}\PY{p}{]}\PY{o}{.}\PY{n}{values}
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
/opt/conda/lib/python3.6/site-packages/ipykernel/\_\_main\_\_.py:2: RuntimeWarning: invalid value encountered in greater\_equal
from ipykernel import kernelapp as app
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
<xarray.Dataset>
Dimensions: (time: 75, x: 1773, y: 1361)
Coordinates:
* time (time) datetime64[ns] 2014-01-10 {\ldots} 2015-01-06
* x (x) float64 2.2e+05 2.203e+05 2.206e+05 {\ldots} 7.513e+05 7.516e+05
* y (y) float64 6.537e+06 6.538e+06 {\ldots} 6.945e+06 6.945e+06
Data variables:
speedfield (time, y, x) float32 nan nan nan nan nan {\ldots} nan nan nan nan nan
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{outcolor}Out[{\color{outcolor}21}]:} array([[[nan, nan, nan, {\ldots}, nan, nan, nan],
[nan, nan, nan, {\ldots}, nan, nan, nan],
[nan, nan, nan, {\ldots}, nan, nan, nan],
{\ldots},
[nan, nan, nan, {\ldots}, nan, nan, nan],
[nan, nan, nan, {\ldots}, nan, nan, nan],
[nan, nan, nan, {\ldots}, nan, nan, nan]],
[[nan, nan, nan, {\ldots}, nan, nan, nan],
[nan, nan, nan, {\ldots}, nan, nan, nan],
[nan, nan, nan, {\ldots}, nan, nan, nan],
{\ldots},
[nan, nan, nan, {\ldots}, nan, nan, nan],
[nan, nan, nan, {\ldots}, nan, nan, nan],
[nan, nan, nan, {\ldots}, nan, nan, nan]],
[[nan, nan, nan, {\ldots}, nan, nan, nan],
[nan, nan, nan, {\ldots}, nan, nan, nan],
[nan, nan, nan, {\ldots}, nan, nan, nan],
{\ldots},
[nan, nan, nan, {\ldots}, nan, nan, nan],
[nan, nan, nan, {\ldots}, nan, nan, nan],
[nan, nan, nan, {\ldots}, nan, nan, nan]],
{\ldots},
[[nan, nan, nan, {\ldots}, nan, nan, nan],
[nan, nan, nan, {\ldots}, nan, nan, nan],
[nan, nan, nan, {\ldots}, nan, nan, nan],
{\ldots},
[nan, nan, nan, {\ldots}, nan, nan, nan],
[nan, nan, nan, {\ldots}, nan, nan, nan],
[nan, nan, nan, {\ldots}, nan, nan, nan]],
[[nan, nan, nan, {\ldots}, nan, nan, nan],
[nan, nan, nan, {\ldots}, nan, nan, nan],
[nan, nan, nan, {\ldots}, nan, nan, nan],
{\ldots},
[nan, nan, nan, {\ldots}, nan, nan, nan],
[nan, nan, nan, {\ldots}, nan, nan, nan],
[nan, nan, nan, {\ldots}, nan, nan, nan]],
[[nan, nan, nan, {\ldots}, nan, nan, nan],
[nan, nan, nan, {\ldots}, nan, nan, nan],
[nan, nan, nan, {\ldots}, nan, nan, nan],
{\ldots},
[nan, nan, nan, {\ldots}, nan, nan, nan],
[nan, nan, nan, {\ldots}, nan, nan, nan],
[nan, nan, nan, {\ldots}, nan, nan, nan]]], dtype=float32)
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}22}]:} \PY{n+nb}{print}\PY{p}{(}\PY{n}{vvds}\PY{o}{.}\PY{n}{speedfield}\PY{o}{.}\PY{n}{sel}\PY{p}{(}\PY{n}{y}\PY{o}{=}\PY{l+m+mf}{6710000.}\PY{p}{,}\PY{n}{x}\PY{o}{=}\PY{l+m+mf}{500000.}\PY{p}{,}\PY{n}{method}\PY{o}{=}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{nearest}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}\PY{o}{.}\PY{n}{data}\PY{p}{)}
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
[ nan nan nan nan nan nan
nan nan nan nan nan nan
nan nan nan nan nan nan
nan nan nan nan nan nan
0.3805175 nan 0.45239696 nan 0.15132774 nan
nan 0.19357407 nan 0.05995712 nan nan
nan nan nan 3.3384483 nan nan
nan nan nan nan 0.29603022 nan
nan 0.25648597 nan nan nan nan
nan nan nan nan nan nan
0.3377338 nan nan nan nan nan
nan nan nan nan nan nan
nan nan nan]
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}23}]:} \PY{c+c1}{\PYZsh{} Scott configures the source dataset as time\PYZhy{}series 2D spatial array of (in time) noisy linear trends}
\PY{k}{if} \PY{k+kc}{False}\PY{p}{:}
\PY{c+c1}{\PYZsh{} Give each pixel a different set of noise}
\PY{n}{nt}\PY{p}{,} \PY{n}{ny}\PY{p}{,} \PY{n}{nx} \PY{o}{=} \PY{l+m+mi}{5}\PY{p}{,} \PY{l+m+mi}{4}\PY{p}{,} \PY{l+m+mi}{3}
\PY{n}{images} \PY{o}{=} \PY{n}{np}\PY{o}{.}\PY{n}{ones}\PY{p}{(}\PY{p}{[}\PY{n}{ny}\PY{p}{,}\PY{n}{nx}\PY{p}{,}\PY{n}{nt}\PY{p}{]}\PY{p}{)}
\PY{n}{values} \PY{o}{=} \PY{n}{np}\PY{o}{.}\PY{n}{arange}\PY{p}{(}\PY{n}{nt}\PY{p}{)}
\PY{n}{noise} \PY{o}{=} \PY{n}{np}\PY{o}{.}\PY{n}{random}\PY{o}{.}\PY{n}{random}\PY{p}{(}\PY{p}{[}\PY{n}{ny}\PY{p}{,}\PY{n}{nx}\PY{p}{,}\PY{n}{nt}\PY{p}{]}\PY{p}{)}
\PY{c+c1}{\PYZsh{} NOTE: more slope variability:}
\PY{c+c1}{\PYZsh{}cube = (images*values*noise).T}
\PY{c+c1}{\PYZsh{} alternatively:}
\PY{n}{cube} \PY{o}{=} \PY{p}{(}\PY{p}{(}\PY{n}{images}\PY{o}{*}\PY{n}{values}\PY{p}{)} \PY{o}{+} \PY{n}{noise}\PY{p}{)}\PY{o}{.}\PY{n}{T}
\PY{c+c1}{\PYZsh{}time = pd.date\PYZus{}range(start=\PYZsq{}1950\PYZhy{}01\PYZhy{}01\PYZsq{}, periods=nt, freq=\PYZsq{}10D\PYZsq{})}
\PY{n}{time} \PY{o}{=} \PY{n}{np}\PY{o}{.}\PY{n}{arange}\PY{p}{(}\PY{n}{nt}\PY{p}{)}
\PY{n}{DA} \PY{o}{=} \PY{n}{xr}\PY{o}{.}\PY{n}{DataArray}\PY{p}{(}\PY{n}{da}\PY{o}{.}\PY{n}{from\PYZus{}array}\PY{p}{(}\PY{n}{cube}\PY{p}{,} \PY{n}{chunks}\PY{o}{=}\PY{p}{(}\PY{l+m+mi}{1}\PY{p}{,} \PY{n}{nx}\PY{p}{,} \PY{n}{ny}\PY{p}{)}\PY{p}{)}\PY{p}{,}
\PY{n}{dims}\PY{o}{=}\PY{p}{(}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{time}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,} \PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{lat}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,} \PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{lon}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}\PY{p}{,}
\PY{n}{coords}\PY{o}{=}\PY{p}{\PYZob{}}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{time}\PY{l+s+s1}{\PYZsq{}}\PY{p}{:} \PY{n}{time}\PY{p}{\PYZcb{}}\PY{p}{)}
\PY{n}{DS} \PY{o}{=} \PY{n}{DA}\PY{o}{.}\PY{n}{to\PYZus{}dataset}\PY{p}{(}\PY{n}{name}\PY{o}{=}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{field}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}
\PY{n+nb}{print}\PY{p}{(}\PY{n}{DS}\PY{p}{)}
\PY{n}{DS}\PY{p}{[}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{field}\PY{l+s+s1}{\PYZsq{}}\PY{p}{]}\PY{o}{.}\PY{n}{values}
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}24}]:} \PY{k}{if} \PY{k+kc}{False}\PY{p}{:}
\PY{c+c1}{\PYZsh{} I would like a time axis that has decimal time in some familiar unit like 1 day. Why? }
\PY{c+c1}{\PYZsh{} Because when I try to fit a line to the data it complains...}
\PY{c+c1}{\PYZsh{} Since our time range is 2013 to 2018 we will have values of a couple thousand days; }
\PY{c+c1}{\PYZsh{} but that is pretty reasonable particularly when our data for speed are in meters per day.}
\PY{c+c1}{\PYZsh{} The first step is to choose some epoch; I used January 1 2013 as prior to all the GOLIVE data.}
\PY{c+c1}{\PYZsh{} With this epoch in hand we convert from datetime.datetime to np.datetime64.}
\PY{c+c1}{\PYZsh{} We then reference a datetime in the vvds Dataset, subtract the epoch and convert to (24\PYZhy{}hour) units}
\PY{n}{dtEpoch} \PY{o}{=} \PY{n}{datetime}\PY{p}{(}\PY{l+m+mi}{2013}\PY{p}{,}\PY{l+m+mi}{1}\PY{p}{,}\PY{l+m+mi}{1}\PY{p}{,}\PY{l+m+mi}{0}\PY{p}{,}\PY{l+m+mi}{0}\PY{p}{,}\PY{l+m+mi}{0}\PY{p}{)}
\PY{n}{dt64Epoch} \PY{o}{=} \PY{n}{np}\PY{o}{.}\PY{n}{datetime64}\PY{p}{(}\PY{n}{datetime}\PY{p}{(}\PY{l+m+mi}{2013}\PY{p}{,}\PY{l+m+mi}{1}\PY{p}{,}\PY{l+m+mi}{1}\PY{p}{,}\PY{l+m+mi}{0}\PY{p}{,}\PY{l+m+mi}{0}\PY{p}{,}\PY{l+m+mi}{0}\PY{p}{)}\PY{p}{)}
\PY{n}{ts} \PY{o}{=} \PY{p}{(}\PY{n}{vvds}\PY{o}{.}\PY{n}{time}\PY{p}{[}\PY{l+m+mi}{0}\PY{p}{]}\PY{o}{.}\PY{n}{data} \PY{o}{\PYZhy{}} \PY{n}{dt64Epoch}\PY{p}{)} \PY{o}{/} \PY{n}{np}\PY{o}{.}\PY{n}{timedelta64}\PY{p}{(}\PY{l+m+mi}{24}\PY{p}{,} \PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{h}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}
\PY{n+nb}{print}\PY{p}{(}\PY{n}{ts}\PY{p}{,} \PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{days from Jan 1 2013 to the first timestamp of the Dataset:}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,} \PY{n}{vvds}\PY{o}{.}\PY{n}{time}\PY{p}{[}\PY{l+m+mi}{0}\PY{p}{]}\PY{o}{.}\PY{n}{data}\PY{p}{)}
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}25}]:} \PY{n}{vvds}\PY{o}{.}\PY{n}{coords} \PY{c+c1}{\PYZsh{} notice time is datetime64; some with hours added to avoid collisions}
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{outcolor}Out[{\color{outcolor}25}]:} Coordinates:
* time (time) datetime64[ns] 2014-01-10 2014-01-10T01:00:00 {\ldots} 2015-01-06
* x (x) float64 2.2e+05 2.203e+05 2.206e+05 {\ldots} 7.513e+05 7.516e+05
* y (y) float64 6.537e+06 6.538e+06 6.538e+06 {\ldots} 6.945e+06 6.945e+06
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}26}]:} \PY{n}{vvds}\PY{o}{.}\PY{n}{dims} \PY{c+c1}{\PYZsh{} 75 source results x (spatial extent)}
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{outcolor}Out[{\color{outcolor}26}]:} Frozen(SortedKeysDict(\{'time': 75, 'x': 1773, 'y': 1361\}))
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}27}]:} \PY{k+kn}{from} \PY{n+nn}{numpy} \PY{k}{import} \PY{n}{datetime64} \PY{k}{as} \PY{n}{dt64}\PY{p}{,} \PY{n}{timedelta64} \PY{k}{as} \PY{n}{td64}
\PY{k}{def} \PY{n+nf}{ETimeDDays}\PY{p}{(}\PY{n}{timeX}\PY{p}{)}\PY{p}{:} \PY{k}{return} \PY{p}{(}\PY{n}{timeX} \PY{o}{\PYZhy{}} \PY{n}{dt64}\PY{p}{(}\PY{n}{datetime}\PY{p}{(}\PY{l+m+mi}{2013}\PY{p}{,}\PY{l+m+mi}{1}\PY{p}{,}\PY{l+m+mi}{1}\PY{p}{)}\PY{p}{)}\PY{p}{)} \PY{o}{/} \PY{n}{td64}\PY{p}{(}\PY{l+m+mi}{24}\PY{p}{,} \PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{h}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}
\PY{n}{vvds}\PY{p}{[}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{time}\PY{l+s+s1}{\PYZsq{}}\PY{p}{]} \PY{o}{=} \PY{p}{(}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{time}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,} \PY{p}{[}\PY{n}{ETimeDDays}\PY{p}{(}\PY{n}{atime}\PY{o}{.}\PY{n}{data}\PY{p}{)} \PY{k}{for} \PY{n}{atime} \PY{o+ow}{in} \PY{n}{vvds}\PY{o}{.}\PY{n}{time}\PY{p}{]}\PY{p}{)}
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}28}]:} \PY{n}{vvds}\PY{o}{.}\PY{n}{count}\PY{p}{(}\PY{p}{)}
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{outcolor}Out[{\color{outcolor}28}]:} <xarray.Dataset>
Dimensions: ()
Data variables:
speedfield int64 10364397
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}29}]:} \PY{n}{vvds}
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{outcolor}Out[{\color{outcolor}29}]:} <xarray.Dataset>
Dimensions: (time: 75, x: 1773, y: 1361)
Coordinates:
* time (time) float64 374.0 374.0 376.0 381.0 {\ldots} 728.0 733.0 735.0
* x (x) float64 2.2e+05 2.203e+05 2.206e+05 {\ldots} 7.513e+05 7.516e+05
* y (y) float64 6.537e+06 6.538e+06 {\ldots} 6.945e+06 6.945e+06
Data variables:
speedfield (time, y, x) float32 nan nan nan nan nan {\ldots} nan nan nan nan nan
\end{Verbatim}
\hypertarget{remarks}{%
\subsubsection{Remarks}\label{remarks}}
There are two types of \texttt{coordinates} in \texttt{xarray}:
Dimension coordinates (1D, correspond precisely by name to their
\texttt{dimension}, marked by an asterisk (*) as above; and
non-dimension coordinates. These latter contain coordinate data but are
not a dimension coordinate.
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}30}]:} \PY{c+c1}{\PYZsh{} vvds.assign\PYZus{}coords(dtime=EpochTimeDecimalDays(vvds.time))}
\PY{c+c1}{\PYZsh{} vvds.dtime=vvds.time.}
\PY{c+c1}{\PYZsh{} print(dir(vvds.time[0].data))}
\PY{c+c1}{\PYZsh{} print(vvds.time[0].data)}
\PY{c+c1}{\PYZsh{} print(type(vvds.time[0].data))}
\PY{c+c1}{\PYZsh{} dir(vvds.time)}
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}31}]:} \PY{o}{\PYZpc{}\PYZpc{}}\PY{k}{time}
dsRob = vvds.chunk(chunks=\PYZob{}\PYZsq{}time\PYZsq{}:\PYZhy{}1, \PYZsq{}y\PYZsq{}:400, \PYZsq{}x\PYZsq{}:400\PYZcb{})
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
CPU times: user 959 ms, sys: 0 ns, total: 959 ms
Wall time: 958 ms
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}32}]:} \PY{n+nb}{print}\PY{p}{(}\PY{n}{dsRob}\PY{p}{,} \PY{l+s+s1}{\PYZsq{}}\PY{l+s+se}{\PYZbs{}n}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}
\PY{n}{timeseries} \PY{o}{=} \PY{n}{dsRob}\PY{o}{.}\PY{n}{speedfield}
\PY{n}{apoint} \PY{o}{=} \PY{n}{timeseries}\PY{o}{.}\PY{n}{sel}\PY{p}{(}\PY{n}{y}\PY{o}{=}\PY{l+m+mf}{6710000.}\PY{p}{,}\PY{n}{x}\PY{o}{=}\PY{l+m+mf}{500000.}\PY{p}{,}\PY{n}{method}\PY{o}{=}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{nearest}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}
\PY{n+nb}{print}\PY{p}{(}\PY{n}{apoint}\PY{o}{.}\PY{n}{data}\PY{p}{)}
\PY{n+nb}{print}\PY{p}{(}\PY{n}{vvds}\PY{o}{.}\PY{n}{speedfield}\PY{o}{.}\PY{n}{sel}\PY{p}{(}\PY{n}{y}\PY{o}{=}\PY{l+m+mf}{6710000.}\PY{p}{,}\PY{n}{x}\PY{o}{=}\PY{l+m+mf}{500000.}\PY{p}{,}\PY{n}{method}\PY{o}{=}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{nearest}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}\PY{o}{.}\PY{n}{data}\PY{p}{)}
\PY{c+c1}{\PYZsh{} timeseries.plot()}
\PY{c+c1}{\PYZsh{} print(dsRob.speedfield.sel(y=6710000.,x=500000.,method=\PYZsq{}nearest\PYZsq{}).data)}
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
<xarray.Dataset>
Dimensions: (time: 75, x: 1773, y: 1361)
Coordinates:
* time (time) float64 374.0 374.0 376.0 381.0 {\ldots} 728.0 733.0 735.0
* x (x) float64 2.2e+05 2.203e+05 2.206e+05 {\ldots} 7.513e+05 7.516e+05
* y (y) float64 6.537e+06 6.538e+06 {\ldots} 6.945e+06 6.945e+06
Data variables:
speedfield (time, y, x) float32 dask.array<shape=(75, 1361, 1773), chunksize=(75, 400, 400)>
dask.array<getitem, shape=(75,), dtype=float32, chunksize=(75,)>
[ nan nan nan nan nan nan
nan nan nan nan nan nan
nan nan nan nan nan nan
nan nan nan nan nan nan
0.3805175 nan 0.45239696 nan 0.15132774 nan
nan 0.19357407 nan 0.05995712 nan nan
nan nan nan 3.3384483 nan nan
nan nan nan nan 0.29603022 nan
nan 0.25648597 nan nan nan nan
nan nan nan nan nan nan
0.3377338 nan nan nan nan nan
nan nan nan nan nan nan
nan nan nan]
\end{Verbatim}
\hypertarget{solve-ls-fit-here}{%
\section{Solve LS fit here}\label{solve-ls-fit-here}}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}60}]:} \PY{n}{y}\PY{p}{,} \PY{n}{x} \PY{o}{=} \PY{l+m+mf}{6728488.}\PY{p}{,}\PY{l+m+mf}{517888.}
\PY{n}{lsvel} \PY{o}{=} \PY{n}{vvds}\PY{o}{.}\PY{n}{speedfield}\PY{o}{.}\PY{n}{sel}\PY{p}{(}\PY{n}{y}\PY{o}{=}\PY{n}{y}\PY{p}{,}\PY{n}{x}\PY{o}{=}\PY{n}{x}\PY{p}{,}\PY{n}{method}\PY{o}{=}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{nearest}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}\PY{o}{.}\PY{n}{data}
\PY{n}{lstime}\PY{p}{,} \PY{n}{lsx}\PY{p}{,} \PY{n}{lsy} \PY{o}{=} \PY{n}{vvds}\PY{p}{[}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{time}\PY{l+s+s1}{\PYZsq{}}\PY{p}{]}\PY{p}{,} \PY{p}{[}\PY{p}{]}\PY{p}{,} \PY{p}{[}\PY{p}{]}
\PY{k}{for} \PY{n}{d} \PY{o+ow}{in} \PY{n+nb}{range}\PY{p}{(}\PY{n+nb}{len}\PY{p}{(}\PY{n}{lsvel}\PY{p}{)}\PY{p}{)}\PY{p}{:}
\PY{k}{if} \PY{o+ow}{not} \PY{n}{np}\PY{o}{.}\PY{n}{isnan}\PY{p}{(}\PY{n}{lsvel}\PY{p}{[}\PY{n}{d}\PY{p}{]}\PY{p}{)}\PY{p}{:}
\PY{n}{lsx}\PY{o}{.}\PY{n}{append}\PY{p}{(}\PY{n+nb}{float}\PY{p}{(}\PY{n}{lstime}\PY{p}{[}\PY{n}{d}\PY{p}{]}\PY{o}{.}\PY{n}{data}\PY{p}{)}\PY{p}{)}
\PY{n}{lsy}\PY{o}{.}\PY{n}{append}\PY{p}{(}\PY{n}{lsvel}\PY{p}{[}\PY{n}{d}\PY{p}{]}\PY{p}{)}
\PY{n}{lsymf} \PY{o}{=} \PY{n}{mf}\PY{p}{(}\PY{n}{lsy}\PY{p}{,} \PY{n}{kernel\PYZus{}size}\PY{o}{=}\PY{l+m+mi}{3}\PY{p}{)}\PY{p}{;}
\PY{n}{fig}\PY{p}{,}\PY{n}{axes} \PY{o}{=} \PY{n}{plt}\PY{o}{.}\PY{n}{subplots}\PY{p}{(}\PY{l+m+mi}{1}\PY{p}{)}\PY{p}{;} \PY{n}{fig}\PY{o}{.}\PY{n}{set\PYZus{}size\PYZus{}inches}\PY{p}{(}\PY{l+m+mi}{6}\PY{p}{,}\PY{l+m+mi}{4}\PY{p}{)}
\PY{n}{axes}\PY{o}{.}\PY{n}{set}\PY{p}{(}\PY{n}{xlabel}\PY{o}{=}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{time (days)}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,} \PY{n}{ylabel} \PY{o}{=} \PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{m / d}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,} \PY{n}{title}\PY{o}{=}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{signal (black) and median filter (red)}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}
\PY{n}{axes}\PY{o}{.}\PY{n}{plot}\PY{p}{(}\PY{n}{lsx}\PY{p}{,} \PY{n}{lsy}\PY{p}{,} \PY{n}{color}\PY{o}{=}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{black}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}\PY{p}{;} \PY{n}{axes}\PY{o}{.}\PY{n}{plot}\PY{p}{(}\PY{n}{lsx}\PY{p}{,} \PY{n}{lsymf}\PY{p}{,} \PY{n}{color}\PY{o}{=}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{red}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}
\PY{n}{fit} \PY{o}{=} \PY{n}{np}\PY{o}{.}\PY{n}{polyfit}\PY{p}{(}\PY{n}{lsx}\PY{p}{,} \PY{n}{lsymf}\PY{p}{,} \PY{l+m+mi}{1}\PY{p}{)}\PY{p}{;} \PY{n}{fitx}\PY{o}{=}\PY{p}{[}\PY{n}{lsx}\PY{p}{[}\PY{l+m+mi}{0}\PY{p}{]}\PY{p}{,}\PY{n}{lsx}\PY{p}{[}\PY{o}{\PYZhy{}}\PY{l+m+mi}{1}\PY{p}{]}\PY{p}{]}\PY{p}{;} \PY{n}{fity}\PY{o}{=}\PY{p}{[}\PY{n}{fit}\PY{p}{[}\PY{l+m+mi}{1}\PY{p}{]} \PY{o}{+} \PY{n}{fit}\PY{p}{[}\PY{l+m+mi}{0}\PY{p}{]}\PY{o}{*}\PY{n}{fitx}\PY{p}{[}\PY{l+m+mi}{0}\PY{p}{]}\PY{p}{,}\PY{n}{fit}\PY{p}{[}\PY{l+m+mi}{1}\PY{p}{]} \PY{o}{+} \PY{n}{fit}\PY{p}{[}\PY{l+m+mi}{0}\PY{p}{]}\PY{o}{*}\PY{n}{fitx}\PY{p}{[}\PY{l+m+mi}{1}\PY{p}{]}\PY{p}{]}
\PY{n}{axes}\PY{o}{.}\PY{n}{plot}\PY{p}{(}\PY{n}{fitx}\PY{p}{,} \PY{n}{fity}\PY{p}{,} \PY{n}{color}\PY{o}{=}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{blue}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}
\PY{n}{fit0} \PY{o}{=} \PY{n}{np}\PY{o}{.}\PY{n}{polyfit}\PY{p}{(}\PY{n}{lsx}\PY{p}{,} \PY{n}{lsy}\PY{p}{,} \PY{l+m+mi}{1}\PY{p}{)}\PY{p}{;} \PY{n}{fitx0}\PY{o}{=}\PY{p}{[}\PY{n}{lsx}\PY{p}{[}\PY{l+m+mi}{0}\PY{p}{]}\PY{p}{,}\PY{n}{lsx}\PY{p}{[}\PY{o}{\PYZhy{}}\PY{l+m+mi}{1}\PY{p}{]}\PY{p}{]}\PY{p}{;} \PY{n}{fity0}\PY{o}{=}\PY{p}{[}\PY{n}{fit0}\PY{p}{[}\PY{l+m+mi}{1}\PY{p}{]} \PY{o}{+} \PY{n}{fit0}\PY{p}{[}\PY{l+m+mi}{0}\PY{p}{]}\PY{o}{*}\PY{n}{fitx0}\PY{p}{[}\PY{l+m+mi}{0}\PY{p}{]}\PY{p}{,}\PY{n}{fit0}\PY{p}{[}\PY{l+m+mi}{1}\PY{p}{]} \PY{o}{+} \PY{n}{fit0}\PY{p}{[}\PY{l+m+mi}{0}\PY{p}{]}\PY{o}{*}\PY{n}{fitx0}\PY{p}{[}\PY{l+m+mi}{1}\PY{p}{]}\PY{p}{]}
\PY{n}{axes}\PY{o}{.}\PY{n}{plot}\PY{p}{(}\PY{n}{fitx0}\PY{p}{,} \PY{n}{fity0}\PY{p}{,} \PY{n}{color}\PY{o}{=}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{grey}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{outcolor}Out[{\color{outcolor}60}]:} [<matplotlib.lines.Line2D at 0x7f133fc755c0>]
\end{Verbatim}
\begin{center}
\adjustimage{max size={0.9\linewidth}{0.9\paperheight}}{output_36_1.png}
\end{center}
{ \hspace*{\fill} \\}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}36}]:} \PY{o}{\PYZpc{}\PYZpc{}}\PY{k}{time}
dsRob1 = get\PYZus{}slope(dsRob.time, dsRob, \PYZsq{}time\PYZsq{}).compute()
\PYZsh{} def get\PYZus{}slope(x, y, dim=\PYZsq{}time\PYZsq{}):
\PYZsh{} x = Pixel value, y = a vector containing the date, dim == dimension
\PYZsh{} DS1 = xr.apply\PYZus{}ufunc(
\PYZsh{} ls\PYZus{}slope, x , y,
\PYZsh{} input\PYZus{}core\PYZus{}dims=[[dim], [dim]],
\PYZsh{} vectorize=True,
\PYZsh{} dask=\PYZsq{}parallelized\PYZsq{},
\PYZsh{} output\PYZus{}dtypes=[float],
\PYZsh{} )
\PYZsh{} return DS1.rename(\PYZob{}\PYZsq{}speedfield\PYZsq{}:\PYZsq{}p0\PYZsq{}\PYZcb{})
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
CPU times: user 7min 27s, sys: 1min 17s, total: 8min 45s
Wall time: 6min 28s
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}37}]:} \PY{n}{dsRob1}
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{outcolor}Out[{\color{outcolor}37}]:} <xarray.Dataset>
Dimensions: (x: 1773, y: 1361)
Coordinates:
* x (x) float64 2.2e+05 2.203e+05 2.206e+05 {\ldots} 7.513e+05 7.516e+05
* y (y) float64 6.537e+06 6.538e+06 6.538e+06 {\ldots} 6.945e+06 6.945e+06
Data variables:
p0 (y, x) float64 nan nan nan nan nan nan {\ldots} nan nan nan nan nan nan
\end{Verbatim}